# 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