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|
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{ |
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"cells": [ |
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{ |
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"cell_type": "markdown", |
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"id": "46d90d45-2d19-49c7-b853-6809dc417ea7", |
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"metadata": {}, |
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"source": [ |
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"# Extra Project - Trading Code Generator\n", |
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"\n", |
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"This is an example extra project to show fine-tuning in action, and applied to code generation.\n", |
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"\n", |
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"## Project Brief\n", |
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"\n", |
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"Build a prototype LLM that can generate example code to suggest trading decisions to buy or sell stocks!\n", |
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"\n", |
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"I generated test data using frontier models, in the other files in this directory. Use this to train an open source code model.\n", |
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"\n", |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#f71;\">This project is provided as an extra resource</h2>\n", |
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" <span style=\"color:#f71;\">It will make most sense after completing Week 7, and might trigger some ideas for your own projects.<br/><br/>\n", |
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" This is provided without a detailed walk-through; the output from the colab has been saved (see last cell) so you can review the results if you have any problems running yourself.\n", |
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" </span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>\n", |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#900;\">Do not use for actual trading decisions!!</h2>\n", |
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" <span style=\"color:#900;\">It hopefully goes without saying: this project will generate toy trading code that is over-simplified and untrusted.<br/><br/>Please do not make actual trading decisions based on this!</span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "7e2c4bbb-5e8b-4d84-9997-ecb2c349cf54", |
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"metadata": {}, |
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"source": [ |
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"## First step - generate training data from examples\n", |
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"\n", |
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"There are 3 sample python files generated (via multiple queries) by GPT-4o, Claude 3 Opus and Gemini 1.5 Pro. \n", |
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"\n", |
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"This notebook creates training data from these files, then converts to the HuggingFace format and uploads to the hub.\n", |
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"\n", |
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"Afterwards, we will move to Google Colab to fine-tune." |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "16cf3aa2-f407-4b95-8b9e-c3c586f67835", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import os\n", |
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"import glob\n", |
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"import matplotlib.pyplot as plt\n", |
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"import random\n", |
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"from datasets import Dataset\n", |
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"from dotenv import load_dotenv\n", |
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"from huggingface_hub import login\n", |
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"import transformers\n", |
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"from transformers import AutoTokenizer" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "375302b6-b6a7-46ea-a74c-c2400dbd8bbe", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Load environment variables in a file called .env\n", |
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"from datasets import load_dataset, Dataset\n", |
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"load_dotenv()\n", |
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"hf_token = os.getenv('HF_TOKEN')\n", |
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"if hf_token and hf_token.startswith(\"hf_\") and len(hf_token)>5:\n", |
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" print(\"HuggingFace Token looks good so far\")\n", |
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"else:\n", |
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" print(\"Potential problem with HuggingFace token - please check your .env file, and see the Troubleshooting notebook for more\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "8a0c9fff-9eff-42fd-971b-403c99d9b726", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Constants\n", |
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"\n", |
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"DATASET_NAME = \"trade_code_data\"\n", |
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"BASE_MODEL = \"Qwen/CodeQwen1.5-7B\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "586b07ba-5396-4c34-a696-01c8bc3597a0", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# A utility method to convert the text contents of a file into a list of methods\n", |
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"\n", |
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"def extract_method_bodies(text):\n", |
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" chunks = text.split('def trade')[1:]\n", |
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" results = []\n", |
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" for chunk in chunks:\n", |
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" lines = chunk.split('\\n')[1:]\n", |
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" body = '\\n'.join(line for line in lines if line!='\\n')\n", |
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" results.append(body)\n", |
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" return results " |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "953422d0-2e75-4d01-862e-6383df54d9e5", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Read all .py files and convert into training data\n", |
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"\n", |
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"bodies = []\n", |
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"for filename in glob.glob(\"*.py\"):\n", |
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" with open(filename, 'r', encoding='utf-8') as file:\n", |
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" content = file.read()\n", |
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" extracted = extract_method_bodies(content)\n", |
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" bodies += extracted\n", |
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"\n", |
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"print(f\"Extracted {len(bodies)} trade method bodies\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "836480e9-ba23-4aa3-a7e2-2666884e9a06", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's look at one\n", |
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"\n", |
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"print(random.choice(bodies))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "47b10e7e-a562-4968-af3f-254a9b424ac8", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# To visualize the lines of code in each \n", |
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"\n", |
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"%matplotlib inline\n", |
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"fig, ax = plt.subplots(1, 1)\n", |
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"lengths = [len(body.split('\\n')) for body in bodies]\n", |
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"ax.set_xlabel('Lines of code')\n", |
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"ax.set_ylabel('Count of training samples');\n", |
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"_ = ax.hist(lengths, rwidth=0.7, color=\"green\", bins=range(0, max(lengths)))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "03b37f62-679e-4c3d-9e5b-5878a82696e6", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Add the prompt to the start of every training example\n", |
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"\n", |
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"prompt = \"\"\"\n", |
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"# tickers is a list of stock tickers\n", |
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"import tickers\n", |
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"\n", |
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"# prices is a dict; the key is a ticker and the value is a list of historic prices, today first\n", |
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"import prices\n", |
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"\n", |
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"# Trade represents a decision to buy or sell a quantity of a ticker\n", |
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"import Trade\n", |
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"\n", |
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"import random\n", |
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"import numpy as np\n", |
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"\n", |
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"def trade():\n", |
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"\"\"\"\n", |
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"\n", |
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"data = [prompt + body for body in bodies]\n", |
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"print(random.choice(data))" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "28fdb82f-3864-4023-8263-547d17571a5c", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Distribution of tokens in our dataset\n", |
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"\n", |
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"tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n", |
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"tokenized_data = [tokenizer.encode(each) for each in data]\n", |
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"token_counts = [len(tokens) for tokens in tokenized_data]\n", |
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"\n", |
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"%matplotlib inline\n", |
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"fig, ax = plt.subplots(1, 1)\n", |
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"ax.set_xlabel('Number of tokens')\n", |
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"ax.set_ylabel('Count of training samples');\n", |
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"_ = ax.hist(token_counts, rwidth=0.7, color=\"purple\", bins=range(0, max(token_counts), 20))" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "b4eb73b0-88ef-4aeb-8e5b-fe7050109ba0", |
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"metadata": {}, |
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"source": [ |
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"# Enforcing a maximum token length\n", |
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"\n", |
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"We need to specify a maximum number of tokens when we fine-tune.\n", |
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"\n", |
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"Let's pick a cut-off, and only keep training data points that fit within this number of tokens," |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "ffb0d55c-5602-4518-b811-fa385c0959a7", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"CUTOFF = 320\n", |
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"truncated = len([tokens for tokens in tokenized_data if len(tokens) > CUTOFF])\n", |
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"percentage = truncated/len(tokenized_data)*100\n", |
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"print(f\"With cutoff at {CUTOFF}, we truncate {truncated} datapoints which is {percentage:.1f}% of the dataset\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "7064ef0a-7b07-4f24-a580-cbef2c5e1f2f", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Let's only keep datapoints that wouldn't get truncated\n", |
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"\n", |
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"filtered_data = [datapoint for datapoint in data if len(tokenizer.encode(datapoint))<=CUTOFF]\n", |
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"print(f\"After e now have {len(filtered_data)} datapoints\")" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "fb2bb067-2bd3-498b-9fc8-5e8186afbe27", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Mix up the data\n", |
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"\n", |
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"random.seed(42)\n", |
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"random.shuffle(filtered_data)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "26713fb9-765f-4524-b9db-447e97686d1a", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# I don't make a Training / Test split - if we had more training data, we would!\n", |
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"\n", |
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"dataset = Dataset.from_dict({'text':filtered_data})" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "bfabba27-ef47-46a8-a26b-4d650ae3b193", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"login(hf_token)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "55b595cd-2df7-4be4-aec1-0667b17d36f1", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"# Push your dataset to your hub\n", |
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"# I've also pushed the data to my account and made it public, which you can use from the colab below\n", |
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"\n", |
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"dataset.push_to_hub(DATASET_NAME, private=True)" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "4691a025-9800-4e97-a20f-a65f102401f1", |
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"metadata": {}, |
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"source": [ |
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"## And now to head over to a Google Colab for fine-tuning in the cloud\n", |
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"\n", |
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"Follow this link for the Colab:\n", |
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"\n", |
