From c52b1f99a6fa4194eb66c558a9baf8cc40f341fc Mon Sep 17 00:00:00 2001 From: Edward Donner Date: Thu, 21 Nov 2024 13:41:10 -0500 Subject: [PATCH] Some tidy-up and added extra project --- extras/trading/curator.ipynb | 352 +++++++++++++ extras/trading/trades_claude.py | 725 ++++++++++++++++++++++++++ extras/trading/trades_gemini.py | 534 +++++++++++++++++++ extras/trading/trades_gpt-4o.py | 884 ++++++++++++++++++++++++++++++++ week1/day2 EXERCISE.ipynb | 2 +- week1/day5.ipynb | 2 +- 6 files changed, 2497 insertions(+), 2 deletions(-) create mode 100644 extras/trading/curator.ipynb create mode 100644 extras/trading/trades_claude.py create mode 100644 extras/trading/trades_gemini.py create mode 100644 extras/trading/trades_gpt-4o.py diff --git a/extras/trading/curator.ipynb b/extras/trading/curator.ipynb new file mode 100644 index 0000000..19cad6c --- /dev/null +++ b/extras/trading/curator.ipynb @@ -0,0 +1,352 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "46d90d45-2d19-49c7-b853-6809dc417ea7", + "metadata": {}, + "source": [ + "# Extra Project - Trading Code Generator\n", + "\n", + "This is an example extra project to show fine-tuning in action, and applied to code generation.\n", + "\n", + "## Project Brief\n", + "\n", + "Build a prototype LLM that can generate example code to suggest trading decisions to buy or sell stocks!\n", + "\n", + "I generated test data using frontier models, in the other files in this directory. Use this to train an open source code model.\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

This project is provided as an extra resource

\n", + " It will make most sense after completing Week 7, and might trigger some ideas for your own projects.

\n", + " 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", + "
\n", + "
\n", + "\n", + " \n", + " \n", + " \n", + " \n", + "
\n", + " \n", + " \n", + "

Do not use for actual trading decisions!!

\n", + " It hopefully goes without saying: this project will generate toy trading code that is over-simplified and untrusted.

Please do not make actual trading decisions based on this!
\n", + "
" + ] + }, + { + "cell_type": "markdown", + "id": "7e2c4bbb-5e8b-4d84-9997-ecb2c349cf54", + "metadata": {}, + "source": [ + "## First step - generate training data from examples\n", + "\n", + "There are 3 sample python files generated (via multiple queries) by GPT-4o, Claude 3 Opus and Gemini 1.5 Pro. \n", + "\n", + "This notebook creates training data from these files, then converts to the HuggingFace format and uploads to the hub.\n", + "\n", + "Afterwards, we will move to Google Colab to fine-tune." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "16cf3aa2-f407-4b95-8b9e-c3c586f67835", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import glob\n", + "import matplotlib.pyplot as plt\n", + "import random\n", + "from datasets import Dataset\n", + "from dotenv import load_dotenv\n", + "from huggingface_hub import login\n", + "import transformers\n", + "from transformers import AutoTokenizer" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "375302b6-b6a7-46ea-a74c-c2400dbd8bbe", + "metadata": {}, + "outputs": [], + "source": [ + "# Load environment variables in a file called .env\n", + "from datasets import load_dataset, Dataset\n", + "load_dotenv()\n", + "hf_token = os.getenv('HF_TOKEN')\n", + "if hf_token and hf_token.startswith(\"hf_\") and len(hf_token)>5:\n", + " print(\"HuggingFace Token looks good so far\")\n", + "else:\n", + " print(\"Potential problem with HuggingFace token - please check your .env file, and see the Troubleshooting notebook for more\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a0c9fff-9eff-42fd-971b-403c99d9b726", + "metadata": {}, + "outputs": [], + "source": [ + "# Constants\n", + "\n", + "DATASET_NAME = \"trade_code_data\"\n", + "BASE_MODEL = \"Qwen/CodeQwen1.5-7B\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "586b07ba-5396-4c34-a696-01c8bc3597a0", + "metadata": {}, + "outputs": [], + "source": [ + "# A utility method to convert the text contents of a file into a list of methods\n", + "\n", + "def extract_method_bodies(text):\n", + " chunks = text.split('def trade')[1:]\n", + " results = []\n", + " for chunk in chunks:\n", + " lines = chunk.split('\\n')[1:]\n", + " body = '\\n'.join(line for line in lines if line!