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113 lines
3.6 KiB
113 lines
3.6 KiB
import datetime
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import math
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import numpy as np
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import pandas as pd
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import matplotlib.pyplot as plt
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import backtrader as bt
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import strategies as st
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# import data
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def get_data(stocks, start, end):
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from pandas_datareader.yahoo.headers import DEFAULT_HEADERS
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from pandas_datareader import data as pdr
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import requests_cache
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expire_after = datetime.timedelta(days=1)
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session = requests_cache.CachedSession(
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cache_name="cache", backend="sqlite", expire_after=expire_after
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)
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session.headers = DEFAULT_HEADERS
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start = datetime.datetime.fromisoformat("1900-01-01")
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end = datetime.datetime.now()
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stockData = pdr.get_data_yahoo(stocks, start, end, session=session)
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return stockData
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def run(strategy, data, fund_mode=False):
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if fund_mode:
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cerebro = bt.Cerebro()
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cerebro.addobserver(bt.observers.FundShares)
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else:
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cerebro = bt.Cerebro(stdstats=False)
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cerebro.addobserver(bt.observers.Broker)
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cerebro.addobserver(bt.observers.BuySell)
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cerebro.adddata(data)
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cerebro.addstrategy(strategy)
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# Broker Information
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broker_args = dict(coc=True)
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cerebro.broker = bt.brokers.BackBroker(**broker_args)
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comminfo = st.PercentageCommisionScheme()
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cerebro.broker.addcommissioninfo(comminfo)
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if fund_mode:
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cerebro.broker.set_fundmode(True)
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cerebro.broker.set_cash(st.month_sum)
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cerebro.addanalyzer(bt.analyzers.DrawDown, _name="drawdown")
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cerebro.addanalyzer(bt.analyzers.VWR, _name="returns")
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cerebro.addanalyzer(
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bt.analyzers.TimeReturn, timeframe=bt.TimeFrame.NoTimeFrame, _name="tr"
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)
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thestrats = cerebro.run()
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thestrat = thestrats[0]
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# cerebro.plot(iplot=False, style="candlestick")
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return thestrat
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if __name__ == "__main__":
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df = pd.DataFrame(
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columns=[
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"froi",
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"cost",
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"total_value",
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"times",
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"units",
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"comms",
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"annual%",
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"max_dd_len",
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"max_dd",
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"max_md",
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"twr",
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"tr",
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]
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)
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stockList = ["^GSPC"]
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# startDate = datetime.datetime.fromisoformat("2000-01-01")
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# endDate = datetime.datetime.fromisoformat("2022-01-01")
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print(stockList[0])
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for period_years in (20, 50):
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endDate = datetime.datetime.now()
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startDate = endDate - datetime.timedelta(days=period_years * 365)
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stockData = get_data(stockList[0], startDate, endDate)
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actualStart: datetime.datetime = stockData.index[0]
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data = bt.feeds.PandasData(dataname=stockData)
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for i, strategy in enumerate(
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(st.SmaCross, st.DCA, st.QDCA, st.VA, st.QVA, st.SmaVA)
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):
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therun = run(strategy, data)
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dd = therun.analyzers.drawdown
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ret = therun.analyzers.returns
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tr = therun.analyzers.tr
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# print(next(reversed(tr.get_analysis())))
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params = therun.calc_params()
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annual = 100 * (
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(1 + params[0] / 100) ** (365 / (endDate - actualStart).days) - 1
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)
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df.loc[strategy.__name__] = therun.calc_params() + (
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annual,
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dd.get_analysis().max.len,
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dd.get_analysis().max.drawdown,
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dd.get_analysis().max.moneydown,
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ret.get_analysis()["vwr"],
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list(tr.get_analysis().items())[0][1],
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)
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print("Starting from:", actualStart)
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print("Time in Market: {:.1f} years".format((endDate - actualStart).days / 365))
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# print(df[["annual%", "froi", "cost", "total_value", "max_dd", "max_md"]])
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print(df)
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