Source code for pyqstrat.strategy


# coding: utf-8

# In[1]:


import numpy as np
import pandas as pd
import sys
from collections import defaultdict
from pprint import pformat
import math

from pyqstrat.pq_utils import *
from pyqstrat.marketdata import *
from pyqstrat.evaluator import compute_return_metrics, display_return_metrics, plot_return_metrics
from pyqstrat.account import Account


# In[2]:


[docs]class Strategy:
[docs] def __init__(self, contracts, marketdata_collection, starting_equity = 1.0e6, calc_frequency = 'D', additional_order_dates = None, additional_trade_dates = None): ''' Args: contracts (list of Contract): The contracts we will potentially trade starting_equity (float, optional): Starting equity in Strategy currency. Default 1.e6 calc_frequency (str, optional): How often P&L is calculated. Default is 'D' for daily additional_account_dates (np.array of np.datetime64, optional): If present, we check for orders on these dates. Default None additional_tradedates (np.array of np.datetime64, optional): If present, we check for trades on these dates. Default None ''' self.name = None date_list = [] if additional_order_dates is not None: date_list.append(additional_order_dates) if additional_trade_dates is not None: date_list.append(additional_trade_dates) self.additional_order_dates = additional_order_dates self.additional_trade_dates = additional_trade_dates if len(date_list): marketdata_collection.add_dates(np.concatenate(date_list)) self.dates = marketdata_collection.dates() self.account = Account(contracts, marketdata_collection, starting_equity, calc_frequency) self.symbols = [contract.symbol for contract in contracts] self.indicators = {} self.indicator_values = defaultdict(dict) self.signals = {} self.signal_values = defaultdict(dict) self.rules = {} self.rule_signals = {} self.market_sims = {} self._trades = defaultdict(list) self._orders = []
[docs] def add_indicator(self, name, indicator_function): ''' Args: name: Name of the indicator indicator_function: A function taking a MarketData object and returning a numpy array containing indicator values. The return array must have the same length as the MarketData object ''' self.indicators[name] = indicator_function
[docs] def add_signal(self, name, signal_function): ''' Args: name: Name of the signal signal_function: A function taking a MarketData object and a dictionary of indicator value arrays as input and returning a numpy array containing signal values. The return array must have the same length as the MarketData object ''' self.signals[name] = signal_function
[docs] def add_rule(self, name, rule_function, signal_name, sig_true_values = None): '''Add a trading rule Args: name (str): Name of the trading rule rule_function (function): A trading rule function that returns a list of Orders signal_name (str): The strategy will call the trading rule function when the signal with this name matches sig_true_values sig_true_values (numpy array, optional): If the signal value at a bar is equal to one of these values, the Strategy will call the trading rule function. Default [TRUE] ''' if sig_true_values is None: sig_true_values = [True] self.rule_signals[name] = (signal_name, sig_true_values) self.rules[name] = rule_function
[docs] def add_market_sim(self, market_sim_function, symbols = None): '''Add a market simulator. A market simulator takes a list of Orders as input and returns a list of Trade objects. Args: market_sim_function: A function that takes a list of Orders and MarketData as input and returns a list of Trade objects symbols: A list of the symbols that this market_sim_function applies to. If None (default) it will apply to all symbols ''' if symbols is None: symbols = self.symbols for symbol in symbols: self.market_sims[symbol] = market_sim_function
[docs] def run_indicators(self, indicator_names = None, symbols = None): '''Calculate values of the indicators specified and store them. Args: indicator_names: List of indicator names. If None (default) run all indicators symbols: List of symbols to run these indicators for. If None (default) use all symbols ''' if indicator_names is None: indicator_names = self.indicators.keys() if symbols is None: symbols = self.symbols for indicator_name in indicator_names: indicator_function = self.indicators[indicator_name] for symbol in symbols: marketdata = self.account.marketdata[symbol] self.indicator_values[symbol][indicator_name] = series_to_array(indicator_function(marketdata))
