110 lines
3.3 KiB
Python
110 lines
3.3 KiB
Python
import pandas as pd
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import datetime as dt
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import matplotlib.pyplot as plt
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import TheoreticallyOptimalStrategy as tos
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from util import plot_data, get_data
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from marketsim.marketsim import compute_portvals
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from optimize_something.optimization import calculate_stats
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def author():
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return "felixm"
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def test_optimal_strategy():
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symbol = "JPM"
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start_value = 100000
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sd = dt.datetime(2008, 1, 1)
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ed = dt.datetime(2009, 12, 31)
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orders = tos.testPolicy(symbol=symbol, sd=sd, ed=ed, sv=start_value)
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portvals = compute_portvals(orders, start_value, 0, 0)
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start_date = portvals.index[0]
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end_date = portvals.index[-1]
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cum_ret, avg_daily_ret, \
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std_daily_ret, sharpe_ratio = calculate_stats(portvals, [1])
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d = {"Symbol": [symbol, symbol],
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"Order": ["BUY", "SELL"],
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"Shares": [1000, 1000]}
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orders = pd.DataFrame(data=d, index=[start_date, end_date])
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bench = compute_portvals(orders, start_value, 0, 0)
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cum_ret_bench, avg_daily_ret_bench, \
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std_daily_ret_bench, sharpe_ratio_bench = calculate_stats(bench, [1])
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# Compare portfolio against benchmark
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print(f"Date Range: {start_date} to {end_date}")
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print()
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print(f"Sharpe Ratio of Optimal Strategy: {sharpe_ratio}")
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print(f"Sharpe Ratio of bench: {sharpe_ratio_bench}")
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print()
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print(f"Cumulative Return of Optimal Strategy: {cum_ret}")
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print(f"Cumulative Return of bench: {cum_ret_bench}")
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print()
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print(f"Standard Deviation of Optimal Strategy: {std_daily_ret}")
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print(f"Standard Deviation of bench: {std_daily_ret_bench}")
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print()
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print(f"Average Daily Return of Optimal Strategy: {avg_daily_ret}")
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print(f"Average Daily Return of bench: {avg_daily_ret_bench}")
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print()
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print(f"Final Portfolio Value Optimal: {portvals.iloc[-1][0]}")
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print(f"Final Portfolio Value Bench: {bench.iloc[-1][0]}")
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portvals["Optimal"] = portvals["Portval"]
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portvals["Bench"] = bench["Portval"]
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portvals.drop(columns=["Portval"], inplace=True)
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portvals.plot(title="Optimal strategy versus 1000 shares of JPM")
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plt.savefig('figure_5.png')
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def normalize(timeseries):
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return timeseries / timeseries.iloc[0]
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def bollinger_band(df, symbol, period=20, m=2):
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boll_sma = df[symbol].rolling(period).mean()
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std = df[symbol].rolling(period).std()
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boll_up = boll_sma + m * std
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boll_lo = boll_sma - m * std
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df[f"Boll({symbol}, {period})-sma"] = boll_sma
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df[f"Boll({symbol}, {period})-up"] = boll_up
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df[f"Boll({symbol}, {period})-lo"] = boll_lo
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def sma(df, symbol, period):
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"""Adds a new column to the dataframe SMA(period)"""
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df[f"{symbol}-sma({period})"] = df[symbol].rolling(period).mean()
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def price_sma(df, symbol, period):
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"""Calculates SMA and adds new column price divided by SMA to the df."""
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sma = df[symbol].rolling(period).mean()
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df[f"{symbol}-price/sma({period})"] = df[symbol] / sma
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def main():
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# test_optimal_strategy()
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symbol = "JPM"
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sd = dt.datetime(2008, 1, 1)
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ed = dt.datetime(2009, 12, 31)
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df = get_data([symbol], pd.date_range(sd, ed))
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df.drop(columns=["SPY"], inplace=True)
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df = normalize(df)
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sma(df, symbol, 8)
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# price_sma(df, symbol, 8)
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# bollinger_band(df, symbol)
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# TODO
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# rsi(df, symbol)
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# macd(df, symbol)
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plot_data(df)
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if __name__ == "__main__":
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main()
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