='\\n')\n", + " results.append(body)\n", + " return results " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "953422d0-2e75-4d01-862e-6383df54d9e5", + "metadata": {}, + "outputs": [], + "source": [ + "# Read all .py files and convert into training data\n", + "\n", + "bodies = []\n", + "for filename in glob.glob(\"*.py\"):\n", + " with open(filename, 'r', encoding='utf-8') as file:\n", + " content = file.read()\n", + " extracted = extract_method_bodies(content)\n", + " bodies += extracted\n", + "\n", + "print(f\"Extracted {len(bodies)} trade method bodies\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "836480e9-ba23-4aa3-a7e2-2666884e9a06", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's look at one\n", + "\n", + "print(random.choice(bodies))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "47b10e7e-a562-4968-af3f-254a9b424ac8", + "metadata": {}, + "outputs": [], + "source": [ + "# To visualize the lines of code in each \n", + "\n", + "%matplotlib inline\n", + "fig, ax = plt.subplots(1, 1)\n", + "lengths = [len(body.split('\\n')) for body in bodies]\n", + "ax.set_xlabel('Lines of code')\n", + "ax.set_ylabel('Count of training samples');\n", + "_ = ax.hist(lengths, rwidth=0.7, color=\"green\", bins=range(0, max(lengths)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "03b37f62-679e-4c3d-9e5b-5878a82696e6", + "metadata": {}, + "outputs": [], + "source": [ + "# Add the prompt to the start of every training example\n", + "\n", + "prompt = \"\"\"\n", + "# tickers is a list of stock tickers\n", + "import tickers\n", + "\n", + "# prices is a dict; the key is a ticker and the value is a list of historic prices, today first\n", + "import prices\n", + "\n", + "# Trade represents a decision to buy or sell a quantity of a ticker\n", + "import Trade\n", + "\n", + "import random\n", + "import numpy as np\n", + "\n", + "def trade():\n", + "\"\"\"\n", + "\n", + "data = [prompt + body for body in bodies]\n", + "print(random.choice(data))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28fdb82f-3864-4023-8263-547d17571a5c", + "metadata": {}, + "outputs": [], + "source": [ + "# Distribution of tokens in our dataset\n", + "\n", + "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n", + "tokenized_data = [tokenizer.encode(each) for each in data]\n", + "token_counts = [len(tokens) for tokens in tokenized_data]\n", + "\n", + "%matplotlib inline\n", + "fig, ax = plt.subplots(1, 1)\n", + "ax.set_xlabel('Number of tokens')\n", + "ax.set_ylabel('Count of training samples');\n", + "_ = ax.hist(token_counts, rwidth=0.7, color=\"purple\", bins=range(0, max(token_counts), 20))" + ] + }, + { + "cell_type": "markdown", + "id": "b4eb73b0-88ef-4aeb-8e5b-fe7050109ba0", + "metadata": {}, + "source": [ + "# Enforcing a maximum token length\n", + "\n", + "We need to specify a maximum number of tokens when we fine-tune.\n", + "\n", + "Let's pick a cut-off, and only keep training data points that fit within this number of tokens," + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ffb0d55c-5602-4518-b811-fa385c0959a7", + "metadata": {}, + "outputs": [], + "source": [ + "CUTOFF = 320\n", + "truncated = len([tokens for tokens in tokenized_data if len(tokens) > CUTOFF])\n", + "percentage = truncated/len(tokenized_data)*100\n", + "print(f\"With cutoff at {CUTOFF}, we truncate {truncated} datapoints which is {percentage:.1f}% of the dataset\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7064ef0a-7b07-4f24-a580-cbef2c5e1f2f", + "metadata": {}, + "outputs": [], + "source": [ + "# Let's only keep datapoints that wouldn't get truncated\n", + "\n", + "filtered_data = [datapoint for datapoint in data if len(tokenizer.encode(datapoint))<=CUTOFF]\n", + "print(f\"After e now have {len(filtered_data)} datapoints\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb2bb067-2bd3-498b-9fc8-5e8186afbe27", + "metadata": {}, + "outputs": [], + "source": [ + "# Mix up the data\n", + "\n", + "random.seed(42)\n", + "random.shuffle(filtered_data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26713fb9-765f-4524-b9db-447e97686d1a", + "metadata": {}, + "outputs": [], + "source": [ + "# I don't make a Training / Test split - if we had more training data, we would!