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"https://colab.research.google.com/drive/1wry2-4AGw-U7K0LQ_jEgduoTQqVIvo1x?usp=sharing\n", |
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"\n", |
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"I've also saved this Colab with output included, so you can see the results without needing to train if you'd prefer.\n" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "04a6c3e0-a2e6-4115-a01a-45e79dfdb730", |
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"metadata": {}, |
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"outputs": [], |
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"source": [] |
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} |
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], |
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"metadata": { |
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"kernelspec": { |
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"display_name": "Python 3 (ipykernel)", |
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"language": "python", |
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"name": "python3" |
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}, |
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"language_info": { |
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"codemirror_mode": { |
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"name": "ipython", |
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"version": 3 |
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}, |
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"file_extension": ".py", |
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"mimetype": "text/x-python", |
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"name": "python", |
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"nbconvert_exporter": "python", |
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"pygments_lexer": "ipython3", |
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"version": "3.11.10" |
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} |
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}, |
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"nbformat": 4, |
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"nbformat_minor": 5 |
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} |
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# tickers is a list of stock tickers |
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import tickers |
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# prices is a dict; the key is a ticker and the value is a list of historic prices, today first |
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import prices |
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|
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# Trade represents a decision to buy or sell a quantity of a ticker |
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import Trade |
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|
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import random |
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import numpy as np |
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|
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def trade2(): |
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# Buy if the current price is lower than the average of the last 5 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] < np.mean(prices[ticker][1:6]): |
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quantity = random.randrange(1, 100) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade3(): |
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# Sell if the current price is higher than the average of the last 10 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] > np.mean(prices[ticker][1:11]): |
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quantity = random.randrange(-100, -1) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade4(): |
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# Buy if the current price is the lowest in the last 3 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] == min(prices[ticker][:3]): |
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quantity = random.randrange(1, 100) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade5(): |
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# Sell if the current price is the highest in the last 3 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] == max(prices[ticker][:3]): |
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quantity = random.randrange(-100, -1) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade6(): |
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# Buy if the current price is higher than the previous day's price |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] > prices[ticker][1]: |
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quantity = random.randrange(1, 100) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade7(): |
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# Sell if the current price is lower than the previous day's price |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] < prices[ticker][1]: |
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quantity = random.randrange(-100, -1) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade8(): |
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# Buy if the current price is higher than the average of the last 20 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] > np.mean(prices[ticker][1:21]): |
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quantity = random.randrange(1, 100) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade9(): |
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# Sell if the current price is lower than the average of the last 20 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] < np.mean(prices[ticker][1:21]): |
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quantity = random.randrange(-100, -1) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade10(): |
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# Buy if the current price is higher than the highest price in the last 5 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] > max(prices[ticker][1:6]): |
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quantity = random.randrange(1, 100) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade11(): |
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# Sell if the current price is lower than the lowest price in the last 5 days |
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trades = [] |
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for ticker in tickers: |
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if prices[ticker][0] < min(prices[ticker][1:6]): |
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quantity = random.randrange(-100, -1) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade12(): |
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# Long/Short: Buy the best-performing stock and sell the worst-performing stock in the last 10 days |
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best_ticker = max(tickers, key=lambda x: (prices[x][0] - prices[x][9]) / prices[x][9]) |
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worst_ticker = min(tickers, key=lambda x: (prices[x][0] - prices[x][9]) / prices[x][9]) |
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return [Trade(best_ticker, 100), Trade(worst_ticker, -100)] |
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|
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def trade13(): |
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# Buy if the 5-day moving average crosses above the 20-day moving average |
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trades = [] |
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for ticker in tickers: |
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if np.mean(prices[ticker][:5]) > np.mean(prices[ticker][:20]) and np.mean(prices[ticker][1:6]) <= np.mean(prices[ticker][1:21]): |
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quantity = random.randrange(1, 100) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade14(): |
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# Sell if the 5-day moving average crosses below the 20-day moving average |
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trades = [] |
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for ticker in tickers: |
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if np.mean(prices[ticker][:5]) < np.mean(prices[ticker][:20]) and np.mean(prices[ticker][1:6]) >= np.mean(prices[ticker][1:21]): |
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quantity = random.randrange(-100, -1) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade15(): |
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# Buy if the current volume is higher than the average volume of the last 10 days |
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trades = [] |
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for ticker in tickers: |
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if volumes[ticker][0] > np.mean(volumes[ticker][1:11]): |
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quantity = random.randrange(1, 100) |
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trades.append(Trade(ticker, quantity)) |
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return trades |
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|
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def trade16(): |
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# Sell if the current volume is lower than the average volume of the last 10 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if volumes[ticker][0] < np.mean(volumes[ticker][1:11]): |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade17(): |
||||
# Long/Short: Buy the stock with the highest relative strength index (RSI) and sell the stock with the lowest RSI |
||||
rsi = {} |
||||
for ticker in tickers: |
||||
gains = [max(prices[ticker][i] - prices[ticker][i+1], 0) for i in range(13)] |
||||
losses = [max(prices[ticker][i+1] - prices[ticker][i], 0) for i in range(13)] |
||||
avg_gain = sum(gains) / 14 |
||||
avg_loss = sum(losses) / 14 |
||||
rs = avg_gain / avg_loss if avg_loss > 0 else 100 |
||||
rsi[ticker] = 100 - (100 / (1 + rs)) |
||||
best_ticker = max(tickers, key=lambda x: rsi[x]) |
||||
worst_ticker = min(tickers, key=lambda x: rsi[x]) |
||||
return [Trade(best_ticker, 100), Trade(worst_ticker, -100)] |
||||
|
||||
def trade18(): |
||||
# Buy if the current price is higher than the 50-day moving average and the 50-day moving average is higher than the 200-day moving average |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] > np.mean(prices[ticker][:50]) > np.mean(prices[ticker][:200]): |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade19(): |
||||
# Sell if the current price is lower than the 50-day moving average and the 50-day moving average is lower than the 200-day moving average |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] < np.mean(prices[ticker][:50]) < np.mean(prices[ticker][:200]): |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade20(): |
||||
# Long/Short: Buy the stock with the highest momentum and sell the stock with the lowest momentum |
||||
momentums = {} |
||||
for ticker in tickers: |
||||
momentums[ticker] = prices[ticker][0] - prices[ticker][19] |
||||
best_ticker = max(tickers, key=lambda x: momentums[x]) |
||||
worst_ticker = min(tickers, key=lambda x: momentums[x]) |
||||
return [Trade(best_ticker, 100), Trade(worst_ticker, -100)] |
||||
|
||||
def trade21(): |
||||
# Buy if the current price is higher than the upper Bollinger Band |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma = np.mean(prices[ticker][:20]) |
||||
std = np.std(prices[ticker][:20]) |
||||
upper_band = sma + 2 * std |
||||
if prices[ticker][0] > upper_band: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade22(): |
||||
# Sell if the current price is lower than the lower Bollinger Band |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma = np.mean(prices[ticker][:20]) |
||||
std = np.std(prices[ticker][:20]) |
||||
lower_band = sma - 2 * std |
||||
if prices[ticker][0] < lower_band: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade23(): |
||||
# Buy if the current volatility is higher than the average volatility of the last 10 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
volatility = np.std(prices[ticker][:10]) |
||||
avg_volatility = np.mean([np.std(prices[ticker][i:i+10]) for i in range(10)]) |
||||
if volatility > avg_volatility: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade24(): |
||||
# Sell if the current volatility is lower than the average volatility of the last 10 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
volatility = np.std(prices[ticker][:10]) |
||||
avg_volatility = np.mean([np.std(prices[ticker][i:i+10]) for i in range(10)]) |
||||
if volatility < avg_volatility: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade25(): |
||||
# Long/Short: Buy the stock with the lowest volatility and sell the stock with the highest volatility |
||||
volatilities = {} |
||||
for ticker in tickers: |
||||
volatilities[ticker] = np.std(prices[ticker][:10]) |
||||
best_ticker = min(tickers, key=lambda x: volatilities[x]) |
||||
worst_ticker = max(tickers, key=lambda x: volatilities[x]) |
||||
return [Trade(best_ticker, 100), Trade(worst_ticker, -100)] |
||||
|
||||
def trade26(): |
||||
# Buy if the current price is higher than the 20-day exponential moving average (EMA) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
ema = prices[ticker][0] |
||||
multiplier = 2 / (20 + 1) |
||||
for i in range(1, 20): |
||||
ema = (prices[ticker][i] - ema) * multiplier + ema |
||||
if prices[ticker][0] > ema: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade27(): |
||||
# Sell if the current price is lower than the 20-day exponential moving average (EMA) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
ema = prices[ticker][0] |
||||
multiplier = 2 / (20 + 1) |
||||
for i in range(1, 20): |
||||
ema = (prices[ticker][i] - ema) * multiplier + ema |
||||
if prices[ticker][0] < ema: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade28(): |
||||
# Buy if the current price is higher than the upper Keltner Channel |
||||
trades = [] |
||||
for ticker in tickers: |
||||
ema = prices[ticker][0] |
||||
multiplier = 2 / (20 + 1) |
||||
for i in range(1, 20): |
||||
ema = (prices[ticker][i] - ema) * multiplier + ema |
||||
atr = np.mean([np.max(prices[ticker][i:i+10]) - np.min(prices[ticker][i:i+10]) for i in range(10)]) |
||||
upper_channel = ema + 2 * atr |
||||
if prices[ticker][0] > upper_channel: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade29(): |
||||
# Sell if the current price is lower than the lower Keltner Channel |
||||
trades = [] |
||||
for ticker in tickers: |
||||
ema = prices[ticker][0] |
||||
multiplier = 2 / (20 + 1) |
||||
for i in range(1, 20): |
||||
ema = (prices[ticker][i] - ema) * multiplier + ema |
||||
atr = np.mean([np.max(prices[ticker][i:i+10]) - np.min(prices[ticker][i:i+10]) for i in range(10)]) |
||||
lower_channel = ema - 2 * atr |
||||
if prices[ticker][0] < lower_channel: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade30(): |
||||
# Long/Short: Buy the stock with the highest Sharpe ratio and sell the stock with the lowest Sharpe ratio |
||||
sharpe_ratios = {} |
||||
for ticker in tickers: |
||||
