mirror of
https://github.com/wassname/options_backtester.git
synced 2026-08-05 13:10:21 +08:00
295 lines
11 KiB
Python
295 lines
11 KiB
Python
import pandas as pd
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import numpy as np
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import pyprind
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from .strategy import Strategy
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from .enums import Order, Stock
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from .datahandler import HistoricalOptionsData, TiingoData
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class Backtest:
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"""Processes signals from the Strategy object"""
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def __init__(self, allocation, initial_capital=1_000_000):
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assert isinstance(allocation, dict)
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assets = ('stocks', 'options', 'cash')
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total_allocation = sum(allocation.get(a, 0.0) for a in assets)
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self.allocation = {}
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for asset in assets:
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self.allocation[asset] = allocation.get(asset, 0.0) / total_allocation
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self.current_cash = self.initial_capital = initial_capital
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self.stop_if_broke = True
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self._stocks = []
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self._options_strategy = None
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self._stock_data = None
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self._options_data = None
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def add_stock(self, stock):
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"""Adds stock to the backtest"""
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assert isinstance(stock, Stock)
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self.stocks.append(stock)
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return self
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def add_stocks(self, stocks):
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"""Adds stocks to the backtest"""
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for stock in stocks:
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self.add_stock(stock)
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return self
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@property
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def strategy(self):
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return self._options_strategy
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@strategy.setter
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def strategy(self, strat):
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assert isinstance(strat, Strategy)
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self._options_strategy = strat
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self.current_cash = strat.initial_capital
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@property
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def stock_data(self):
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return self._stock_data
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@stock_data.setter
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def stock_data(self, data):
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assert isinstance(data, TiingoData)
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self._stock_data = data
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@property
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def options_data(self):
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return self._options_data
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@options_data.setter
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def options_data(self, data):
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assert isinstance(data, HistoricalOptionsData)
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self._options_data = data
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def run(self, rebalance_freq=0, monthly=False):
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"""Runs the backtest and returns a `pd.DataFrame` of the orders executed (`self.trade_log`)
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Args:
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rebalance_freq (int, optional): Determines the frequency of portfolio rebalances. Defaults to 0.
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monthly (bool, optional): Iterates through data monthly rather than daily. Defaults to False.
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Returns:
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pd.DataFrame: Log of the trades executed.
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"""
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assert self._stock_data, 'Stock data not set'
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assert self._options_data, 'Options data not set'
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assert self._options_strategy, 'Options Strategy not set'
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assert self._options_data.schema == self._options_strategy.schema
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option_dates = self._options_data['date'].unique()
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stock_dates = self._stock_data['date'].unique()
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assert np.array_equal(stock_dates, option_dates), 'Stock and options dates do not match'
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columns = pd.MultiIndex.from_product(
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[[l.name for l in self._options_strategy.legs],
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['contract', 'underlying', 'expiration', 'type', 'strike', 'cost', 'order']])
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totals = pd.MultiIndex.from_product([['totals'], ['cost', 'qty', 'date']])
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self.options_inventory = pd.DataFrame(columns=columns.append(totals))
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self.stock_inventory = pd.DataFrame(columns=['symbol', 'cost', 'qty'])
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rebalancing_days = pd.date_range(
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self.stock_data.first_date, self.stock_data.end_date, freq=str(rebalance_freq) +
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'BMS') if rebalance_freq else []
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self.trade_log = pd.DataFrame()
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self.balance = pd.DataFrame({
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'capital': self.current_cash,
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'cash': self.current_cash
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},
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index=[self.stock_data.start_date - pd.Timedelta(1, unit='day')])
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data_iterator = self._data_iterator(monthly)
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bar = pyprind.ProgBar(data_iterator.ngroups, bar_char='█')
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for date, stocks, options in data_iterator:
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if date == first_day:
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self._rebalance_portfolio(data, sma_days)
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self._update_balance(date, data)
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if date in rebalancing_days:
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self._rebalance_portfolio(data, sma_days)
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entry_signals = self._strategy.filter_entries(options, self.inventory, date)
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exit_signals = self._strategy.filter_exits(options, self.inventory, date)
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self._execute_exit(exit_signals)
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self._execute_entry(entry_signals)
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self._update_balance(date, options)
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bar.update()
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self.balance['% change'] = self.balance['capital'].pct_change()
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self.balance['accumulated return'] = (1.0 + self.balance['% change']).cumprod()
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return self.trade_log
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def _data_iterator(self, monthly):
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"""Returns combined iterator for stock and options data.
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Each step, it produces a tuple like the following:
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(date, stocks, options)
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Returns:
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generator: Daily/monthly iterator over `self.stock_data` and `self.options_data`
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"""
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if monthly:
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it = zip(self._stock_data.iter_months(), self._options_data.iter_months())
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else:
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it = zip(self._stock_data.iter_dates(), self._options_data.iter_dates())
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return ((date, stocks, options) for (date, stocks), (_, options) in it)
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def _execute_entry(self, entry_signals):
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"""Executes entry orders and updates `self.inventory` and `self.trade_log`"""
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entry, total_price = self._process_entry_signals(entry_signals)
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if (not self.stop_if_broke) or (self.current_cash >= total_price):
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self.inventory = self.inventory.append(entry, ignore_index=True)
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self.trade_log = self.trade_log.append(entry, ignore_index=True)
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self.current_cash -= total_price
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def _execute_exit(self, exit_signals):
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"""Executes exits and updates `self.inventory` and `self.trade_log`"""
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exits, exits_mask, total_costs = exit_signals
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self.trade_log = self.trade_log.append(exits, ignore_index=True)
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self.inventory.drop(self.inventory[exits_mask].index, inplace=True)
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self.current_cash -= sum(total_costs)
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def _process_entry_signals(self, entry_signals):
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"""Returns the entry signals to execute and their cost."""
