def get_stats_from_strategy(strategy_type, train_ts, test_ts, commission_fee,
compound, rf, periods):
"""Calculate backtesting statistics for a specific strategy.
Args:
strategy_type: A specific strategy.
train_ts: Train data time series.
test_ts: Test data time series.
commission_fee: A number represents the commission fee.
compound: A bool variable indicating whehter calculates compound
return or not.
rf: Risk free rate.
periods: Scaling parameter to calculate annualized sharpe ratio.
Returns:
A summary statistics.
"""
if strategy_type == 'corpus':
strat = strats[strategy_type](train_ts, n)
else:
strat = strats[strategy_type]()
strat.get_vectorized_signals(test_ts)
# accept signal and produce position
port = EqualWeightPort()
port.get_position(strat.signals.iloc[(n - 1):])
# backtest
bt = Backtest(test_ts[port.positions.index], port.positions)
stats = bt.output_summary_stats(rf=rf,
periods=periods,
compound=compound,
commission_fee=commission_fee)
return stats
Created on Mon Apr 23 16:46:25 2018
@author: rl17174
"""
import os
os.chdir('/Users/sunrukawa/Desktop/python/qishi_qr/bin/')
from strategy import CorpusStrategy
from data import OHLCData
from portfolio import EqualWeightPort
from backtest import Backtest
n = 6
# process data
OHLC = OHLCData()
data_dir = '../data/OHLC data/000001.SS.csv'
OHLC.read(data_dir)
price_ts = OHLC.get_clean_price_ts('Adj Close')
train_ts = price_ts['1995':'2004']
test_ts = price_ts['2005':'2013']
# create strategy
strat = CorpusStrategy(train_ts, n)
strat.get_vectorized_signal(test_ts)
# accept signal and produce position
port = EqualWeightPort()
port.get_position(strat.signals.iloc[(n - 1):])
# backtest
my_bt = Backtest(test_ts[port.positions.index], port.positions)
stats = my_bt.output_summary_stats(rf=0.02)
