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backtest.py
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from abc import ABC, abstractmethod
import pandas as pd
import statistics
import numpy as np
class Strategy(ABC):
def __init__(self, stop_loss=-100, stop_gain=100):
self.stop_loss = stop_loss
self.stop_gain = stop_gain
@abstractmethod
def create_indicators(self, dataframe):
pass
@abstractmethod
def buy_entry(self, dataframe):
pass
@abstractmethod
def buy_exit(self, dataframe):
pass
@abstractmethod
def sell_entry(self, dataframe):
pass
@abstractmethod
def sell_exit(self, dataframe):
pass
class Position:
def __init__(self, hour, price_entry, position_type):
self.hour = hour
self.price_entry = price_entry
self.position_type = position_type
class Backtest:
def __init__(self, data, strategy):
self.data = data
self.data.columns = [
'Date',
'Open',
'High',
'Low',
'Close',
'Volume']
self.strategy = strategy
self.stop_loss = strategy.stop_loss
self.stop_gain = strategy.stop_gain
self.buy_trade = False
self.sell_trade = False
self.setted = None
self.trades_results = list()
self.trades_info = list()
self.init_date = '01-23-2010 00:00:00'
self.init_dataframe()
self.populate_signals(self.data)
def init_dataframe(self):
# ToDo - Remove this function
self.add_last_in_day()
self.data['buy_entry'] = 0
self.data['buy_exit'] = 0
self.data['sell_entry'] = 0
self.data['sell_exit'] = 0
def gain(self, type_trade, exit_price, regist=True):
gain = 0
if type_trade == 'buy':
gain = (exit_price - self.setted.price_entry) * 0.2
if regist:
self.trades_info.append({'type': 'buy', 'init': self.setted.price_entry,
'end': exit_price, 'date': self.setted.hour})
elif type_trade == 'sell':
gain = (exit_price - self.setted.price_entry) * -0.2
if regist:
self.trades_info.append({'type': 'sell', 'init': self.setted.price_entry,
'end': exit_price, 'date': self.setted.hour})
return gain
def calc_last(self):
all_df = []
for date in self.data['day'].unique():
dfc = self.data[self.data['day'] == date].copy(deep=True)
dfc['last'] = dfc['num_candle'] \
== np.argmax(dfc['num_candle'])
all_df.append(dfc)
self.data['last'] = pd.concat(all_df)['last']
def add_last_in_day(self):
self.data['day'] = self.data['Date'].apply(lambda x: x[:-6])
self.data['hour'] = self.data['Date'].apply(lambda x: x[-5:])
self.data['num_candle'] = self.data.groupby('day').cumcount()
self.calc_last()
def run_backtest(self):
valid_data = self.data[pd.to_datetime(self.data['Date']) >= self.init_date]
for (_, row) in valid_data.iterrows():
if row['last'] and self.setted or row['hour'] >= '17' \
and self.setted:
if self.buy_trade:
self.trades_results.append(self.gain(type_trade='buy', exit_price=row['Low']))
self.buy_trade = False
elif self.sell_trade:
self.trades_results.append(self.gain(type_trade='sell', exit_price=row['High']))
self.sell_trade = False
self.setted = False
elif self.setted and self.buy_trade \
and self.gain(type_trade='buy',
exit_price=row['Close'], regist=False) <= self.stop_loss:
self.trades_results.append(self.gain(type_trade='buy', exit_price=row['Close']))
self.buy_trade = False
self.setted = False
elif self.setted and self.sell_trade \
and self.gain(type_trade='sell',
exit_price=row['Close'], regist=False) <= self.stop_loss:
self.trades_results.append(self.gain(type_trade='buy', exit_price=row['Close']))
self.sell_trade = False
self.setted = False
elif self.setted and self.buy_trade \
and self.gain(type_trade='buy',
exit_price=row['Close'], regist=False) >= self.stop_gain:
self.trades_results.append(self.gain(type_trade='buy', exit_price=row['Close']))
self.buy_trade = False
self.setted = False
elif self.setted and self.sell_trade \
and self.gain(type_trade='sell',
exit_price=row['Close'], regist=False) >= self.stop_gain:
self.trades_results.append(self.gain(type_trade='buy', exit_price=row['Close']))
self.sell_trade = False
self.setted = False
elif not self.setted and row['buy_entry'] and row['hour'] < '17':
self.setted = Position(row['Date'], row['High'], 'buy')
self.buy_trade = True
elif self.setted and row['buy_exit']:
self.buy_trade = False
self.trades_results.append(self.gain(type_trade='buy', exit_price=row['Low']))
self.setted = None
elif not self.setted and row['sell_entry'] and row['hour'] < '17':
self.setted = Position(row['Date'], row['Low'], 'sell')
self.sell_trade = True
elif self.setted and row['sell_exit']:
self.sell_trade = False
self.trades_results.append(self.gain(type_trade='sell', exit_price=row['High']))
self.setted = None
return self.get_results()
def get_results(self):
all_trades = self.trades_results
gain = sum(self.trades_results)
mean = 0
try:
mean = statistics.mean(self.trades_results)
except Exception as ex:
raise ex
return {"all_trades_gain": all_trades, "total_gain": gain, "mean_gain": mean,
"trades_info": self.trades_info}
def populate_signals(self, dataframe):
self.data = self.strategy.create_indicators(dataframe)
self.data = self.strategy.buy_entry(dataframe)
self.data = self.strategy.buy_exit(dataframe)
self.data = self.strategy.sell_entry(dataframe)
self.data = self.strategy.sell_exit(dataframe)