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if not entry_signals.empty:
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# costs = entry_signals['totals']['cost']
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# return entry_signals.loc[costs.idxmin():costs.idxmin()], costs.min()
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entry = entry_signals.iloc[0]
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return entry, entry['totals']['cost'] * entry['totals']['qty']
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else:
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return entry_signals, 0
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def _update_balance(self, date, options):
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"""Updates positions and calculates statistics for the current date.
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Args:
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date (pd.Timestamp): Current date.
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options (pd.DataFrame): DataFrame of (daily/monthly) options.
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"""
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leg_candidates = [
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self._strategy._exit_candidates(l.direction, self.inventory[l.name], options, self.inventory.index)
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for l in self._strategy.legs
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]
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# If a contract is missing we replace the NaN values with those of the inventory
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# except for cost, which we imput as zero.
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for leg in leg_candidates:
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leg['cost'].fillna(0, inplace=True)
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calls_value = -np.sum(
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sum(leg['cost'] * self.inventory['totals']['qty']
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for leg in leg_candidates if (leg['type'] == 'call').any()))
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puts_value = -np.sum(
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sum(leg['cost'] * self.inventory['totals']['qty']
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for leg in leg_candidates if (leg['type'] == 'put').any()))
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capital = calls_value + puts_value + self.current_cash
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row = pd.Series(
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{
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'qty': self.inventory['totals']['qty'].sum(),
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'calls value': calls_value,
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'puts value': puts_value,
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'cash': self.current_cash,
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'capital': capital,
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},
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name=date)
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self.balance = self.balance.append(row)
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def summary(self):
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"""Returns a table with summary statistics about the trade log"""
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df = self.trade_log
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balance = self.balance
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df.loc[:,
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('totals',
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'capital')] = (-df['totals']['cost'] * df['totals']['qty']).cumsum() + self._strategy.initial_capital
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daily_returns = balance['% change'] * 100
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first_leg = self._strategy.legs[0].name
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entry_mask = df[first_leg].eval('(order == @Order.BTO) | (order == @Order.STO)')
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entries = df.loc[entry_mask]
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exits = df.loc[~entry_mask]
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costs = np.array([])
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for contract in entries[first_leg]['contract']:
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entry = entries.loc[entries[first_leg]['contract'] == contract]
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exit_ = exits.loc[exits[first_leg]['contract'] == contract]
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try:
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# Here we assume we are entering only once per contract (i.e both entry and exit_ have only one row)
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costs = np.append(costs, (entry['totals']['cost'] * entry['totals']['qty']).values[0] +
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(exit_['totals']['cost'] * exit_['totals']['qty']).values[0])
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except IndexError:
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continue
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# trades = entries.merge(exits,
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# on=[(l.name, 'contract') for l in self._strategy.legs],
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# suffixes=['_entry', '_exit'])
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# costs = trades.apply(lambda row: row['totals_entry']['cost'] + row['totals_exit']['cost'], axis=1)
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wins = costs < 0
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losses = costs >= 0
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profit_factor = np.sum(wins) / np.sum(losses)
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total_trades = len(exits)
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win_number = np.sum(wins)
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loss_number = total_trades - win_number
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win_pct = (win_number / total_trades) * 100
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largest_loss = max(0, np.max(costs))
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avg_profit = np.mean(-costs)
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avg_pl = np.mean(daily_returns)
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total_pl = (df['totals']['capital'].iloc[-1] / self._strategy.initial_capital) * 100
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data = [
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total_trades, win_number, loss_number, win_pct, largest_loss, profit_factor, avg_profit, avg_pl, total_pl
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]
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stats = [
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'Total trades', 'Number of wins', 'Number of losses', 'Win %', 'Largest loss', 'Profit factor',
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'Average profit', 'Average P&L %', 'Total P&L %'
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]
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strat = ['Strategy']
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summary = pd.DataFrame(data, stats, strat)
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# Applies formatters to rows
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def format_row_wise(styler, formatters):
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for row, row_formatter in formatters.items():
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row_num = styler.index.get_loc(row)
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for col_num in range(len(styler.columns)):
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styler._display_funcs[(row_num, col_num)] = row_formatter
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return styler
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formatters = {
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"Total trades": lambda x: f"{x:.0f}",
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"Number of wins": lambda x: f"{x:.0f}",
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"Number of losses": lambda x: f"{x:.0f}",
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"Win %": lambda x: f"{x:.2f}%",
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"Largest loss": lambda x: f"${x:.2f}",
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"Profit factor": lambda x: f"{x:.2f}",
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"Average profit": lambda x: f"${x:.2f}",
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"Average P&L %": lambda x: f"{x:.2f}%",
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"Total P&L %": lambda x: f"{x:.2f}%"
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}
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styler = format_row_wise(summary.style, formatters)
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return styler
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def __repr__(self):
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return "Backtest(capital={}, strategy={})".format(self.current_cash, self._strategy)
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