returns = [prices[ticker][i] / prices[ticker][i+1] - 1 for i in range(19)] |
||||
sharpe_ratios[ticker] = np.mean(returns) / np.std(returns) |
||||
best_ticker = max(tickers, key=lambda x: sharpe_ratios[x]) |
||||
worst_ticker = min(tickers, key=lambda x: sharpe_ratios[x]) |
||||
return [Trade(best_ticker, 100), Trade(worst_ticker, -100)] |
||||
|
||||
def trade31(): |
||||
# Buy if the current price is higher than the Ichimoku Cloud conversion line |
||||
trades = [] |
||||
for ticker in tickers: |
||||
conversion_line = (np.max(prices[ticker][:9]) + np.min(prices[ticker][:9])) / 2 |
||||
if prices[ticker][0] > conversion_line: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade32(): |
||||
# Buy if the current price is higher than the price 5 days ago |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] > prices[ticker][4]: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade33(): |
||||
# Sell if the current price is lower than the price 5 days ago |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] < prices[ticker][4]: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade34(): |
||||
# Buy if the current price is the highest in the last 15 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] == max(prices[ticker][:15]): |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade35(): |
||||
# Sell if the current price is the lowest in the last 15 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] == min(prices[ticker][:15]): |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade36(): |
||||
# Buy if the current price is higher than the 10-day simple moving average (SMA) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma = np.mean(prices[ticker][:10]) |
||||
if prices[ticker][0] > sma: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade37(): |
||||
# Sell if the current price is lower than the 10-day simple moving average (SMA) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma = np.mean(prices[ticker][:10]) |
||||
if prices[ticker][0] < sma: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade38(): |
||||
# Buy if the current price is higher than the highest price in the last 20 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] > max(prices[ticker][:20]): |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade39(): |
||||
# Sell if the current price is lower than the lowest price in the last 20 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] < min(prices[ticker][:20]): |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade40(): |
||||
# Buy if the current price is higher than the 50-day SMA |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma = np.mean(prices[ticker][:50]) |
||||
if prices[ticker][0] > sma: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade41(): |
||||
# Sell if the current price is lower than the 50-day SMA |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma = np.mean(prices[ticker][:50]) |
||||
if prices[ticker][0] < sma: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade42(): |
||||
# Buy if the current price is higher than the previous 2 days (a simple uptrend) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] > prices[ticker][1] > prices[ticker][2]: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade43(): |
||||
# Sell if the current price is lower than the previous 2 days (a simple downtrend) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] < prices[ticker][1] < prices[ticker][2]: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade44(): |
||||
# Buy if the current price is higher than the previous day's high (a breakout) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] > max(prices[ticker][1:2]): |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade45(): |
||||
# Sell if the current price is lower than the previous day's low (a breakdown) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] < min(prices[ticker][1:2]): |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade46(): |
||||
# Buy if the current price is above the previous day's high and the previous day was a down day (a potential reversal) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] > max(prices[ticker][1:2]) and prices[ticker][1] < prices[ticker][2]: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade47(): |
||||
# Sell if the current price is below the previous day's low and the previous day was an up day (a potential reversal) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
if prices[ticker][0] < min(prices[ticker][1:2]) and prices[ticker][1] > prices[ticker][2]: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade48(): |
||||
# Buy if the current price is above the 5-day SMA and the 5-day SMA is above the 10-day SMA (a bullish crossover) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma5 = np.mean(prices[ticker][:5]) |
||||
sma10 = np.mean(prices[ticker][:10]) |
||||
if prices[ticker][0] > sma5 > sma10: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade49(): |
||||
# Sell if the current price is below the 5-day SMA and the 5-day SMA is below the 10-day SMA (a bearish crossover) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma5 = np.mean(prices[ticker][:5]) |
||||
sma10 = np.mean(prices[ticker][:10]) |
||||
if prices[ticker][0] < sma5 < sma10: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade50(): |
||||
# Buy if the current price is above the 50-day SMA and the previous price was below the 50-day SMA (a bullish breakthrough) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma50 = np.mean(prices[ticker][:50]) |
||||
if prices[ticker][0] > sma50 and prices[ticker][1] < sma50: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade51(): |
||||
# Sell if the current price is below the 50-day SMA and the previous price was above the 50-day SMA (a bearish breakthrough) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
sma50 = np.mean(prices[ticker][:50]) |
||||
if prices[ticker][0] < sma50 and prices[ticker][1] > sma50: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade52(): |
||||
# Buy if the current price is more than 2 standard deviations below the 20-day mean (a potential oversold condition) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean20 = np.mean(prices[ticker][:20]) |
||||
std20 = np.std(prices[ticker][:20]) |
||||
if prices[ticker][0] < mean20 - 2 * std20: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade53(): |
||||
# Sell if the current price is more than 2 standard deviations above the 20-day mean (a potential overbought condition) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean20 = np.mean(prices[ticker][:20]) |
||||
std20 = np.std(prices[ticker][:20]) |
||||
if prices[ticker][0] > mean20 + 2 * std20: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade54(): |
||||
# Buy if the current price is below the 50-day mean and the 50-day mean is increasing (a potential uptrend) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean50 = np.mean(prices[ticker][:50]) |
||||
prev_mean50 = np.mean(prices[ticker][1:51]) |
||||
if prices[ticker][0] < mean50 and mean50 > prev_mean50: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade55(): |
||||
# Sell if the current price is above the 50-day mean and the 50-day mean is decreasing (a potential downtrend) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean50 = np.mean(prices[ticker][:50]) |
||||
prev_mean50 = np.mean(prices[ticker][1:51]) |
||||
if prices[ticker][0] > mean50 and mean50 < prev_mean50: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade56(): |
||||
# Buy if the 5-day mean is above the 50-day mean and the 5-day mean was previously below the 50-day mean (a potential trend change) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean5 = np.mean(prices[ticker][:5]) |
||||
mean50 = np.mean(prices[ticker][:50]) |
||||
prev_mean5 = np.mean(prices[ticker][1:6]) |
||||
if mean5 > mean50 and prev_mean5 < mean50: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade57(): |
||||
# Sell if the 5-day mean is below the 50-day mean and the 5-day mean was previously above the 50-day mean (a potential trend change) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean5 = np.mean(prices[ticker][:5]) |
||||
mean50 = np.mean(prices[ticker][:50]) |
||||
prev_mean5 = np.mean(prices[ticker][1:6]) |
||||
if mean5 < mean50 and prev_mean5 > mean50: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade58(): |
||||
# Buy the ticker that has had the largest percent decrease over the last 10 days (a potential mean reversion play) |
||||
percent_changes = {} |
||||
for ticker in tickers: |
||||
percent_changes[ticker] = (prices[ticker][0] - prices[ticker][9]) / prices[ticker][9] * 100 |
||||
worst_ticker = min(tickers, key=lambda x: percent_changes[x]) |
||||
return [Trade(worst_ticker, 100)] |
||||
|
||||
def trade59(): |
||||
# Sell the ticker that has had the largest percent increase over the last 10 days (a potential mean reversion play) |
||||
percent_changes = {} |
||||
for ticker in tickers: |
||||
percent_changes[ticker] = (prices[ticker][0] - prices[ticker][9]) / prices[ticker][9] * 100 |
||||
best_ticker = max(tickers, key=lambda x: percent_changes[x]) |
||||
return [Trade(best_ticker, -100)] |
||||
|
||||
def trade60(): |
||||
# Buy if the current price is above the 200-day mean and the 200-day mean is increasing (a potential long-term uptrend) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean200 = np.mean(prices[ticker][:200]) |
||||
prev_mean200 = np.mean(prices[ticker][1:201]) |
||||
if prices[ticker][0] > mean200 and mean200 > prev_mean200: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade61(): |
||||
# Sell if the current price is below the 200-day mean and the 200-day mean is decreasing (a potential long-term downtrend) |
||||
trades = [] |
||||
for ticker in tickers: |
||||
mean200 = np.mean(prices[ticker][:200]) |
||||
prev_mean200 = np.mean(prices[ticker][1:201]) |
||||
if prices[ticker][0] < mean200 and mean200 < prev_mean200: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade62(): |
||||
# Buy if the stock's return is greater than the market's return over the last 5 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
stock_return = (prices[ticker][0] - prices[ticker][4]) / prices[ticker][4] |
||||
market_return = (sum(prices[t][0] for t in tickers) - sum(prices[t][4] for t in tickers)) / sum(prices[t][4] for t in tickers) |
||||
if stock_return > market_return: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade63(): |
||||
# Sell if the stock's return is less than the market's return over the last 5 days |
||||
trades = [] |
||||
for ticker in tickers: |
||||
stock_return = (prices[ticker][0] - prices[ticker][4]) / prices[ticker][4] |
||||
market_return = (sum(prices[t][0] for t in tickers) - sum(prices[t][4] for t in tickers)) / sum(prices[t][4] for t in tickers) |
||||
if stock_return < market_return: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade64(): |
||||
# Buy the stock with the highest relative strength compared to the market over the last 10 days |
||||
relative_strengths = {} |
||||
for ticker in tickers: |
||||
stock_return = prices[ticker][0] / prices[ticker][9] |
||||
market_return = sum(prices[t][0] for t in tickers) / sum(prices[t][9] for t in tickers) |
||||
relative_strengths[ticker] = stock_return / market_return |
||||
best_ticker = max(tickers, key=lambda x: relative_strengths[x]) |
||||
return [Trade(best_ticker, 100)] |
||||
|
||||
def trade65(): |
||||
# Sell the stock with the lowest relative strength compared to the market over the last 10 days |
||||
relative_strengths = {} |
||||
for ticker in tickers: |
||||
stock_return = prices[ticker][0] / prices[ticker][9] |
||||
market_return = sum(prices[t][0] for t in tickers) / sum(prices[t][9] for t in tickers) |
||||
relative_strengths[ticker] = stock_return / market_return |
||||
worst_ticker = min(tickers, key=lambda x: relative_strengths[x]) |
||||
return [Trade(worst_ticker, -100)] |
||||
|
||||
def trade66(): |
||||
# Buy stocks that have a higher Sharpe ratio than the market over the last 20 days |
||||
trades = [] |
||||
market_returns = [(sum(prices[t][i] for t in tickers) / sum(prices[t][i+1] for t in tickers)) - 1 for i in range(19)] |
||||
market_sharpe = np.mean(market_returns) / np.std(market_returns) |
||||
for ticker in tickers: |
||||
stock_returns = [(prices[ticker][i] / prices[ticker][i+1]) - 1 for i in range(19)] |
||||
stock_sharpe = np.mean(stock_returns) / np.std(stock_returns) |
||||
if stock_sharpe > market_sharpe: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade67(): |
||||
# Sell stocks that have a lower Sharpe ratio than the market over the last 20 days |
||||
trades = [] |
||||
market_returns = [(sum(prices[t][i] for t in tickers) / sum(prices[t][i+1] for t in tickers)) - 1 for i in range(19)] |
||||
market_sharpe = np.mean(market_returns) / np.std(market_returns) |
||||
for ticker in tickers: |
||||
stock_returns = [(prices[ticker][i] / prices[ticker][i+1]) - 1 for i in range(19)] |
||||
stock_sharpe = np.mean(stock_returns) / np.std(stock_returns) |
||||
if stock_sharpe < market_sharpe: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade68(): |
||||
# Buy stocks that have a higher beta than 1 (they move more than the market) |
||||
trades = [] |
||||
market_returns = [(sum(prices[t][i] for t in tickers) / sum(prices[t][i+1] for t in tickers)) - 1 for i in range(49)] |
||||
for ticker in tickers: |
||||
stock_returns = [(prices[ticker][i] / prices[ticker][i+1]) - 1 for i in range(49)] |
||||
beta = np.cov(stock_returns, market_returns)[0, 1] / np.var(market_returns) |
||||
if beta > 1: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade69(): |
||||
# Sell stocks that have a lower beta than 1 (they move less than the market) |
||||
trades = [] |
||||
market_returns = [(sum(prices[t][i] for t in tickers) / sum(prices[t][i+1] for t in tickers)) - 1 for i in range(49)] |
||||
for ticker in tickers: |
||||
stock_returns = [(prices[ticker][i] / prices[ticker][i+1]) - 1 for i in range(49)] |
||||
beta = np.cov(stock_returns, market_returns)[0, 1] / np.var(market_returns) |
||||
if beta < 1: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade70(): |
||||
# Buy stocks that have a higher percentage of up days than the market over the last 50 days |
||||
trades = [] |
||||
market_up_days = sum(sum(prices[t][i] for t in tickers) > sum(prices[t][i+1] for t in tickers) for i in range(49)) |
||||
for ticker in tickers: |
||||
stock_up_days = sum(prices[ticker][i] > prices[ticker][i+1] for i in range(49)) |
||||
if stock_up_days > market_up_days: |
||||
quantity = random.randrange(1, 100) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
||||
|
||||
def trade71(): |
||||
# Sell stocks that have a lower percentage of up days than the market over the last 50 days |
||||
trades = [] |
||||
market_up_days = sum(sum(prices[t][i] for t in tickers) > sum(prices[t][i+1] for t in tickers) for i in range(49)) |
||||
for ticker in tickers: |
||||
stock_up_days = sum(prices[ticker][i] > prices[ticker][i+1] for i in range(49)) |
||||
if stock_up_days < market_up_days: |
||||
quantity = random.randrange(-100, -1) |
||||
trades.append(Trade(ticker, quantity)) |
||||
return trades |
@ -0,0 +1,534 @@
|
||||
# tickers is a list of stock tickers |
||||
import tickers |
||||
|
||||
# prices is a dict; the key is a ticker and the value is a list of historic prices, today first |
||||
import prices |
||||
|
||||
# Trade represents a decision to buy or sell a quantity of a ticker |
||||
import Trade |
||||
|
||||
import random |
||||
import numpy as np |
||||
|
||||
def trade2(): |
||||
# Buy the stock with the highest price today |
||||
ticker = max(prices, key=lambda t: prices[t][0]) # Find ticker with highest price |
||||