\n", + "\n", + "dataset = Dataset.from_dict({'text':filtered_data})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bfabba27-ef47-46a8-a26b-4d650ae3b193", + "metadata": {}, + "outputs": [], + "source": [ + "login(hf_token)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "55b595cd-2df7-4be4-aec1-0667b17d36f1", + "metadata": {}, + "outputs": [], + "source": [ + "# Push your dataset to your hub\n", + "# I've also pushed the data to my account and made it public, which you can use from the colab below\n", + "\n", + "dataset.push_to_hub(DATASET_NAME, private=True)" + ] + }, + { + "cell_type": "markdown", + "id": "4691a025-9800-4e97-a20f-a65f102401f1", + "metadata": {}, + "source": [ + "## And now to head over to a Google Colab for fine-tuning in the cloud\n", + "\n", + "Follow this link for the Colab:\n", + "\n", + "https://colab.research.google.com/drive/1wry2-4AGw-U7K0LQ_jEgduoTQqVIvo1x?usp=sharing\n", + "\n", + "I've also saved this Colab with output included, so you can see the results without needing to train if you'd prefer.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "04a6c3e0-a2e6-4115-a01a-45e79dfdb730", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/extras/trading/trades_claude.py b/extras/trading/trades_claude.py new file mode 100644 index 0000000..8871c3b --- /dev/null +++ b/extras/trading/trades_claude.py @@ -0,0 +1,725 @@ +# 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 if the current price is lower than the average of the last 5 days + trades = [] + for ticker in tickers: + if prices[ticker][0] < np.mean(prices[ticker][1:6]): + quantity = random.randrange(1, 100) + trades.append(Trade(ticker, quantity)) + return trades + +def trade3(): + # Sell if the current price is higher than the average of the last 10 days + trades = [] + for ticker in tickers: + if prices[ticker][0] > np.mean(prices[ticker][1:11]): + quantity = random.randrange(-100, -1) + trades.append(Trade(ticker, quantity)) + return trades + +def trade4(): + # Buy if the current price is the lowest in the last 3 days + trades = [] + for ticker in tickers: + if prices[ticker][0] == min(prices[ticker][:3]): + quantity = random.randrange(1, 100) + trades.append(Trade(ticker, quantity)) + return trades + +def trade5(): + # Sell if the current price is the highest in the last 3 days + trades = [] + for ticker in tickers: + if prices[ticker][0] == max(prices[ticker][:3]): + quantity = random.randrange(-100, -1) + trades.append(Trade(ticker, quantity)) + return trades + +def trade6(): + # Buy if the current price is higher than the previous day's price + trades = [] + for ticker in tickers: + if prices[ticker][0] > prices[ticker][1]: + quantity = random.randrange(1, 100) + trades.append(Trade(ticker, quantity)) + return trades + +def trade7(): + # Sell if the current price is lower than the previous day's price + trades = [] + for ticker in tickers: + if prices[ticker][0] < prices[ticker][1]: + quantity = random.randrange(-100, -1) + trades.append(Trade(ticker, quantity)) + return trades + +def trade8(): + # Buy if the current price is higher than the average of the last 20 days + trades = [] + for ticker in tickers: + if prices[ticker][0] > np.mean(prices[ticker][1:21]): + quantity = random.randrange(1, 100) + trades.append(Trade(ticker, quantity)) + return trades + +def trade9(): + # Sell if the current price is lower than the average of the last 20 days + trades = [] + for ticker in tickers: + if prices[ticker][0] < np.mean(prices[ticker][1:21]): + quantity = random.randrange(-100, -1) + trades.append(Trade(ticker, quantity)) + return trades + +def trade10(): + # Buy if the current price is higher than the highest price in the last 5 days + trades = [] + for ticker in tickers: + if prices[ticker][0] > max(prices[ticker][1:6]): + quantity = random.randrange(1, 100) + trades.append(Trade(ticker, quantity)) + return trades + +def trade11(): + # Sell if the current price is lower than the lowest price in the last 5 days + trades = [] + for ticker in tickers: + if prices[ticker][0] < min(prices[ticker][1:6]): + quantity = random.randrange(-100, -1) + trades.append(Trade(ticker, quantity)) + return trades + +def trade12(): + # Long/Short: Buy the best-performing stock and sell the worst-performing stock in the last 10 days + best_ticker = max(tickers, key=lambda x: (prices[x][0] - prices[x][9]) / prices[x][9]) + worst_ticker = min(tickers, key=lambda x: (prices[x][0] - prices[x][9]) / prices[x][9]) + return [Trade(best_ticker, 100), Trade(worst_ticker, -100)] + +def trade13(): + # Buy if the 5-day moving average crosses above the 20-day moving average + trades = [] + for ticker in tickers: + if np.mean(prices[ticker][:5]) > np.mean(prices[ticker][:20]) and np.mean(prices[ticker][1:6]) <= np.mean(prices[ticker][1:21]): + quantity = random.randrange(1, 100) + trades.append(Trade(ticker, quantity)) + return trades + +def trade14(): + # Sell if the 5-day moving average crosses below the 20-day moving average + trades = [] + for ticker in tickers: + if np.mean(prices[ticker][:5]) < np.mean(prices[ticker][:20]) and np.mean(prices[ticker][1:6]) >= np.mean(prices[ticker][1:21]): + quantity = random.randrange(-100, -1) + trades.append(Trade(ticker, quantity)) + return trades + +def trade15(): + # Buy if the current volume is higher 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(1, 100) + trades.append(Trade(ticker, quantity)) + return trades + +def trade16(): + # 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 \ No newline at end of file diff --git a/extras/trading/trades_gemini.py b/extras/trading/trades_gemini.py new file mode 100644 index 0000000..9254bb8 --- /dev/null +++ b/extras/trading/trades_gemini.py @@ -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 \ No newline at end of file diff --git a/extras/trading/trades_gpt-4o.py b/extras/trading/trades_gpt-4o.py new file mode 100644 index 0000000..8f16580 --- /dev/null +++ b/extras/trading/trades_gpt-4o.py @@ -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 + market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(15)] + market_trend = market_total[0] < market_total[-1] + decreasing_tickers = [ticker for ticker in tickers if prices[ticker][0] < prices[ticker][14] and market_trend] + trades = [Trade(ticker, -100) for ticker in random.sample(decreasing_tickers, min(3, len(decreasing_tickers)))] + return trades + +def trade109(): + # Buy stocks that have broken above their previous 10-day high while the market is flat + market_total = [sum(prices[ticker][i] for ticker in tickers) for i in range(10)] + market_flat = abs((market_total[0] - market_total[-1]) / market_total[-1]) < 0.01 + 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] and market_flat] + trades = [Trade(ticker, 100) for ticker in random.sample(breakout_tickers, min(3, len(breakout_tickers)))] + return trades + +def trade110(): + # 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)] + market_flat = abs((market_total[0] - market_total[-1]) / market_total[-1]) < 0.01 + 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] and market_flat] + trades = [Trade(ticker, -100) for ticker in random.sample(breakdown_tickers, min(3, len(breakdown_tickers)))] + return trades + +def trade111(): + # Buy stocks that have shown a higher positive 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)] + market_return = (market_total[0] - market_total[-1]) / market_total[-1] + higher_positive_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_positive_tickers, min(3, len(higher_positive_tickers)))] + return trades + +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)] + 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 \ No newline at end of file diff --git a/week1/day2 EXERCISE.ipynb b/week1/day2 EXERCISE.ipynb index 75ac44e..6bdbde5 100644 --- a/week1/day2 EXERCISE.ipynb +++ b/week1/day2 EXERCISE.ipynb @@ -185,7 +185,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.10" } }, "nbformat": 4, diff --git a/week1/day5.ipynb b/week1/day5.ipynb index 1f42d40..037e278 100644 --- a/week1/day5.ipynb +++ b/week1/day5.ipynb @@ -473,7 +473,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.3" + "version": "3.11.10" } }, "nbformat": 4,