[docs] def run_signals(self, signal_names = None, symbols = None): '''Calculate values of the signals specified and store them. Args: signal_names: List of signal names. If None (default) run all signals symbols: List of symbols to run these signals for. If None (default) use all symbols ''' if signal_names is None: signal_names = self.signals.keys() if symbols is None: symbols = self.symbols for signal_name in signal_names: signal_function = self.signals[signal_name] for symbol in symbols: marketdata = self.account.marketdata[symbol] self.signal_values[symbol][signal_name] = series_to_array(signal_function(marketdata, self.indicator_values[symbol]))
[docs] def run_rules(self, rule_names = None, symbols = None, start_date = None, end_date = None): '''Run trading rules. Args: rule_names: List of rule names. If None (default) run all rules symbols: List of symbols to run these signals for. If None (default) use all symbols start_date: Run rules starting from this date. Default None end_date: Don't run rules after this date. Default None ''' start_date, end_date = str2date(start_date), str2date(end_date) dates, orders_iter, trades_iter = self._get_iteration_indices(rule_names, symbols, start_date, end_date) # Now we know which rules, symbols need to be applied for each iteration, go through each iteration and apply them # in the same order they were added to the strategy for i, tup_list in enumerate(orders_iter): self._check_for_trades(i, trades_iter[i]) self._check_for_orders(i, tup_list)
def _get_iteration_indices(self, rule_names = None, symbols = None, start_date = None, end_date = None): ''' >>> class MockStrat: ... def __init__(self): ... self.dates = dates ... self.account = self ... self.additional_order_dates = None ... self.additional_trade_dates = np.array(['2018-01-03'], dtype = 'M8[D]') ... self.rules = {'rule_a' : rule_a, 'rule_b' : rule_b} ... self.marketdata = {'IBM' : self, 'AAPL' : self} ... self.market_sims = {'IBM' : market_sim_ibm, 'AAPL' : market_sim_aapl} ... self.rule_signals = {'rule_a' : ('sig_a', [1]), 'rule_b' : ('sig_b', [1, -1])} ... self.signal_values = {'IBM' : {'sig_a' : np.array([0., 1., 1.]), 'sig_b' : np.array([0., 0., 0.])}, ... 'AAPL' : {'sig_a' : np.array([0., 0., 0.]), 'sig_b' : np.array([0., -1., -1])}} ... self.indicator_values = {'IBM' : None, 'AAPL' : None} >>> >>> def market_sim_aapl(): pass >>> def market_sim_ibm(): pass >>> def rule_a(): pass >>> def rule_b(): pass >>> dates = np.array(['2018-01-01', '2018-01-02', '2018-01-03'], dtype = 'M8[D]') >>> rule_names = ['rule_a', 'rule_b'] >>> symbols = ['IBM', 'AAPL'] >>> start_date = np.datetime64('2018-01-01') >>> end_date = np.datetime64('2018-02-05') >>> dates, orders_iter, trades_iter = Strategy._get_iteration_indices(MockStrat(), rule_names, symbols, start_date, end_date) >>> assert(len(trades_iter[1]) == 0) >>> assert(trades_iter[2][1][1] == "AAPL") >>> assert(trades_iter[2][2][1] == "IBM") >>> assert(len(orders_iter[0]) == 0) >>> assert(len(orders_iter[1]) == 2) >>> assert(orders_iter[1][0][1] == "IBM") >>> assert(orders_iter[1][1][1] == "AAPL") >>> assert(len(orders_iter[2]) == 0) ''' start_date, end_date = str2date(start_date), str2date(end_date) if rule_names is None: rule_names = self.rules.keys() if symbols is None: symbols = self.symbols num_dates = len(self.dates) orders_iter = [[] for x in range(num_dates)] trades_iter = [[] for x in range(num_dates)] for rule_name in rule_names: rule_function = self.rules[rule_name] for symbol in symbols: marketdata = self.account.marketdata[symbol] market_sim = self.market_sims[symbol] signal_name = self.rule_signals[rule_name][0] sig_true_values = self.rule_signals[rule_name][1] sig_values = self.signal_values[symbol][signal_name] dates = marketdata.dates null_value = False if sig_values.dtype == np.dtype('bool') else np.nan if start_date: sig_values[0:np.searchsorted(dates, start_date)] = null_value if end_date: sig_values[np.searchsorted(dates, end_date):] = null_value indices = np.nonzero(np.isin(sig_values, sig_true_values))[0] if self.additional_order_dates is not None: additional_indices = np.searchsorted(self.dates, self.additional_order_dates) indices = np.sort(np.unique(np.concatenate([indices, additional_indices]))) if len(indices) and indices[-1] == len(sig_values) -1: indices = indices[:-1] # Don't run rules on last index since we cannot fill any orders indicator_values = self.indicator_values[symbol] iteration_params = {'market_sim' : market_sim, 'indicator_values' : indicator_values, 'signal_values' : sig_values, 'marketdata' : marketdata} for idx in indices: orders_iter[idx].append((rule_function, symbol, iteration_params)) if self.additional_trade_dates is not None: trade_indices = np.sort(np.unique(np.searchsorted(self.dates, self.additional_trade_dates))) for idx in trade_indices: trades_iter[idx].append(([], symbol, iteration_params)) self.orders_iter = orders_iter self.trades_iter = trades_iter # For debugging return self.dates, orders_iter, trades_iter def _check_for_trades(self, i, tup_list): for tup in tup_list: try: open_orders, symbol, params = tup open_orders = self._sim_market(i, open_orders, symbol, params) if len(open_orders): self.trades_iter[i + 1].append((open_orders, symbol, params)) except Exception as e: raise type(e)(f'Exception: {str(e)} at rule: {type(tup[0])} symbol: {tup[1]} index: {i}').with_traceback(sys.exc_info()[2]) def _check_for_orders(self, i, tup_list): for tup in tup_list: try: rule_function, symbol, params = tup open_orders = self._get_orders(i, rule_function, symbol, params) self._orders += open_orders if len(open_orders): self.trades_iter[i + 1].append((open_orders, symbol, params)) except Exception as e: raise type(e)(f'Exception: {str(e)} at rule: {type(tup[0])} symbol: {tup[1]} index: {i}').with_traceback(sys.exc_info()[2]) def _get_orders(self, idx, rule_function, symbol, params): indicator_values, signal_values, marketdata = (params['indicator_values'], params['signal_values'], params['marketdata']) open_orders = rule_function(self, symbol, idx, self.dates[idx], marketdata, indicator_values, signal_values, self.account) return open_orders def _sim_market(self, idx, open_orders, symbol, params): ''' Keep iterating while we have open orders since they may get filled TODO: For limit orders and trigger orders we can be smarter here and reduce indices like quantstrat does ''' market_sim_function = params['market_sim'] trades = market_sim_function(self, open_orders, idx, self.dates[idx], self.account.marketdata[symbol]) if len(trades) == 0: return [] self._trades[symbol] += trades self.account._add_trades(symbol, trades) self.account.calc(idx) open_orders = [order for order in open_orders if order.status == 'open'] return open_orders
[docs] def df_data(self, symbols = None, add_pnl = True, start_date = None, end_date = None): ''' Add indicators and signals to end of market data and return as a pandas dataframe. Args: symbols: list of symbols to include. All if set to None (default) add_pnl: If True (default), include P&L columns in dataframe start_date: string or numpy datetime64. Default None end_date: string or numpy datetime64: Default None ''' start_date, end_date = str2date(start_date), str2date(end_date) if symbols is None: symbols = self.symbols if not isinstance(symbols, list): symbols = [symbols] mds = [] for symbol in symbols: md = self.account.marketdata[symbol].df(start_date, end_date) md.insert(0, 'symbol', symbol) if add_pnl: df_pnl = self.account.df_pnl(symbol) del df_pnl['symbol'] indicator_values = self.indicator_values[symbol] for k in sorted(indicator_values.keys()): name = k if name in md.columns: name = name + '.ind' # if we have a market data column with the same name as the indicator md.insert(len(md.columns), name, indicator_values[k]) signal_values = self.signal_values[symbol] for k in sorted(signal_values.keys()): name = k if name in md.columns: name = name + '.sig' md.insert(len(md.columns), name, signal_values[k]) if add_pnl: md = pd.merge(md, df_pnl, left_index = True, right_index = True, how = 'left') # Add counter column for debugging md.insert(len(md.columns), 'i', np.arange(len(md))) mds.append(md) return pd.concat(mds)
[docs] def marketdata(self, symbol): '''Return MarketData object for this symbol''' return self.account.marketdata[symbol]
[docs] def trades(self, symbol = None, start_date = None, end_date = None): '''Returns a list of trades with the given symbol and with trade date between (and including) start date and end date if they are specified. If symbol is None trades for all symbols are returned''' start_date, end_date = str2date(start_date), str2date(end_date) return self.account.trades(symbol, start_date, end_date)
[docs] def df_trades(self, symbol = None, start_date = None, end_date = None): '''Returns a dataframe with data from trades with the given symbol and with trade date between (and including) start date and end date if they are specified. If symbol is None, trades for all symbols are returned''' start_date, end_date = str2date(start_date), str2date(end_date) return self.account.df_trades(symbol, start_date, end_date)
[docs] def orders(self, symbol = None, start_date = None, end_date = None): '''Returns a list of orders with the given symbol and with order date between (and including) start date and end date if they are specified. If symbol is None orders for all symbols are returned''' start_date, end_date = str2date(start_date), str2date(end_date) return [order for order in self._orders if (symbol is None or order.symbol == symbol) and ( start_date is None or order.date >= start_date) and (end_date is None or order.date <= end_date)]