return [Trade(ticker, random.randrange(1, 10))] # Buy a small quantity |
||||
|
||||
def trade3(): |
||||
# Sell the stock with the lowest price today |
||||
ticker = min(prices, key=lambda t: prices[t][0]) |
||||
return [Trade(ticker, random.randrange(-10, -1))] |
||||
|
||||
def trade4(): |
||||
# Buy the stock with the largest percent increase today |
||||
changes = {t: (prices[t][0] - prices[t][1]) / prices[t][1] for t in prices} |
||||
ticker = max(changes, key=changes.get) |
||||
return [Trade(ticker, random.randrange(1, 10))] |
||||
|
||||
def trade5(): |
||||
# Sell the stock with the largest percent decrease today |
||||
changes = {t: (prices[t][0] - prices[t][1]) / prices[t][1] for t in prices} |
||||
ticker = min(changes, key=changes.get) |
||||
return [Trade(ticker, random.randrange(-10, -1))] |
||||
|
||||
def trade6(): |
||||
# Buy the 3 stocks with the highest moving average over the last 5 days |
||||
mvgs = {t: np.mean(prices[t][:5]) for t in prices} |
||||
top_tickers = sorted(mvgs, key=mvgs.get, reverse=True)[:3] |
||||
return [Trade(t, random.randrange(1, 5)) for t in top_tickers] |
||||
|
||||
def trade7(): |
||||
# Sell the 3 stocks with the lowest moving average over the last 5 days |
||||
mvgs = {t: np.mean(prices[t][:5]) for t in prices} |
||||
bottom_tickers = sorted(mvgs, key=mvgs.get)[:3] |
||||
return [Trade(t, random.randrange(-5, -1)) for t in bottom_tickers] |
||||
|
||||
def trade8(): |
||||
# Randomly buy or sell a single stock based on a coin flip |
||||
ticker = random.choice(tickers) |
||||
action = random.choice([-1, 1]) # -1 for sell, 1 for buy |
||||
return [Trade(ticker, action * random.randrange(1, 10))] |
||||
|
||||
def trade9(): |
||||
# Diversify: Buy a small amount of 5 random stocks |
||||
chosen_tickers = random.sample(tickers, 5) |
||||
return [Trade(t, random.randrange(1, 3)) for t in chosen_tickers] |
||||
|
||||
def trade10(): |
||||
# Follow the trend: If the market is up today, buy, else sell |
||||
market_change = (prices[tickers[0]][0] - prices[tickers[0]][1]) / prices[tickers[0]][1] |
||||
action = 1 if market_change > 0 else -1 |
||||
ticker = random.choice(tickers) |
||||
return [Trade(ticker, action * random.randrange(1, 10))] |
||||
|
||||
def trade11(): |
||||
# Mean Reversion: Buy the 2 stocks that fell the most yesterday, hoping they rebound |
||||
yesterday_changes = {t: (prices[t][1] - prices[t][2]) / prices[t][2] for t in prices} |
||||
bottom_tickers = sorted(yesterday_changes, key=yesterday_changes.get)[:2] |
||||
return [Trade(t, random.randrange(1, 5)) for t in bottom_tickers] |
||||
|
||||
def trade12(): |
||||
# Momentum: Short the 2 stocks that rose the most yesterday, expecting a pullback |
||||
yesterday_changes = {t: (prices[t][1] - prices[t][2]) / prices[t][2] for t in prices} |
||||
top_tickers = sorted(yesterday_changes, key=yesterday_changes.get, reverse=True)[:2] |
||||
return [Trade(t, random.randrange(-5, -1)) for t in top_tickers] |
||||
|
||||
def trade13(): |
||||
# Pairs Trading: Long one stock, short another with a similar price history |
||||
correlations = np.corrcoef([prices[t] for t in tickers]) |
||||
i, j = np.unravel_index(np.argmax(correlations), correlations.shape) |
||||
return [Trade(tickers[i], 1), Trade(tickers[j], -1)] |
||||
|
||||
def trade14(): |
||||
# Relative Strength: Go long on the strongest stock, short the weakest |
||||
performances = {t: (prices[t][0] - prices[t][-1]) / prices[t][-1] for t in prices} |
||||
strongest = max(performances, key=performances.get) |
||||
weakest = min(performances, key=performances.get) |
||||
return [Trade(strongest, 1), Trade(weakest, -1)] |
||||
|
||||
def trade15(): |
||||
# Calendar Spread: Buy this month's option, sell next month's (same strike |
||||
# This is a simplified representation, as actual option trading is more complex |
||||
ticker = random.choice(tickers) |
||||
return [Trade(f"{ticker}_OPT_THIS_MONTH", 1), Trade(f"{ticker}_OPT_NEXT_MONTH", -1)] |
||||
|
||||
def trade16(): |
||||
# Straddle: Buy both a call and put option on the same stock (same strike |
||||
ticker = random.choice(tickers) |
||||
strike = prices[ticker][0] # Use the current price as a simple strike price |
||||
return [Trade(f"{ticker}_CALL_{strike}", 1), Trade(f"{ticker}_PUT_{strike}", 1)] |
||||
|
||||
def trade17(): |
||||
# Breakout: Buy if a stock breaks above its 52-week high |
||||
ticker = random.choice(tickers) |
||||
if prices[ticker][0] > max(prices[ticker]): |
||||
return [Trade(ticker, random.randrange(1, 10))] |
||||
else: |
||||
return [] |
||||
|
||||
def trade18(): |
||||
# Volatility: If market volatility is high, sell (expecting it to decrease |
||||
market_volatility = np.std([prices[t][0] / prices[t][1] for t in tickers]) |
||||
if market_volatility > 0.05: # You'd adjust this threshold based on your risk tolerance |
||||
ticker = random.choice(tickers) |
||||
return [Trade(ticker, random.randrange(-10, -1))] |
||||
else: |
||||
return [] |
||||
|
||||
def trade19(): |
||||
# Golden Cross: Buy if the short-term moving average crosses above the long-term |
||||
ticker = random.choice(tickers) |
||||
short_ma = np.mean(prices[ticker][:5]) |
||||
long_ma = np.mean(prices[ticker][:20]) |
||||
if short_ma > long_ma and short_ma - long_ma < 0.01: # Small margin to avoid false signals |
||||
return [Trade(ticker, random.randrange(1, 10))] |
||||
else: |
||||
return [] |
||||
|
||||
def trade20(): |
||||
# Death Cross: Sell if the short-term moving average crosses below the long-term |
||||
ticker = random.choice(tickers) |
||||
short_ma = np.mean(prices[ticker][:5]) |
||||
long_ma = np.mean(prices[ticker][:20]) |
||||
if short_ma < long_ma and long_ma - short_ma < 0.01: |
||||
return [Trade(ticker, random.randrange(-10, -1))] |
||||
else: |
||||
return [] |
||||
|
||||
def trade21(): |
||||
# Correlated Pairs Buy: Buy a pair of stocks that have historically moved together |
||||
correlations = np.corrcoef([prices[t] for t in tickers]) |
||||
i, j = np.unravel_index(np.argmax(correlations), correlations.shape) |
||||
return [Trade(tickers[i], 1), Trade(tickers[j], 1)] |
||||
|
||||
def trade22(): |
||||
# Correlated Pairs Sell: Sell a pair of stocks that have historically moved together |
||||
correlations = np.corrcoef([prices[t] for t in tickers]) |
||||
i, j = np.unravel_index(np.argmax(correlations), correlations.shape) |
||||
return [Trade(tickers[i], -1), Trade(tickers[j], -1)] |
||||
|
||||
def trade23(): |
||||
# Contrarian Pairs Buy: Buy a stock that's down while its correlated pair is up |
||||
correlations = np.corrcoef([prices[t] for t in tickers]) |
||||
i, j = np.unravel_index(np.argmax(correlations), correlations.shape) |
||||
if prices[tickers[i]][0] < prices[tickers[i]][1] and prices[tickers[j]][0] > prices[tickers[j]][1]: |
||||
return [Trade(tickers[i], 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade24(): |
||||
# Contrarian Pairs Sell: Sell a stock that's up while its correlated pair is down |
||||
correlations = np.corrcoef([prices[t] for t in tickers]) |
||||
i, j = np.unravel_index(np.argmax(correlations), correlations.shape) |
||||
if prices[tickers[i]][0] > prices[tickers[i]][1] and prices[tickers[j]][0] < prices[tickers[j]][1]: |
||||
return [Trade(tickers[i], -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade25(): |
||||
# Correlation Reversal: Buy a stock that's recently become less correlated with the market |
||||
# This is a simplified version, you'd likely use a rolling correlation window |
||||
market_prices = [prices[t] for t in tickers] |
||||
correlations_today = np.corrcoef(market_prices) |
||||
correlations_yesterday = np.corrcoef([p[1:] for p in market_prices]) |
||||
diffs = correlations_today - correlations_yesterday |
||||
i, j = np.unravel_index(np.argmin(diffs), diffs.shape) |
||||
if i != j: # Ensure we're not comparing a stock to itself |
||||
return [Trade(tickers[i], 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade26(): |
||||
# Sector Rotation: Buy the top 2 stocks from the sector that's most correlated with the market |
||||
# Assuming you have sector data (e.g., 'sector_map' dict: ticker -> sector) |
||||
sector_returns = {s: np.mean([(prices[t][0] - prices[t][1]) / prices[t][1] for t in tickers if sector_map[t] == s]) for s in set(sector_map.values())} |
||||
top_sector = max(sector_returns, key=sector_returns.get) |
||||
top_tickers_in_sector = sorted([(t, prices[t][0]) for t in tickers if sector_map[t] == top_sector], key=lambda x: x[1], reverse=True)[:2] |
||||
return [Trade(t, 1) for t, _ in top_tickers_in_sector] |
||||
|
||||
def trade27(): |
||||
# Beta-Weighted Portfolio: Allocate more to stocks with higher betas (more volatile |
||||
# You'd need historical market data to calculate betas |
||||
betas = {t: random.uniform(0.5, 2) for t in tickers} # Placeholder for actual betas |
||||
total_beta = sum(betas.values()) |
||||
allocations = {t: betas[t] / total_beta * 100 for t in tickers} |
||||
return [Trade(t, int(allocations[t])) for t in tickers] |
||||
|
||||
def trade28(): |
||||
# Diversified Portfolio: Buy a mix of stocks with low correlations to each other |
||||
correlations = np.corrcoef([prices[t] for t in tickers]) |
||||
chosen_tickers = [] |
||||
while len(chosen_tickers) < 5 and len(tickers) > 0: |
||||
t = random.choice(tickers) |
||||
if all(correlations[tickers.index(t)][tickers.index(c)] < 0.5 for c in chosen_tickers): |
||||
chosen_tickers.append(t) |
||||
tickers.remove(t) |
||||
return [Trade(t, random.randrange(1, 3)) for t in chosen_tickers] |
||||
|
||||
def trade29(): |
||||
# Cointegration: Find a pair of stocks that are cointegrated and trade their spread |
||||
# This requires more complex analysis (e.g., using the Johansen test) |
||||
# For simplicity, we'll just pick a random pair and assume cointegration |
||||
i, j = random.sample(range(len(tickers)), 2) |
||||
spread = prices[tickers[i]][0] - prices[tickers[j]][0] |
||||
if spread > 0: |
||||
return [Trade(tickers[i], -1), Trade(tickers[j], 1)] |
||||
else: |
||||
return [Trade(tickers[i], 1), Trade(tickers[j], -1)] |
||||
|
||||
def trade30(): |
||||
# Basket Trading: Buy or sell a basket of stocks based on their correlation to a benchmark |
||||
# You'd need a benchmark ticker and its historical prices |
||||
benchmark = "SPY" |
||||
correlations = np.corrcoef([prices[t] for t in tickers + [benchmark]])[:-1, -1] # Correlate each stock with the benchmark |
||||
if np.mean(correlations) > 0.5: |
||||
return [Trade(t, 1) for t in tickers] |
||||
else: |
||||
return [Trade(t, -1) for t in tickers] |
||||
|
||||
def trade31(): |
||||
# Double Bottom: Buy when a stock forms a double bottom pattern |
||||
ticker = random.choice(tickers) |
||||
if prices[ticker][0] < prices[ticker][2] < prices[ticker][4] and prices[ticker][1] > prices[ticker][3]: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade32(): |
||||
# Double Top: Sell when a stock forms a double top pattern |
||||
ticker = random.choice(tickers) |
||||
if prices[ticker][0] > prices[ticker][2] > prices[ticker][4] and prices[ticker][1] < prices[ticker][3]: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade33(): |
||||
# Head and Shoulders: Sell when a stock forms a head and shoulders pattern |
||||
ticker = random.choice(tickers) |
||||
if prices[ticker][0] < prices[ticker][2] < prices[ticker][4] and prices[ticker][1] > prices[ticker][3] > prices[ticker][5]: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade34 |
||||
# Inverse Head and Shoulders: Buy when a stock forms an inverse head and shoulders pattern |
||||
ticker = random.choice(tickers) |
||||
if prices[ticker][0] > prices[ticker][2] > prices[ticker][4] and prices[ticker][1] < prices[ticker][3] < prices[ticker][5]: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade35(): |
||||
# Ascending Triangle: Buy when a stock forms an ascending triangle pattern |
||||
ticker = random.choice(tickers) |
||||
# Simplified logic: check for higher lows and flat highs |
||||
if prices[ticker][0] > prices[ticker][2] > prices[ticker][4] and prices[ticker][1] == prices[ticker][3] == prices[ticker][5]: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade36(): |
||||
# Descending Triangle: Sell when a stock forms a descending triangle pattern |
||||
ticker = random.choice(tickers) |
||||
# Simplified logic: check for lower highs and flat lows |
||||
if prices[ticker][0] < prices[ticker][2] < prices[ticker][4] and prices[ticker][1] == prices[ticker][3] == prices[ticker][5]: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade37(): |
||||
# Flag/Pennant: Buy or sell based on the direction of the flag/pennant pattern |
||||
ticker = random.choice(tickers) |
||||
# Simplified logic: check for a consolidation period after a strong move |
||||
if abs(prices[ticker][0] - np.mean(prices[ticker][1:5])) < 0.05 and abs(prices[ticker][5] - prices[ticker][6]) > 0.1: |
||||
# Buy if the prior move was up, sell if down |
||||
return [Trade(ticker, 1 if prices[ticker][5] > prices[ticker][6] else -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade38(): |
||||
# Gap Up: Buy when a stock opens significantly higher than its previous close |
||||
ticker = random.choice(tickers) |
||||
if prices[ticker][0] > prices[ticker][1] * 1.05: # 5% gap up |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade39(): |
||||
# Gap Down: Sell when a stock opens significantly lower than its previous close |
||||
ticker = random.choice(tickers) |
||||
if prices[ticker][0] < prices[ticker][1] * 0.95: # 5% gap down |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade40(): |
||||
# Rounding Bottom: Buy when a stock forms a rounding bottom pattern |
||||
ticker = random.choice(tickers) |
||||
# Simplified logic: check for a gradual price increase after a period of decline |
||||
if prices[ticker][0] > prices[ticker][2] > prices[ticker][4] and prices[ticker][1] < prices[ticker][3] < prices[ticker][5]: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade41(): |
||||
# Overbought/Oversold (RSI): Sell if RSI is above 70, buy if below 30 |
||||
ticker = random.choice(tickers) |
||||
rsi = calculate_rsi(prices[ticker], 14) # Assuming you have an RSI calculation function |
||||
if rsi > 70: |
||||
return [Trade(ticker, -1)] |
||||
elif rsi < 30: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade42(): |
||||
# Bollinger Bands Breakout: Buy if price breaks above the upper band, sell if below lower |
||||
ticker = random.choice(tickers) |
||||
upper, middle, lower = calculate_bollinger_bands(prices[ticker], 20, 2) # Assuming you have a Bollinger Band calculation function |
||||
if prices[ticker][0] > upper: |
||||
return [Trade(ticker, 1)] |
||||
elif prices[ticker][0] < lower: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade43(): |
||||
# Channel Breakout: Buy or sell when price breaks out of a recent price channel |
||||
ticker = random.choice(tickers) |
||||
highs = [max(prices[ticker][i:i+5]) for i in range(len(prices[ticker]) - 5)] |
||||
lows = [min(prices[ticker][i:i+5]) for i in range(len(prices[ticker]) - 5)] |
||||
if prices[ticker][0] > highs[-1]: |
||||
return [Trade(ticker, 1)] |
||||
elif prices[ticker][0] < lows[-1]: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade44(): |
||||
# Trend Following: Buy if the 20-day moving average is rising, sell if falling |
||||
ticker = random.choice(tickers) |
||||
ma20_today = np.mean(prices[ticker][:20]) |
||||
ma20_yesterday = np.mean(prices[ticker][1:21]) |
||||
if ma20_today > ma20_yesterday: |
||||
return [Trade(ticker, 1)] |
||||
elif ma20_today < ma20_yesterday: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade45(): |
||||
# MACD Crossover: Buy when MACD line crosses above signal line, sell when below |
||||
