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letianzjletianzj
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backtest strats
some ta backtests
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‎.gitignore

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# C extensions
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*.so
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# data
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*.csv
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*.xlsx
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# pycharm
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*.xml
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*.iml
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# Distribution / packaging
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.Python
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build/

‎.idea/.gitignore

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‎backtest/README.md

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# Backtest
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|Index |Backtest |
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|----:|:---------------------------------------------------------------------------------|-----------:|
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|1 | [Buy and Hold](./buy_hold.py) |
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|2 | [Moving Average cross](./ma_cross.py) |
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|3 | [Moving Average double cross](./ma_double_cross.py) |
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```python
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```

‎backtest/__init__.py

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#!/usr/bin/env python
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# -*- coding: utf-8 -*-

‎backtest/buy_hold.py

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import os
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import pandas as pd
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from datetime import datetime
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import backtrader as bt
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class BuyAndHold(bt.Strategy):
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def __init__(self):
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# To keep track of pending orders and buy price/commission
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self.order = None
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def log(self, txt, dt=None):
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''' Logging function fot this strategy'''
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dt = dt or self.datas[0].datetime.date(0)
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print('%s, %s' % (dt.isoformat(), txt))
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def start(self):
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self.val_start = self.broker.get_cash() # keep the starting cash
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def nextstart(self):
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# Buy all the available cash
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# self.order_target_value(target=self.broker.get_cash())
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#size = int(self.broker.get_cash() / self.data)
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#self.order = self.buy(size=size)
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self.buy()
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def notify_trade(self, trade):
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if not trade.isclosed:
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return
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self.log('OPERATION PROFIT, GROSS %.2f, NET %.2f' % (trade.pnl, trade.pnlcomm))
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def notify_order(self, order):
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if order.status in [order.Submitted, order.Accepted]:
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# Buy/Sell order submitted/accepted to/by broker - Nothing to do
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return
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# Check if an order has been completed
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# Attention: broker could reject order if not enough cash
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if order.status in [order.Completed]:
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if order.isbuy():
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self.log(
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'BUY EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
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(order.executed.price,
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order.executed.value,
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order.executed.comm))
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self.buyprice = order.executed.price
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self.buycomm = order.executed.comm
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else: # Sell
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self.log('SELL EXECUTED, Price: %.2f, Cost: %.2f, Comm %.2f' %
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(order.executed.price,
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order.executed.value,
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order.executed.comm))
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self.bar_executed = len(self)
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def next(self):
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# Simply log the closing price of the series from the reference
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# self.log('Close, %.2f' % self.data.close[0])
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pass
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def stop(self):
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# calculate the actual returns
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self.roi = (self.broker.get_value() / self.val_start) - 1.0
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print('ROI: {:.2f}%'.format(100.0 * self.roi))
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if __name__ == '__main__':
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# Create a cerebro entitysetsizing
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cerebro = bt.Cerebro()
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datapath = os.path.join('../data/', 'AAPL.csv')
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# Create a Data Feed
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data = bt.feeds.YahooFinanceCSVData(
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dataname=datapath,
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# Do not pass values before this date
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fromdate=datetime(2010, 1, 1),
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# Do not pass values before this date
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todate=datetime(2015, 12, 31),
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# Do not pass values after this date
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reverse=False)
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# Add the Data Feed to Cerebro
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cerebro.adddata(data)
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# Set our desired cash start
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cerebro.broker.setcash(100000.0)
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# Add a FixedSize sizer according to the stake
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# cerebro.addsizer(bt.sizers.FixedSize, stake=10)
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cerebro.addsizer(bt.sizers.PercentSizer, percents=95)
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# Set the commission - 0.1% ... divide by 100 to remove the %
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cerebro.broker.setcommission(commission=0.001)
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# Print out the starting conditions
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print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
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# Add Analyzer
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cerebro.addanalyzer(bt.analyzers.PyFolio, _name='pyfolio')
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# Add a strategy
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cerebro.addstrategy(BuyAndHold)
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# Run over everything
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results = cerebro.run()
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# Print out the final result
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print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
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cerebro.plot(style='candlestick')
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strat = results[0]
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pyfoliozer = strat.analyzers.getbyname('pyfolio')
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returns, positions, transactions, gross_lev = pyfoliozer.get_pf_items()
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print('-------------- RETURNS ----------------')
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print(returns)
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print('-------------- POSITIONS ----------------')
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print(positions)
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print('-------------- TRANSACTIONS ----------------')
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print(transactions)
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print('-------------- GROSS LEVERAGE ----------------')
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print(gross_lev)
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import empyrical as ep
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import pyfolio as pf
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import matplotlib.pyplot as plt
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perf_stats_strat = pf.timeseries.perf_stats(returns)
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drawdown_table = pf.timeseries.gen_drawdown_table(returns, 5)
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monthly_ret_table = ep.aggregate_returns(returns, 'monthly')
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monthly_ret_table = monthly_ret_table.unstack().round(3)
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ann_ret_df = pd.DataFrame(ep.aggregate_returns(returns, 'yearly'))
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ann_ret_df = ann_ret_df.unstack().round(3)
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print('-------------- PERFORMANCE ----------------')
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print(perf_stats_strat)
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print('-------------- DRAWDOWN ----------------')
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print(drawdown_table)
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print('-------------- MONTHLY RETURN ----------------')
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print(monthly_ret_table)
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print('-------------- ANNUAL RETURN ----------------')
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print(ann_ret_df)
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pf.create_full_tear_sheet(
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returns,
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positions=positions,
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transactions=transactions,
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#live_start_date='2005-05-01',
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round_trips=False)
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plt.show()