ticker = random.choice(tickers) |
||||
macd_line, signal_line = calculate_macd(prices[ticker]) # Assuming you have a MACD calculation function |
||||
if macd_line[-1] > signal_line[-1] and macd_line[-2] <= signal_line[-2]: |
||||
return [Trade(ticker, 1)] |
||||
elif macd_line[-1] < signal_line[-1] and macd_line[-2] >= signal_line[-2]: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade46(): |
||||
# Stochastic Oscillator: Buy if %K crosses above %D in oversold zone, sell if opposite |
||||
ticker = random.choice(tickers) |
||||
k_line, d_line = calculate_stochastic(prices[ticker]) # Assuming you have a Stochastic calculation function |
||||
if k_line[-1] > d_line[-1] and k_line[-1] < 20: |
||||
return [Trade(ticker, 1)] |
||||
elif k_line[-1] < d_line[-1] and k_line[-1] > 80: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade47(): |
||||
# Volume Spike: Buy if today's volume is much higher than the average |
||||
# You'd need volume data for this strategy |
||||
ticker = random.choice(tickers) |
||||
avg_volume = np.mean(volumes[ticker][1:]) # Assuming you have 'volumes' data |
||||
if volumes[ticker][0] > avg_volume * 2: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade48(): |
||||
# Price Spike: Buy if today's price increase is much higher than average daily change |
||||
ticker = random.choice(tickers) |
||||
daily_changes = [(prices[ticker][i] - prices[ticker][i + 1]) / prices[ticker][i + 1] for i in range(len(prices[ticker]) - 1)] |
||||
avg_change = np.mean(daily_changes) |
||||
today_change = (prices[ticker][0] - prices[ticker][1]) / prices[ticker][1] |
||||
if today_change > avg_change * 2: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade49(): |
||||
# Mean Reversion (Long-term): Buy if the price is below its 200-day moving average |
||||
ticker = random.choice(tickers) |
||||
ma200 = np.mean(prices[ticker]) |
||||
if prices[ticker][0] < ma200: |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade50(): |
||||
# Trend Reversal (Parabolic SAR): Buy or sell based on the Parabolic SAR indicator |
||||
# Assuming you have a Parabolic SAR calculation function |
||||
ticker = random.choice(tickers) |
||||
sar = calculate_parabolic_sar(prices[ticker]) |
||||
if prices[ticker][0] > sar[-1]: |
||||
return [Trade(ticker, 1)] |
||||
elif prices[ticker][0] < sar[-1]: |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade51(): |
||||
# Market Outperformance: Buy stocks whose daily returns beat the market |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
market_return = (total_market_values[0] - total_market_values[1]) / total_market_values[1] |
||||
outperformers = [t for t in tickers if (prices[t][0] - prices[t][1]) / prices[t][1] > market_return] |
||||
if outperformers: |
||||
ticker = random.choice(outperformers) |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade52(): |
||||
# Market Underperformance: Short stocks whose daily returns lag the market |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
market_return = (total_market_values[0] - total_market_values[1]) / total_market_values[1] |
||||
underperformers = [t for t in tickers if (prices[t][0] - prices[t][1]) / prices[t][1] < market_return] |
||||
if underperformers: |
||||
ticker = random.choice(underperformers) |
||||
return [Trade(ticker, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade53(): |
||||
# Relative Strength to Market: Buy the stock with the highest relative strength to the market |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
market_return = (total_market_values[0] - total_market_values[1]) / total_market_values[1] |
||||
relative_strengths = {t: ((prices[t][0] - prices[t][1]) / prices[t][1]) - market_return for t in tickers} |
||||
ticker = max(relative_strengths, key=relative_strengths.get) |
||||
return [Trade(ticker, 1)] |
||||
|
||||
def trade54(): |
||||
# Relative Weakness to Market: Short the stock with the lowest relative strength to the market |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
market_return = (total_market_values[0] - total_market_values[1]) / total_market_values[1] |
||||
relative_strengths = {t: ((prices[t][0] - prices[t][1]) / prices[t][1]) - market_return for t in tickers} |
||||
ticker = min(relative_strengths, key=relative_strengths.get) |
||||
return [Trade(ticker, -1)] |
||||
|
||||
def trade55(): |
||||
# Sector vs. Market: Buy top stock from sector outperforming the market, short from underperforming |
||||
# Assuming you have sector data (e.g., 'sector_map' dict: ticker -> sector) |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
market_return = (total_market_values[0] - total_market_values[1]) / total_market_values[1] |
||||
sector_returns = {s: np.mean([(prices[t][0] - prices[t][1]) / prices[t][1] for t in tickers if sector_map[t] == s]) for s in set(sector_map.values())} |
||||
outperforming_sectors = [s for s in sector_returns if sector_returns[s] > market_return] |
||||
underperforming_sectors = [s for s in sector_returns if sector_returns[s] < market_return] |
||||
trades = [] |
||||
if outperforming_sectors: |
||||
top_ticker = max([(t, prices[t][0]) for t in tickers if sector_map[t] == random.choice(outperforming_sectors)], key=lambda x: x[1])[0] |
||||
trades.append(Trade(top_ticker, 1)) |
||||
if underperforming_sectors: |
||||
bottom_ticker = min([(t, prices[t][0]) for t in tickers if sector_map[t] == random.choice(underperforming_sectors)], key=lambda x: x[1])[0] |
||||
trades.append(Trade(bottom_ticker, -1)) |
||||
return trades |
||||
|
||||
def trade56(): |
||||
# Market-Neutral Pairs: Long/short pairs of stocks with similar market betas |
||||
betas = {t: random.uniform(0.8, 1.2) for t in tickers} # Placeholder, calculate actual betas |
||||
pairs = [(t1, t2) for t1 in tickers for t2 in tickers if abs(betas[t1] - betas[t2]) < 0.1 and t1 != t2] |
||||
if pairs: |
||||
t1, t2 = random.choice(pairs) |
||||
return [Trade(t1, 1), Trade(t2, -1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade57(): |
||||
# Beta Rotation: Buy high-beta stocks if the market is rising, low-beta if falling |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
market_return = (total_market_values[0] - total_market_values[1]) / total_market_values[1] |
||||
betas = {t: random.uniform(0.5, 2) for t in tickers} # Placeholder, calculate actual betas |
||||
if market_return > 0: # Market is rising |
||||
target_beta = 1.5 # Example target for high-beta |
||||
else: |
||||
target_beta = 0.8 # Example target for low-beta |
||||
closest_ticker = min(tickers, key=lambda t: abs(betas[t] - target_beta)) |
||||
return [Trade(closest_ticker, 1 if market_return > 0 else -1)] # Buy if rising, short if falling |
||||
|
||||
def trade58(): |
||||
# Market Timing with Relative Strength: Buy strong stocks in up markets, sell in down markets |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
market_return = (total_market_values[0] - total_market_values[1]) / total_market_values[1] |
||||
relative_strengths = {t: ((prices[t][0] - prices[t][-1]) / prices[t][-1]) for t in tickers} # Calculate over a longer period (e.g., 20 days) |
||||
if market_return > 0: |
||||
strongest = max(relative_strengths, key=relative_strengths.get) |
||||
return [Trade(strongest, 1)] |
||||
else: |
||||
weakest = min(relative_strengths, key=relative_strengths.get) |
||||
return [Trade(weakest, -1)] |
||||
|
||||
def trade59(): |
||||
# Relative Value to Market: Buy stocks trading below their historical average relative to the market |
||||
# Requires historical data to calculate averages |
||||
total_market_values = [sum(prices[t][i] for t in tickers) for i in range(len(prices[tickers[0]]))] |
||||
relative_values = {t: prices[t][0] / total_market_values[0] for t in tickers} # Current relative value |
||||
historical_averages = {t: 0.05 for t in tickers} # Placeholder, calculate actual averages |
||||
undervalued = [t for t in tickers if relative_values[t] < historical_averages[t] * 0.95] # Allow some buffer |
||||
if undervalued: |
||||
ticker = random.choice(undervalued) |
||||
return [Trade(ticker, 1)] |
||||
else: |
||||
return [] |
||||
|
||||
def trade60(): |
||||
# Market-Cap Weighted: Allocate trade amounts proportional to each stock's market cap relative to total market |
||||
total_market_value = sum(prices[t][0] for t in tickers) |
||||
market_caps = {t: prices[t][0] * 1000 for t in tickers} # Assuming 1000 shares outstanding for each stock |
||||
weights = {t: market_caps[t] / total_market_value for t in tickers} |
||||
total_trade_amount = 100 # Example |
||||
trades = [Trade(t, int(weights[t] * total_trade_amount)) for t in tickers] |
||||
return trades |
@ -0,0 +1,884 @@
|
||||
# tickers is a list of stock tickers |
||||
import tickers |
||||
|
||||
# prices is a dict; the key is a ticker and the value is a list of historic prices, today first |
||||
import prices |
||||
|
||||
# Trade represents a decision to buy or sell a quantity of a ticker |
||||
import Trade |
||||
|
||||
import random |
||||
import numpy as np |
||||
|
||||
def trade2(): |
||||
# Buy top performing stock in the last 5 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:5]) for ticker in tickers} |
||||
best_ticker = max(avg_prices, key=avg_prices.get) |
||||
trade = Trade(best_ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade3(): |
||||
# Sell worst performing stock in the last 5 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:5]) for ticker in tickers} |
||||
worst_ticker = min(avg_prices, key=avg_prices.get) |
||||
trade = Trade(worst_ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade4(): |
||||
# Buy random stock from top 5 performing in the last 10 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:10]) for ticker in tickers} |
||||
top_5_tickers = sorted(avg_prices, key=avg_prices.get, reverse=True)[:5] |
||||
ticker = random.choice(top_5_tickers) |
||||
trade = Trade(ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade5(): |
||||
# Sell random stock from bottom 5 performing in the last 10 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:10]) for ticker in tickers} |
||||
bottom_5_tickers = sorted(avg_prices, key=avg_prices.get)[:5] |
||||
ticker = random.choice(bottom_5_tickers) |
||||
trade = Trade(ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade6(): |
||||
# Buy stocks with a positive trend over the last 7 days |
||||
trending_up = [ticker for ticker in tickers if prices[ticker][0] > prices[ticker][6]] |
||||
ticker = random.choice(trending_up) |
||||
trade = Trade(ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade7(): |
||||
# Sell stocks with a negative trend over the last 7 days |
||||
trending_down = [ticker for ticker in tickers if prices[ticker][0] < prices[ticker][6]] |
||||
ticker = random.choice(trending_down) |
||||
trade = Trade(ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade8(): |
||||
# Buy stocks with the lowest volatility over the last 20 days |
||||
volatilities = {ticker: np.std(prices[ticker][:20]) for ticker in tickers} |
||||
least_volatile = min(volatilities, key=volatilities.get) |
||||
trade = Trade(least_volatile, 100) |
||||
return [trade] |
||||
|
||||
def trade9(): |
||||
# Sell stocks with the highest volatility over the last 20 days |
||||
volatilities = {ticker: np.std(prices[ticker][:20]) for ticker in tickers} |
||||
most_volatile = max(volatilities, key=volatilities.get) |
||||
trade = Trade(most_volatile, -100) |
||||
return [trade] |
||||
|
||||
def trade10(): |
||||
# Random mixed strategy: randomly buy or sell a random stock |
||||
ticker = random.choice(tickers) |
||||
quantity = random.choice([-100, 100]) |
||||
trade = Trade(ticker, quantity) |
||||
return [trade] |
||||
|
||||
def trade11(): |
||||
# Buy the top 3 performing stocks in the last 15 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:15]) for ticker in tickers} |
||||
top_3_tickers = sorted(avg_prices, key=avg_prices.get, reverse=True)[:3] |
||||
trades = [Trade(ticker, 100) for ticker in top_3_tickers] |
||||
return trades |
||||
|
||||
def trade12(): |
||||
# Sell the bottom 3 performing stocks in the last 15 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:15]) for ticker in tickers} |
||||
bottom_3_tickers = sorted(avg_prices, key=avg_prices.get)[:3] |
||||
trades = [Trade(ticker, -100) for ticker in bottom_3_tickers] |
||||
return trades |
||||
|
||||
def trade13(): |
||||
# Buy 2 random stocks with the highest increase in price in the last 10 days |
||||
price_increases = {ticker: prices[ticker][0] - prices[ticker][9] for ticker in tickers} |
||||
top_2_increases = sorted(price_increases, key=price_increases.get, reverse=True)[:2] |
||||
trades = [Trade(ticker, 100) for ticker in top_2_increases] |
||||
return trades |
||||
|
||||
def trade14(): |
||||
# Sell 2 random stocks with the highest decrease in price in the last 10 days |
||||
price_decreases = {ticker: prices[ticker][0] - prices[ticker][9] for ticker in tickers} |
||||
top_2_decreases = sorted(price_decreases, key=price_decreases.get)[:2] |
||||
trades = [Trade(ticker, -100) for ticker in top_2_decreases] |
||||
return trades |
||||
|
||||
def trade15(): |
||||
# Buy stocks that have shown the highest volatility in the last 30 days |
||||
volatilities = {ticker: np.std(prices[ticker][:30]) for ticker in tickers} |
||||
high_volatility_tickers = sorted(volatilities, key=volatilities.get, reverse=True)[:3] |
||||
trades = [Trade(ticker, 100) for ticker in high_volatility_tickers] |
||||
return trades |
||||
|
||||
def trade16(): |
||||
# Sell stocks that have shown the lowest volatility in the last 30 days |
||||
volatilities = {ticker: np.std(prices[ticker][:30]) for ticker in tickers} |
||||
low_volatility_tickers = sorted(volatilities, key=volatilities.get)[:3] |
||||
trades = [Trade(ticker, -100) for ticker in low_volatility_tickers] |
||||
return trades |
||||
|
||||
def trade17(): |
||||
# Buy stocks with prices above their 50-day moving average |
||||
ma_50 = {ticker: np.mean(prices[ticker][:50]) for ticker in tickers} |
||||
above_ma_tickers = [ticker for ticker in tickers if prices[ticker][0] > ma_50[ticker]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(above_ma_tickers, min(3, len(above_ma_tickers)))] |
||||
return trades |
||||
|
||||
def trade18(): |
||||
# Sell stocks with prices below their 50-day moving average |
||||
ma_50 = {ticker: np.mean(prices[ticker][:50]) for ticker in tickers} |
||||
below_ma_tickers = [ticker for ticker in tickers if prices[ticker][0] < ma_50[ticker]] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(below_ma_tickers, min(3, len(below_ma_tickers)))] |
||||
return trades |
||||
|
||||
def trade19(): |
||||
# Mixed strategy: buy 2 random stocks and sell 2 random stocks |
||||
buy_tickers = random.sample(tickers, 2) |
||||
sell_tickers = random.sample([ticker for ticker in tickers if ticker not in buy_tickers], 2) |
||||
trades = [Trade(ticker, 100) for ticker in buy_tickers] + [Trade(ticker, -100) for ticker in sell_tickers] |
||||
return trades |
||||
|
||||
def trade20(): |
||||
# Buy stocks that have positive return in the last 20 days and sell those with negative return |
||||
returns = {ticker: (prices[ticker][0] - prices[ticker][19]) / prices[ticker][19] for ticker in tickers} |
||||
buy_tickers = [ticker for ticker in tickers if returns[ticker] > 0] |
||||
sell_tickers = [ticker for ticker in tickers if returns[ticker] < 0] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(buy_tickers, min(2, len(buy_tickers)))] + \ |
||||
[Trade(ticker, -100) for ticker in random.sample(sell_tickers, min(2, len(sell_tickers)))] |
||||
return trades |
||||
|
||||
def trade21(): |
||||
# Buy the top performing stock in the last 3 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:3]) for ticker in tickers} |
||||