‎backtest/hist_downloader.py

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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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import os
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import argparse
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from datetime import datetime, timedelta
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import pandas as pd
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import numpy as np
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import pandas_datareader.data as web
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# import yfinance as yf
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def save(df, fn):
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df = df[['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']]
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df.to_csv(fn)
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def run(args):
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current_path = os.path.dirname(os.path.abspath(__file__))
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hist_path = os.path.join(current_path, '..\data')
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end_date = datetime.today()
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#start_date = end_date + timedelta(days=-5 * 365)
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start_date = datetime(2006, 1, 1)
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if args.index:
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print('Index downloading .............')
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data = web.DataReader(name='^GSPC', data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'SPX.csv'))
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data = web.DataReader(name='^DJI', data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'DJI.csv'))
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data = web.DataReader(name='^NDX', data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'NDX.csv'))
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data = web.DataReader(name='^RUT', data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'RUT.csv'))
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print('Index downloaded')
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if args.dow:
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print('Dow30 downloading .............')
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df = pd.read_csv(os.path.join(hist_path, 'dow30.csv'), header=None)
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for idx, row in df.iterrows():
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try:
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data = web.DataReader(name=row[0], data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'{row[0]}.csv'))
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except Exception as e:
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print(f'{row[0]} failed. {str(e)}')
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print('Dow30 downloaded')
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if args.sector:
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print('Sector ETF downloading .............')
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df = pd.read_csv(os.path.join(hist_path, 'sectoretf.csv'), header=None)
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for idx, row in df.iterrows():
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try:
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data = web.DataReader(name=row[0], data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'{row[0]}.csv'))
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except Exception as e:
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print(f'{row[0]} failed. {str(e)}')
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print('Sector ETF downloaded')
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if args.country:
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print('Country ETF downloading .............')
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df = pd.read_csv(os.path.join(hist_path, 'countryetf.csv'), header=None)
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for idx, row in df.iterrows():
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try:
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data = web.DataReader(name=row[0], data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'{row[0]}.csv'))
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except Exception as e:
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print(f'{row[0]} failed. {str(e)}')
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print('Country ETF downloaded')
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if args.taa:
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print('Mebane Faber TAA downloading .............')
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symbols = ['SPY', 'EFA', 'AGG', 'VNQ', 'GLD'] # sp, em, bond, real estate, gold
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for sym in symbols:
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try:
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data = web.DataReader(name=sym, data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'{sym}.csv'))
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except Exception as e:
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print(f'{sym} failed. {str(e)}')
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print('Mebane Faber TAA downloaded')
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if args.sym:
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print(f'{args.sym} downloading .............')
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symbols = args.sym.split('+')
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for sym in symbols:
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try:
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data = web.DataReader(name=sym, data_source='yahoo', start=start_date, end=end_date)
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save(data, os.path.join(hist_path, f'{sym}.csv'))
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except Exception as e:
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print(f'{sym} failed. {str(e)}')
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print(f'{args.sym} downloaded')
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description='Historical Downloader')
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parser.add_argument('--index', action='store_true')
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parser.add_argument('--dow', action='store_true')
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parser.add_argument('--sector', action='store_true')
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parser.add_argument('--country', action='store_true')
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parser.add_argument('--taa', action='store_true')
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parser.add_argument('--sym', help='symbol')
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args = parser.parse_args()
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run(args)

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