best_ticker = max(avg_prices, key=avg_prices.get) |
||||
trade = Trade(best_ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade22(): |
||||
# Sell the worst performing stock in the last 3 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:3]) for ticker in tickers} |
||||
worst_ticker = min(avg_prices, key=avg_prices.get) |
||||
trade = Trade(worst_ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade23(): |
||||
# Buy stocks that have not changed price in the last 7 days |
||||
stable_tickers = [ticker for ticker in tickers if prices[ticker][0] == prices[ticker][6]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(stable_tickers, min(3, len(stable_tickers)))] |
||||
return trades |
||||
|
||||
def trade24(): |
||||
# Sell stocks that have the smallest price change in the last 5 days |
||||
smallest_changes = sorted(tickers, key=lambda t: abs(prices[t][0] - prices[t][4]))[:3] |
||||
trades = [Trade(ticker, -100) for ticker in smallest_changes] |
||||
return trades |
||||
|
||||
def trade25(): |
||||
# Buy random stocks from the top 10 highest priced stocks |
||||
highest_priced = sorted(tickers, key=lambda t: prices[t][0], reverse=True)[:10] |
||||
ticker = random.choice(highest_priced) |
||||
trade = Trade(ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade26(): |
||||
# Sell random stocks from the bottom 10 lowest priced stocks |
||||
lowest_priced = sorted(tickers, key=lambda t: prices[t][0])[:10] |
||||
ticker = random.choice(lowest_priced) |
||||
trade = Trade(ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade27(): |
||||
# Buy 2 stocks with the highest momentum (last 5 days) |
||||
momentums = {ticker: prices[ticker][0] - prices[ticker][4] for ticker in tickers} |
||||
top_momentum_tickers = sorted(momentums, key=momentums.get, reverse=True)[:2] |
||||
trades = [Trade(ticker, 100) for ticker in top_momentum_tickers] |
||||
return trades |
||||
|
||||
def trade28(): |
||||
# Sell 2 stocks with the lowest momentum (last 5 days) |
||||
momentums = {ticker: prices[ticker][0] - prices[ticker][4] for ticker in tickers} |
||||
lowest_momentum_tickers = sorted(momentums, key=momentums.get)[:2] |
||||
trades = [Trade(ticker, -100) for ticker in lowest_momentum_tickers] |
||||
return trades |
||||
|
||||
def trade29(): |
||||
# Buy the stock with the highest daily price increase yesterday |
||||
yesterday_increase = {ticker: prices[ticker][1] - prices[ticker][2] for ticker in tickers} |
||||
best_yesterday_ticker = max(yesterday_increase, key=yesterday_increase.get) |
||||
trade = Trade(best_yesterday_ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade30(): |
||||
# Sell the stock with the highest daily price decrease yesterday |
||||
yesterday_decrease = {ticker: prices[ticker][1] - prices[ticker][2] for ticker in tickers} |
||||
worst_yesterday_ticker = min(yesterday_decrease, key=yesterday_decrease.get) |
||||
trade = Trade(worst_yesterday_ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade31(): |
||||
# Long/short strategy: Buy the top performing stock and sell the worst performing stock over the last 7 days |
||||
avg_prices = {ticker: np.mean(prices[ticker][:7]) for ticker in tickers} |
||||
best_ticker = max(avg_prices, key=avg_prices.get) |
||||
worst_ticker = min(avg_prices, key=avg_prices.get) |
||||
trades = [Trade(best_ticker, 100), Trade(worst_ticker, -100)] |
||||
return trades |
||||
|
||||
def trade32(): |
||||
# Buy stocks that have had a positive return in the last 5 days and sell those with a negative return |
||||
returns = {ticker: (prices[ticker][0] - prices[ticker][4]) / prices[ticker][4] for ticker in tickers} |
||||
buy_tickers = [ticker for ticker in tickers if returns[ticker] > 0] |
||||
sell_tickers = [ticker for ticker in tickers if returns[ticker] < 0] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(buy_tickers, min(2, len(buy_tickers)))] + \ |
||||
[Trade(ticker, -100) for ticker in random.sample(sell_tickers, min(2, len(sell_tickers)))] |
||||
return trades |
||||
|
||||
def trade33(): |
||||
# Buy 2 stocks with the highest price-to-earnings ratio and sell 2 with the lowest |
||||
pe_ratios = {ticker: random.uniform(10, 30) for ticker in tickers} # Mock P/E ratios |
||||
top_pe_tickers = sorted(pe_ratios, key=pe_ratios.get, reverse=True)[:2] |
||||
low_pe_tickers = sorted(pe_ratios, key=pe_ratios.get)[:2] |
||||
trades = [Trade(ticker, 100) for ticker in top_pe_tickers] + [Trade(ticker, -100) for ticker in low_pe_tickers] |
||||
return trades |
||||
|
||||
def trade34(): |
||||
# Buy the stock with the highest volume and sell the one with the lowest volume |
||||
volumes = {ticker: random.randint(1000, 10000) for ticker in tickers} # Mock volumes |
||||
high_volume_ticker = max(volumes, key=volumes.get) |
||||
low_volume_ticker = min(volumes, key=volumes.get) |
||||
trades = [Trade(high_volume_ticker, 100), Trade(low_volume_ticker, -100)] |
||||
return trades |
||||
|
||||
def trade35(): |
||||
# Buy 3 stocks with the highest recent momentum and sell 3 with the lowest recent momentum |
||||
momentums = {ticker: prices[ticker][0] - prices[ticker][5] for ticker in tickers} |
||||
top_momentum_tickers = sorted(momentums, key=momentums.get, reverse=True)[:3] |
||||
low_momentum_tickers = sorted(momentums, key=momentums.get)[:3] |
||||
trades = [Trade(ticker, 100) for ticker in top_momentum_tickers] + [Trade(ticker, -100) for ticker in low_momentum_tickers] |
||||
return trades |
||||
|
||||
def trade36(): |
||||
# Buy stocks in the technology sector and sell stocks in the energy sector |
||||
tech_stocks = random.sample(tickers, 3) # Mock tech stocks |
||||
energy_stocks = random.sample(tickers, 3) # Mock energy stocks |
||||
trades = [Trade(ticker, 100) for ticker in tech_stocks] + [Trade(ticker, -100) for ticker in energy_stocks] |
||||
return trades |
||||
|
||||
def trade37(): |
||||
# Long/short strategy: Buy the top 2 stocks with the highest recent gains and sell the top 2 with the highest recent losses |
||||
recent_gains = {ticker: prices[ticker][0] - prices[ticker][10] for ticker in tickers} |
||||
top_gainers = sorted(recent_gains, key=recent_gains.get, reverse=True)[:2] |
||||
top_losers = sorted(recent_gains, key=recent_gains.get)[:2] |
||||
trades = [Trade(ticker, 100) for ticker in top_gainers] + [Trade(ticker, -100) for ticker in top_losers] |
||||
return trades |
||||
|
||||
def trade38(): |
||||
# Buy the stocks with the highest dividend yield and sell those with the lowest |
||||
dividend_yields = {ticker: random.uniform(1, 5) for ticker in tickers} # Mock dividend yields |
||||
high_yield_tickers = sorted(dividend_yields, key=dividend_yields.get, reverse=True)[:2] |
||||
low_yield_tickers = sorted(dividend_yields, key=dividend_yields.get)[:2] |
||||
trades = [Trade(ticker, 100) for ticker in high_yield_tickers] + [Trade(ticker, -100) for ticker in low_yield_tickers] |
||||
return trades |
||||
|
||||
def trade39(): |
||||
# Buy stocks that are trading near their 52-week highs and sell those near their 52-week lows |
||||
highs_52w = {ticker: max(prices[ticker]) for ticker in tickers} |
||||
lows_52w = {ticker: min(prices[ticker]) for ticker in tickers} |
||||
near_highs = [ticker for ticker in tickers if prices[ticker][0] >= 0.9 * highs_52w[ticker]] |
||||
near_lows = [ticker for ticker in tickers if prices[ticker][0] <= 1.1 * lows_52w[ticker]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(near_highs, min(2, len(near_highs)))] + \ |
||||
[Trade(ticker, -100) for ticker in random.sample(near_lows, min(2, len(near_lows)))] |
||||
return trades |
||||
|
||||
def trade40(): |
||||
# Long/short strategy: Buy 2 random stocks from the top 10 performing sectors and sell 2 from the bottom 10 |
||||
sectors = {ticker: random.choice(['Tech', 'Energy', 'Health', 'Finance', 'Retail']) for ticker in tickers} |
||||
sector_performance = {sector: random.uniform(-10, 10) for sector in set(sectors.values())} |
||||
top_sectors = sorted(sector_performance, key=sector_performance.get, reverse=True)[:2] |
||||
bottom_sectors = sorted(sector_performance, key=sector_performance.get)[:2] |
||||
buy_tickers = [ticker for ticker in tickers if sectors[ticker] in top_sectors] |
||||
sell_tickers = [ticker for ticker in tickers if sectors[ticker] in bottom_sectors] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(buy_tickers, min(2, len(buy_tickers)))] + \ |
||||
[Trade(ticker, -100) for ticker in random.sample(sell_tickers, min(2, len(sell_tickers)))] |
||||
return trades |
||||
|
||||
def trade41(): |
||||
# Buy the stock with the highest price increase today |
||||
price_increases = {ticker: prices[ticker][0] - prices[ticker][1] for ticker in tickers} |
||||
best_ticker = max(price_increases, key=price_increases.get) |
||||
trade = Trade(best_ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade42(): |
||||
# Sell the stock with the highest price decrease today |
||||
price_decreases = {ticker: prices[ticker][0] - prices[ticker][1] for ticker in tickers} |
||||
worst_ticker = min(price_decreases, key=price_decreases.get) |
||||
trade = Trade(worst_ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade43(): |
||||
# Buy stocks that have had a positive return in the last 3 days |
||||
returns = {ticker: (prices[ticker][0] - prices[ticker][2]) / prices[ticker][2] for ticker in tickers} |
||||
buy_tickers = [ticker for ticker in tickers if returns[ticker] > 0] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(buy_tickers, min(3, len(buy_tickers)))] |
||||
return trades |
||||
|
||||
def trade44(): |
||||
# Sell stocks that have had a negative return in the last 3 days |
||||
returns = {ticker: (prices[ticker][0] - prices[ticker][2]) / prices[ticker][2] for ticker in tickers} |
||||
sell_tickers = [ticker for ticker in tickers if returns[ticker] < 0] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(sell_tickers, min(3, len(sell_tickers)))] |
||||
return trades |
||||
|
||||
def trade45(): |
||||
# Buy the stock with the highest average return over the last 10 days |
||||
avg_returns = {ticker: np.mean([(prices[ticker][i] - prices[ticker][i+1]) / prices[ticker][i+1] for i in range(9)]) for ticker in tickers} |
||||
best_ticker = max(avg_returns, key=avg_returns.get) |
||||
trade = Trade(best_ticker, 100) |
||||
return [trade] |
||||
|
||||
def trade46(): |
||||
# Sell the stock with the lowest average return over the last 10 days |
||||
avg_returns = {ticker: np.mean([(prices[ticker][i] - prices[ticker][i+1]) / prices[ticker][i+1] for i in range(9)]) for ticker in tickers} |
||||
worst_ticker = min(avg_returns, key=avg_returns.get) |
||||
trade = Trade(worst_ticker, -100) |
||||
return [trade] |
||||
|
||||
def trade47(): |
||||
# Buy stocks that are oversold based on RSI (Randomly assigned for simplicity) |
||||
rsi = {ticker: random.uniform(0, 100) for ticker in tickers} |
||||
oversold_tickers = [ticker for ticker in tickers if rsi[ticker] < 30] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(oversold_tickers, min(3, len(oversold_tickers)))] |
||||
return trades |
||||
|
||||
def trade48(): |
||||
# Sell stocks that are overbought based on RSI (Randomly assigned for simplicity) |
||||
rsi = {ticker: random.uniform(0, 100) for ticker in tickers} |
||||
overbought_tickers = [ticker for ticker in tickers if rsi[ticker] > 70] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(overbought_tickers, min(3, len(overbought_tickers)))] |
||||
return trades |
||||
|
||||
def trade49(): |
||||
# Buy stocks with positive momentum over the last 20 days |
||||
momentums = {ticker: prices[ticker][0] - prices[ticker][19] for ticker in tickers} |
||||
positive_momentum_tickers = [ticker for ticker in momentums if momentums[ticker] > 0] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(positive_momentum_tickers, min(3, len(positive_momentum_tickers)))] |
||||
return trades |
||||
|
||||
def trade50(): |
||||
# Sell stocks with negative momentum over the last 20 days |
||||
momentums = {ticker: prices[ticker][0] - prices[ticker][19] for ticker in tickers} |
||||
negative_momentum_tickers = [ticker for ticker in momentums if momentums[ticker] < 0] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(negative_momentum_tickers, min(3, len(negative_momentum_tickers)))] |
||||
return trades |
||||
|
||||
def trade51(): |
||||
# Buy stocks that have a high positive correlation with a randomly chosen strong performer |
||||
import scipy.stats |
||||
base_ticker = random.choice(tickers) |
||||
base_prices = prices[base_ticker] |
||||
correlations = {ticker: scipy.stats.pearsonr(base_prices, prices[ticker])[0] for ticker in tickers if ticker != base_ticker} |
||||
high_corr_tickers = [ticker for ticker, corr in correlations.items() if corr > 0.8] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(high_corr_tickers, min(3, len(high_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade52(): |
||||
# Sell stocks that have a high negative correlation with a randomly chosen weak performer |
||||
import scipy.stats |
||||
base_ticker = random.choice(tickers) |
||||
base_prices = prices[base_ticker] |
||||
correlations = {ticker: scipy.stats.pearsonr(base_prices, prices[ticker])[0] for ticker in tickers if ticker != base_ticker} |
||||
low_corr_tickers = [ticker for ticker, corr in correlations.items() if corr < -0.8] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(low_corr_tickers, min(3, len(low_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade53(): |
||||
# Long/short strategy: Buy stocks with high positive correlation and sell stocks with high negative correlation to a strong performer |
||||
import scipy.stats |
||||
base_ticker = random.choice(tickers) |
||||
base_prices = prices[base_ticker] |
||||
correlations = {ticker: scipy.stats.pearsonr(base_prices, prices[ticker])[0] for ticker in tickers if ticker != base_ticker} |
||||
high_corr_tickers = [ticker for ticker, corr in correlations.items() if corr > 0.7] |
||||
low_corr_tickers = [ticker for ticker, corr in correlations.items() if corr < -0.7] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(high_corr_tickers, min(2, len(high_corr_tickers)))] + \ |
||||
[Trade(ticker, -100) for ticker in random.sample(low_corr_tickers, min(2, len(low_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade54(): |
||||
# Buy stocks that have a high correlation with an index (e.g., S&P 500) |
||||
import scipy.stats |
||||
index_prices = [random.uniform(1000, 5000) for _ in range(len(prices[tickers[0]]))] # Mock index prices |
||||
correlations = {ticker: scipy.stats.pearsonr(index_prices, prices[ticker])[0] for ticker in tickers} |
||||
high_corr_tickers = [ticker for ticker, corr in correlations.items() if corr > 0.8] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(high_corr_tickers, min(3, len(high_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade55(): |
||||
# Sell stocks that have a low correlation with an index (e.g., S&P 500) |
||||
import scipy.stats |
||||
index_prices = [random.uniform(1000, 5000) for _ in range(len(prices[tickers[0]]))] # Mock index prices |
||||
correlations = {ticker: scipy.stats.pearsonr(index_prices, prices[ticker])[0] for ticker in tickers} |
||||
low_corr_tickers = [ticker for ticker, corr in correlations.items() if corr < 0.2] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(low_corr_tickers, min(3, len(low_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade56(): |
||||
# Long/short strategy: Buy stocks with high correlation and sell stocks with low correlation to a randomly chosen strong performer |
||||
import scipy.stats |
||||
base_ticker = random.choice(tickers) |
||||
base_prices = prices[base_ticker] |
||||
correlations = {ticker: scipy.stats.pearsonr(base_prices, prices[ticker])[0] for ticker in tickers if ticker != base_ticker} |
||||
high_corr_tickers = [ticker for ticker, corr in correlations.items() if corr > 0.7] |
||||
low_corr_tickers = [ticker for ticker, corr in correlations.items() if corr < 0.2] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(high_corr_tickers, min(2, len(high_corr_tickers)))] + \ |
||||
[Trade(ticker, -100) for ticker in random.sample(low_corr_tickers, min(2, len(low_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade57(): |
||||
# Buy stocks that are inversely correlated with a major sector ETF (mocked data) |
||||
import scipy.stats |
||||
sector_etf_prices = [random.uniform(50, 150) for _ in range(len(prices[tickers[0]]))] # Mock sector ETF prices |
||||
correlations = {ticker: scipy.stats.pearsonr(sector_etf_prices, prices[ticker])[0] for ticker in tickers} |
||||
inverse_corr_tickers = [ticker for ticker, corr in correlations.items() if corr < -0.7] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(inverse_corr_tickers, min(3, len(inverse_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade58(): |
||||
# Sell stocks that are highly correlated with a volatile index |
||||
import scipy.stats |
||||
volatile_index_prices = [random.uniform(1000, 2000) for _ in range(len(prices[tickers[0]]))] # Mock volatile index prices |
||||
correlations = {ticker: scipy.stats.pearsonr(volatile_index_prices, prices[ticker])[0] for ticker in tickers} |
||||
high_corr_tickers = [ticker for ticker, corr in correlations.items() if corr > 0.8] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(high_corr_tickers, min(3, len(high_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade59(): |
||||
# Buy stocks that are less correlated with the overall market (S&P 500) |
||||
import scipy.stats |
||||
market_prices = [random.uniform(1000, 5000) for _ in range(len(prices[tickers[0]]))] # Mock market index prices |
||||
correlations = {ticker: scipy.stats.pearsonr(market_prices, prices[ticker])[0] for ticker in tickers} |
||||
low_corr_tickers = [ticker for ticker, corr in correlations.items() if corr < 0.3] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(low_corr_tickers, min(3, len(low_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade60(): |
||||
# Sell stocks that are highly correlated with a specific commodity price (e.g., oil) |
||||
import scipy.stats |
||||
commodity_prices = [random.uniform(50, 100) for _ in range(len(prices[tickers[0]]))] # Mock commodity prices |
||||
correlations = {ticker: scipy.stats.pearsonr(commodity_prices, prices[ticker])[0] for ticker in tickers} |
||||
high_corr_tickers = [ticker for ticker, corr in correlations.items() if corr > 0.7] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(high_corr_tickers, min(3, len(high_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade61(): |
||||
# Buy stocks forming a "double bottom" pattern (last 5 days) |
||||
double_bottom_tickers = [ticker for ticker in tickers if prices[ticker][4] < prices[ticker][2] == prices[ticker][0] < prices[ticker][1] and prices[ticker][3] > prices[ticker][2]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(double_bottom_tickers, min(3, len(double_bottom_tickers)))] |
||||
return trades |
||||
|
||||
def trade62(): |
||||
# Sell stocks forming a "double top" pattern (last 5 days) |
||||
double_top_tickers = [ticker for ticker in tickers if prices[ticker][4] > prices[ticker][2] == prices[ticker][0] > prices[ticker][1] and prices[ticker][3] < prices[ticker][2]] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(double_top_tickers, min(3, len(double_top_tickers)))] |
||||
return trades |
||||
|
||||
def trade63(): |
||||
# Buy stocks showing a "head and shoulders" bottom pattern (last 7 days) |
||||
hs_bottom_tickers = [ticker for ticker in tickers if prices[ticker][6] > prices[ticker][5] < prices[ticker][4] > prices[ticker][3] < prices[ticker][2] and prices[ticker][1] < prices[ticker][0]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(hs_bottom_tickers, min(3, len(hs_bottom_tickers)))] |
||||
return trades |
||||
|
||||
def trade64(): |
||||
# Sell stocks showing a "head and shoulders" top pattern (last 7 days) |
||||
hs_top_tickers = [ticker for ticker in tickers if prices[ticker][6] < prices[ticker][5] > prices[ticker][4] < prices[ticker][3] > prices[ticker][2] and prices[ticker][1] > prices[ticker][0]] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(hs_top_tickers, min(3, len(hs_top_tickers)))] |
||||
return trades |
||||
|
||||
def trade65(): |
||||
# Buy stocks forming a "bullish flag" pattern (last 10 days) |
||||
bullish_flag_tickers = [ticker for ticker in tickers if prices[ticker][9] < prices[ticker][8] and all(prices[ticker][i] < prices[ticker][i+1] for i in range(8, 4, -1)) and all(prices[ticker][i] > prices[ticker][i+1] for i in range(4, 0, -1))] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(bullish_flag_tickers, min(3, len(bullish_flag_tickers)))] |
||||
return trades |
||||
|
||||
def trade66(): |
||||
# Sell stocks forming a "bearish flag" pattern (last 10 days) |
||||
bearish_flag_tickers = [ticker for ticker in tickers if prices[ticker][9] > prices[ticker][8] and all(prices[ticker][i] > prices[ticker][i+1] for i in range(8, 4, -1)) and all(prices[ticker][i] < prices[ticker][i+1] for i in range(4, 0, -1))] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(bearish_flag_tickers, min(3, len(bearish_flag_tickers)))] |
||||
return trades |
||||
|
||||
def trade67(): |
||||
# Buy stocks forming a "ascending triangle" pattern (last 15 days) |
||||
ascending_triangle_tickers = [ticker for ticker in tickers if prices[ticker][14] < prices[ticker][13] and prices[ticker][0] > prices[ticker][7] and all(prices[ticker][i] <= prices[ticker][i+1] for i in range(13))] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(ascending_triangle_tickers, min(3, len(ascending_triangle_tickers)))] |
||||
return trades |
||||
|
||||
def trade68(): |
||||
# Sell stocks forming a "descending triangle" pattern (last 15 days) |
||||
descending_triangle_tickers = [ticker for ticker in tickers if prices[ticker][14] > prices[ticker][13] and prices[ticker][0] < prices[ticker][7] and all(prices[ticker][i] >= prices[ticker][i+1] for i in range(13))] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(descending_triangle_tickers, min(3, len(descending_triangle_tickers)))] |
||||
return trades |
||||
|
||||
def trade69(): |
||||
# Buy stocks forming a "rounding bottom" pattern (last 20 days) |
||||
rounding_bottom_tickers = [ticker for ticker in tickers if all(prices[ticker][i] >= prices[ticker][i+1] for i in range(10)) and all(prices[ticker][i] <= prices[ticker][i+1] for i in range(10, 19))] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(rounding_bottom_tickers, min(3, len(rounding_bottom_tickers)))] |
||||
return trades |
||||
|
||||
def trade70(): |
||||
# Sell stocks forming a "rounding top" pattern (last 20 days) |
||||
rounding_top_tickers = [ticker for ticker in tickers if all(prices[ticker][i] <= prices[ticker][i+1] for i in range(10)) and all(prices[ticker][i] >= prices[ticker][i+1] for i in range(10, 19))] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(rounding_top_tickers, min(3, len(rounding_top_tickers)))] |
||||
return trades |
||||
|
||||
def trade71(): |
||||
# Buy stocks showing a strong upward trend over the last 10 days |
||||
upward_trend_tickers = [ticker for ticker in tickers if prices[ticker][0] > prices[ticker][9] and all(prices[ticker][i] >= prices[ticker][i+1] for i in range(9))] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(upward_trend_tickers, min(3, len(upward_trend_tickers)))] |
||||
return trades |
||||
|
||||
def trade72(): |
||||
# Sell stocks showing a strong downward trend over the last 10 days |
||||
downward_trend_tickers = [ticker for ticker in tickers if prices[ticker][0] < prices[ticker][9] and all(prices[ticker][i] <= prices[ticker][i+1] for i in range(9))] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(downward_trend_tickers, min(3, len(downward_trend_tickers)))] |
||||
return trades |
||||
|
||||
def trade73(): |
||||
# Buy stocks that have reverted to their mean price over the last 20 days |
||||
mean_reversion_tickers = [ticker for ticker in tickers if abs(prices[ticker][0] - np.mean(prices[ticker][:20])) < np.std(prices[ticker][:20])] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(mean_reversion_tickers, min(3, len(mean_reversion_tickers)))] |
||||
return trades |
||||
|
||||
def trade74(): |
||||
# Sell stocks that have deviated significantly from their mean price over the last 20 days |
||||
mean_deviation_tickers = [ticker for ticker in tickers if abs(prices[ticker][0] - np.mean(prices[ticker][:20])) > 2 * np.std(prices[ticker][:20])] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(mean_deviation_tickers, min(3, len(mean_deviation_tickers)))] |
||||
return trades |
||||
|
||||
def trade75(): |
||||
# Buy stocks that have shown increased volatility in the last 10 days compared to the previous 20 days |
||||
increased_volatility_tickers = [ticker for ticker in tickers if np.std(prices[ticker][:10]) > 1.5 * np.std(prices[ticker][10:30])] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(increased_volatility_tickers, min(3, len(increased_volatility_tickers)))] |
||||
return trades |
||||
|
||||
def trade76(): |
||||
# Sell stocks that have shown decreased volatility in the last 10 days compared to the previous 20 days |
||||
decreased_volatility_tickers = [ticker for ticker in tickers if np.std(prices[ticker][:10]) < 0.5 * np.std(prices[ticker][10:30])] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(decreased_volatility_tickers, min(3, len(decreased_volatility_tickers)))] |
||||
return trades |
||||
|
||||
def trade77(): |
||||
# Buy stocks that have broken above their previous 50-day high |
||||
previous_50_day_highs = {ticker: max(prices[ticker][1:51]) for ticker in tickers} |
||||
breakout_tickers = [ticker for ticker in tickers if prices[ticker][0] > previous_50_day_highs[ticker]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(breakout_tickers, min(3, len(breakout_tickers)))] |
||||
return trades |
||||
|
||||
def trade78(): |
||||
# Sell stocks that have broken below their previous 50-day low |
||||
previous_50_day_lows = {ticker: min(prices[ticker][1:51]) for ticker in tickers} |
||||
breakdown_tickers = [ticker for ticker in tickers if prices[ticker][0] < previous_50_day_lows[ticker]] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(breakdown_tickers, min(3, len(breakdown_tickers)))] |
||||
return trades |
||||
|
||||
def trade79(): |
||||
# Buy stocks that have shown a significant upward price spike in the last 3 days |
||||
price_spike_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][2]) / prices[ticker][2] > 0.1] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(price_spike_tickers, min(3, len(price_spike_tickers)))] |
||||
return trades |
||||
|
||||
def trade80(): |
||||
# Sell stocks that have shown a significant downward price spike in the last 3 days |
||||
price_drop_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][2]) / prices[ticker][2] < -0.1] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(price_drop_tickers, min(3, len(price_drop_tickers)))] |
||||
return trades |
||||
|
||||
def trade81(): |
||||
# Buy stocks that have formed a "golden cross" (50-day MA crosses above 200-day MA) |
||||
golden_cross_tickers = [ticker for ticker in tickers if np.mean(prices[ticker][:50]) > np.mean(prices[ticker][:200])] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(golden_cross_tickers, min(3, len(golden_cross_tickers)))] |
||||
return trades |
||||
|
||||
def trade82(): |
||||
# Sell stocks that have formed a "death cross" (50-day MA crosses below 200-day MA) |
||||
death_cross_tickers = [ticker for ticker in tickers if np.mean(prices[ticker][:50]) < np.mean(prices[ticker][:200])] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(death_cross_tickers, min(3, len(death_cross_tickers)))] |
||||
return trades |
||||
|
||||
def trade83(): |
||||
# Buy stocks that have shown an increase in trading volume in the last 5 days |
||||
volume_increase_tickers = [ticker for ticker in tickers if np.mean(prices[ticker][:5]) > 1.2 * np.mean(prices[ticker][5:10])] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(volume_increase_tickers, min(3, len(volume_increase_tickers)))] |
||||
return trades |
||||
|
||||
def trade84(): |
||||
# Sell stocks that have shown a decrease in trading volume in the last 5 days |
||||
volume_decrease_tickers = [ticker for ticker in tickers if np.mean(prices[ticker][:5]) < 0.8 * np.mean(prices[ticker][5:10])] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(volume_decrease_tickers, min(3, len(volume_decrease_tickers)))] |
||||
return trades |
||||
|
||||
def trade85(): |
||||
# Buy stocks that have shown consistent daily gains for the last 5 days |
||||
consistent_gainers = [ticker for ticker in tickers if all(prices[ticker][i] > prices[ticker][i+1] for i in range(5))] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(consistent_gainers, min(3, len(consistent_gainers)))] |
||||
return trades |
||||
|
||||
def trade86(): |
||||
# Sell stocks that have shown consistent daily losses for the last 5 days |
||||
consistent_losers = [ticker for ticker in tickers if all(prices[ticker][i] < prices[ticker][i+1] for i in range(5))] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(consistent_losers, min(3, len(consistent_losers)))] |
||||
return trades |
||||
|
||||
def trade87(): |
||||
# Buy stocks that are trading near their all-time highs |
||||
all_time_high_tickers = [ticker for ticker in tickers if prices[ticker][0] >= 0.95 * max(prices[ticker])] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(all_time_high_tickers, min(3, len(all_time_high_tickers)))] |
||||
return trades |
||||
|
||||
def trade88(): |
||||
# Sell stocks that are trading near their all-time lows |
||||
all_time_low_tickers = [ticker for ticker in tickers if prices[ticker][0] <= 1.05 * min(prices[ticker])] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(all_time_low_tickers, min(3, len(all_time_low_tickers)))] |
||||
return trades |
||||
|
||||
def trade89(): |
||||
# Buy stocks that have gapped up at market open today |
||||
gap_up_tickers = [ticker for ticker in tickers if prices[ticker][0] > 1.05 * prices[ticker][1]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(gap_up_tickers, min(3, len(gap_up_tickers)))] |
||||
return trades |
||||
|
||||
def trade90(): |
||||
# Sell stocks that have gapped down at market open today |
||||
gap_down_tickers = [ticker for ticker in tickers if prices[ticker][0] < 0.95 * prices[ticker][1]] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(gap_down_tickers, min(3, len(gap_down_tickers)))] |
||||
return trades |
||||
|
||||
def trade91(): |
||||
# Buy stocks that have shown a steady upward trend for the last 15 days |
||||
steady_uptrend_tickers = [ticker for ticker in tickers if all(prices[ticker][i] >= prices[ticker][i+1] for i in range(15))] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(steady_uptrend_tickers, min(3, len(steady_uptrend_tickers)))] |
||||
return trades |
||||
|
||||
def trade92(): |
||||
# Sell stocks that have shown a steady downward trend for the last 15 days |
||||
steady_downtrend_tickers = [ticker for ticker in tickers if all(prices[ticker][i] <= prices[ticker][i+1] for i in range(15))] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(steady_downtrend_tickers, min(3, len(steady_downtrend_tickers)))] |
||||
return trades |
||||
|
||||
def trade93(): |
||||
# Buy stocks that have outperformed the market index by 5% in the last 30 days |
||||
market_index_return = random.uniform(-0.05, 0.05) # Mock market index return |
||||
outperforming_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][29]) / prices[ticker][29] > market_index_return + 0.05] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(outperforming_tickers, min(3, len(outperforming_tickers)))] |
||||
return trades |
||||
|
||||
def trade94(): |
||||
# Sell stocks that have underperformed the market index by 5% in the last 30 days |
||||
market_index_return = random.uniform(-0.05, 0.05) # Mock market index return |
||||
underperforming_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][29]) / prices[ticker][29] < market_index_return - 0.05] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(underperforming_tickers, min(3, len(underperforming_tickers)))] |
||||
return trades |
||||
|
||||
def trade95(): |
||||
# Buy stocks that have broken above their previous 10-day high |
||||
previous_10_day_highs = {ticker: max(prices[ticker][1:11]) for ticker in tickers} |
||||
breakout_tickers = [ticker for ticker in tickers if prices[ticker][0] > previous_10_day_highs[ticker]] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(breakout_tickers, min(3, len(breakout_tickers)))] |
||||
return trades |
||||
|
||||
def trade96(): |
||||
# Sell stocks that have broken below their previous 10-day low |
||||
previous_10_day_lows = {ticker: min(prices[ticker][1:11]) for ticker in tickers} |
||||
breakdown_tickers = [ticker for ticker in tickers if prices[ticker][0] < previous_10_day_lows[ticker]] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(breakdown_tickers, min(3, len(breakdown_tickers)))] |
||||
return trades |
||||
|
||||
def trade97(): |
||||
# Buy stocks with a relative strength index (RSI) below 30 (oversold) |
||||
rsi = {ticker: random.uniform(0, 100) for ticker in tickers} # Mock RSI values |
||||
oversold_tickers = [ticker for ticker in tickers if rsi[ticker] < 30] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(oversold_tickers, min(3, len(oversold_tickers)))] |
||||
return trades |
||||
|
||||
def trade98(): |
||||
# Sell stocks with a relative strength index (RSI) above 70 (overbought) |
||||
rsi = {ticker: random.uniform(0, 100) for ticker in tickers} # Mock RSI values |
||||
overbought_tickers = [ticker for ticker in tickers if rsi[ticker] > 70] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(overbought_tickers, min(3, len(overbought_tickers)))] |
||||
return trades |
||||
|
||||
def trade99(): |
||||
# Buy stocks with a price-to-earnings ratio (P/E) below the industry average (mocked data) |
||||
pe_ratios = {ticker: random.uniform(10, 30) for ticker in tickers} # Mock P/E ratios |
||||
industry_average_pe = 20 # Mock industry average P/E |
||||
undervalued_tickers = [ticker for ticker in tickers if pe_ratios[ticker] < industry_average_pe] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(undervalued_tickers, min(3, len(undervalued_tickers)))] |
||||
return trades |
||||
|
||||
def trade100(): |
||||
# Sell stocks with a price-to-earnings ratio (P/E) above the industry average (mocked data) |
||||
pe_ratios = {ticker: random.uniform(10, 30) for ticker in tickers} # Mock P/E ratios |
||||
industry_average_pe = 20 # Mock industry average P/E |
||||
overvalued_tickers = [ticker for ticker in tickers if pe_ratios[ticker] > industry_average_pe] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(overvalued_tickers, min(3, len(overvalued_tickers)))] |
||||
return trades |
||||
|
||||
def trade101(): |
||||
# Buy stocks that have outperformed the market by more than 5% in the last 10 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(10)] |
||||
market_return = (market_total[0] - market_total[-1]) / market_total[-1] |
||||
outperforming_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][9]) / prices[ticker][9] > market_return + 0.05] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(outperforming_tickers, min(3, len(outperforming_tickers)))] |
||||
return trades |
||||
|
||||
def trade102(): |
||||
# Sell stocks that have underperformed the market by more than 5% in the last 10 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(10)] |
||||
market_return = (market_total[0] - market_total[-1]) / market_total[-1] |
||||
underperforming_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][9]) / prices[ticker][9] < market_return - 0.05] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(underperforming_tickers, min(3, len(underperforming_tickers)))] |
||||
return trades |
||||
|
||||
def trade103(): |
||||
# Buy stocks that have shown a positive return while the market showed a negative return over the last 5 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(5)] |
||||
market_return = (market_total[0] - market_total[-1]) / market_total[-1] |
||||
positive_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][4]) / prices[ticker][4] > 0 and market_return < 0] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(positive_tickers, min(3, len(positive_tickers)))] |
||||
return trades |
||||
|
||||
def trade104(): |
||||
# Sell stocks that have shown a negative return while the market showed a positive return over the last 5 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(5)] |
||||
market_return = (market_total[0] - market_total[-1]) / market_total[-1] |
||||
negative_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][4]) / prices[ticker][4] < 0 and market_return > 0] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(negative_tickers, min(3, len(negative_tickers)))] |
||||
return trades |
||||
|
||||
def trade105(): |
||||
# Buy stocks that have shown less volatility compared to the market over the last 20 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(20)] |
||||
market_volatility = np.std(market_total) |
||||
low_volatility_tickers = [ticker for ticker in tickers if np.std(prices[ticker][:20]) < market_volatility] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(low_volatility_tickers, min(3, len(low_volatility_tickers)))] |
||||
return trades |
||||
|
||||
def trade106(): |
||||
# Sell stocks that have shown more volatility compared to the market over the last 20 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(20)] |
||||
market_volatility = np.std(market_total) |
||||
high_volatility_tickers = [ticker for ticker in tickers if np.std(prices[ticker][:20]) > market_volatility] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(high_volatility_tickers, min(3, len(high_volatility_tickers)))] |
||||
return trades |
||||
|
||||
def trade107(): |
||||
# Buy stocks that have shown an increasing trend while the market showed a decreasing trend over the last 15 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(15)] |
||||
market_trend = market_total[0] > market_total[-1] |
||||
increasing_tickers = [ticker for ticker in tickers if prices[ticker][0] > prices[ticker][14] and not market_trend] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(increasing_tickers, min(3, len(increasing_tickers)))] |
||||
return trades |
||||
|
||||
def trade108(): |
||||
# Sell stocks that have shown a decreasing trend while the market showed an increasing trend over the last 15 days |
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market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(15)] |
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market_trend = market_total[0] < market_total[-1] |
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decreasing_tickers = [ticker for ticker in tickers if prices[ticker][0] < prices[ticker][14] and market_trend] |
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trades = [Trade(ticker, -100) for ticker in random.sample(decreasing_tickers, min(3, len(decreasing_tickers)))] |
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return trades |
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|
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def trade109(): |
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# Buy stocks that have broken above their previous 10-day high while the market is flat |
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market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(10)] |
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market_flat = abs((market_total[0] - market_total[-1]) / market_total[-1]) < 0.01 |
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previous_10_day_highs = {ticker: max(prices[ticker][1:11]) for ticker in tickers} |
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breakout_tickers = [ticker for ticker in tickers if prices[ticker][0] > previous_10_day_highs[ticker] and market_flat] |
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trades = [Trade(ticker, 100) for ticker in random.sample(breakout_tickers, min(3, len(breakout_tickers)))] |
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return trades |
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|
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def trade110(): |
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# Sell stocks that have broken below their previous 10-day low while the market is flat |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(10)] |
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market_flat = abs((market_total[0] - market_total[-1]) / market_total[-1]) < 0.01 |
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previous_10_day_lows = {ticker: min(prices[ticker][1:11]) for ticker in tickers} |
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breakdown_tickers = [ticker for ticker in tickers if prices[ticker][0] < previous_10_day_lows[ticker] and market_flat] |
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trades = [Trade(ticker, -100) for ticker in random.sample(breakdown_tickers, min(3, len(breakdown_tickers)))] |
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return trades |
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|
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def trade111(): |
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# Buy stocks that have shown a higher positive return compared to the market over the last 20 days |
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market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(20)] |
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market_return = (market_total[0] - market_total[-1]) / market_total[-1] |
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higher_positive_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][19]) / prices[ticker][19] > market_return] |
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trades = [Trade(ticker, 100) for ticker in random.sample(higher_positive_tickers, min(3, len(higher_positive_tickers)))] |
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return trades |
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|
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def trade112(): |
||||
# Sell stocks that have shown a higher negative return compared to the market over the last 20 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(20)] |
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market_return = (market_total[0] - market_total[-1]) / market_total[-1] |
||||
higher_negative_tickers = [ticker for ticker in tickers if (prices[ticker][0] - prices[ticker][19]) / prices[ticker][19] < market_return] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(higher_negative_tickers, min(3, len(higher_negative_tickers)))] |
||||
return trades |
||||
|
||||
def trade113(): |
||||
# Buy stocks that have shown less drawdown compared to the market over the last 30 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(30)] |
||||
market_drawdown = min(market_total) / max(market_total) |
||||
less_drawdown_tickers = [ticker for ticker in tickers if min(prices[ticker][:30]) / max(prices[ticker][:30]) > market_drawdown] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(less_drawdown_tickers, min(3, len(less_drawdown_tickers)))] |
||||
return trades |
||||
|
||||
def trade114(): |
||||
# Sell stocks that have shown more drawdown compared to the market over the last 30 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(30)] |
||||
market_drawdown = min(market_total) / max(market_total) |
||||
more_drawdown_tickers = [ticker for ticker in tickers if min(prices[ticker][:30]) / max(prices[ticker][:30]) < market_drawdown] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(more_drawdown_tickers, min(3, len(more_drawdown_tickers)))] |
||||
return trades |
||||
|
||||
def trade115(): |
||||
# Buy stocks that have had a smaller price range compared to the market over the last 15 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(15)] |
||||
market_range = max(market_total) - min(market_total) |
||||
small_range_tickers = [ticker for ticker in tickers if max(prices[ticker][:15]) - min(prices[ticker][:15]) < market_range] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(small_range_tickers, min(3, len(small_range_tickers)))] |
||||
return trades |
||||
|
||||
def trade116(): |
||||
# Sell stocks that have had a larger price range compared to the market over the last 15 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(15)] |
||||
market_range = max(market_total) - min(market_total) |
||||
large_range_tickers = [ticker for ticker in tickers if max(prices[ticker][:15]) - min(prices[ticker][:15]) > market_range] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(large_range_tickers, min(3, len(large_range_tickers)))] |
||||
return trades |
||||
|
||||
def trade117(): |
||||
# Buy stocks that have consistently stayed above their market-relative average price in the last 10 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(10)] |
||||
market_avg = sum(market_total) / len(market_total) |
||||
consistent_above_avg_tickers = [ticker for ticker in tickers if all(prices[ticker][i] > market_avg for i in range(10))] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(consistent_above_avg_tickers, min(3, len(consistent_above_avg_tickers)))] |
||||
return trades |
||||
|
||||
def trade118(): |
||||
# Sell stocks that have consistently stayed below their market-relative average price in the last 10 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(10)] |
||||
market_avg = sum(market_total) / len(market_total) |
||||
consistent_below_avg_tickers = [ticker for ticker in tickers if all(prices[ticker][i] < market_avg for i in range(10))] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(consistent_below_avg_tickers, min(3, len(consistent_below_avg_tickers)))] |
||||
return trades |
||||
|
||||
def trade119(): |
||||
# Buy stocks that have shown a positive correlation with the market trend over the last 20 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(20)] |
||||
market_trend = scipy.stats.linregress(range(20), market_total).slope |
||||
positive_corr_tickers = [ticker for ticker in tickers if scipy.stats.pearsonr(prices[ticker][:20], market_total)[0] > 0.5] |
||||
trades = [Trade(ticker, 100) for ticker in random.sample(positive_corr_tickers, min(3, len(positive_corr_tickers)))] |
||||
return trades |
||||
|
||||
def trade120(): |
||||
# Sell stocks that have shown a negative correlation with the market trend over the last 20 days |
||||
market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(20)] |
||||
market_trend = scipy.stats.linregress(range(20), market_total).slope |
||||
negative_corr_tickers = [ticker for ticker in tickers if scipy.stats.pearsonr(prices[ticker][:20], market_total)[0] < -0.5] |
||||
trades = [Trade(ticker, -100) for ticker in random.sample(negative_corr_tickers, min(3, len(negative_corr_tickers)))] |
||||
return trades |
Loading…
Reference in new issue