[docs] def df_orders(self, symbol = None, start_date = None, end_date = None): '''Returns a dataframe with data from orders with the given symbol and with order date between (and including) start date and end date if they are specified. If symbol is None, orders for all symbols are returned''' start_date, end_date = str2date(start_date), str2date(end_date) orders = self.orders(symbol, start_date, end_date) df_orders = pd.DataFrame.from_records([(order.symbol, type(order).__name__, order.date, order.qty, order.params()) for order in orders], columns = ['symbol', 'type', 'date', 'qty', 'params']) return df_orders
[docs] def df_pnl(self, symbol = None): '''Returns a dataframe with P&L columns. If symbol is set to None (default), sums up P&L across symbols''' return self.account.df_pnl(symbol)
[docs] def df_returns(self, symbol = None, sampling_frequency = 'D'): '''Return a dataframe of returns and equity indexed by date. Args: symbol: The symbol to get returns for. If set to None (default), this returns the sum of PNL for all symbols sampling_frequency: Downsampling frequency. Default is None. See pandas frequency strings for possible values ''' pnl = self.df_pnl(symbol)[['equity']] pnl.equity = pnl.equity.ffill() pnl = pnl.resample(sampling_frequency).last() pnl['ret'] = pnl.equity.pct_change() return pnl
[docs] def plot(self, symbols = None, md_columns = 'c', pnl_columns = 'equity', title = None, figsize = (20, 15), date_range = None, date_format = None, sampling_frequency = None, trade_marker_properties = None, hspace = 0.15): '''Plot indicators, signals, trades, position, pnl Args: symbols: List of symbols or None (default) for all symbols md_columns: List of columns of market data to plot. Default is 'c' for close price. You can set this to 'ohlcv' if you want to plot a candlestick of OHLCV data pnl_columns: List of P&L columns to plot. Default is 'equity' title: Title of plot (None) figsize: Figure size. Default is (20, 15) date_range: Tuple of strings or datetime64, e.g. ("2018-01-01", "2018-04-18 15:00") to restrict the graph. Default None date_format: Date format for tick labels on x axis. If set to None (default), will be selected based on date range. See matplotlib date format strings sampling_frequency: Downsampling frequency. Default is None. The graph may get too busy if you have too many bars of data, in which case you may want to downsample before plotting. See pandas frequency strings for possible values trade_marker_properties: A dictionary of order reason code -> marker shape, marker size, marker color for plotting trades with different reason codes. Default is None in which case the dictionary from the ReasonCode class is used hspace: Height (vertical) space between subplots. Default is 0.15 ''' date_range = strtup2date(date_range) if symbols is None: symbols = self.symbols if not isinstance(symbols, list): symbols = [symbols] if not isinstance(md_columns, list): md_columns = [md_columns] if not isinstance(pnl_columns, list): pnl_columns = [pnl_columns] for symbol in symbols: md = self.marketdata(symbol) md_dates = md.dates if md_columns == ['ohlcv']: md_list = [OHLC('price', dates = md_dates, o = md.o, h = md.h, l = md.l, c = md.c, v = md.v, vwap = md.vwap)] else: md_list = [TimeSeries(md_column, dates = md_dates, values = getattr(md, md_column)) for md_column in md_columns] indicator_list = [TimeSeries(indicator_name, dates = md_dates, values = self.indicator_values[symbol][indicator_name], line_type = '--' ) for indicator_name in self.indicators.keys() if indicator_name in self.indicator_values[symbol]] signal_list = [TimeSeries(signal_name, dates = md_dates, values = self.signal_values[symbol][signal_name] ) for signal_name in self.signals.keys() if signal_name in self.signal_values[symbol]] df_pnl_ = self.df_pnl(symbol) pnl_list = [TimeSeries(pnl_column, dates = df_pnl_.index.values, values = df_pnl_[pnl_column].values) for pnl_column in pnl_columns] if trade_marker_properties: trade_sets = trade_sets_by_reason_code(self._trades[symbol], trade_marker_properties) else: trade_sets = trade_sets_by_reason_code(self._trades[symbol]) main_subplot = Subplot(indicator_list + md_list + trade_sets, height_ratio = 0.5, ylabel = 'Indicators') signal_subplot = Subplot(signal_list, ylabel = 'Signals', height_ratio = 0.167) pnl_subplot = Subplot(pnl_list, ylabel = 'Equity', height_ratio = 0.167, log_y = True, y_tick_format = '${x:,.0f}') position = df_pnl_.position.values pos_subplot = Subplot([TimeSeries('position', dates = df_pnl_.index.values, values = position, plot_type = 'filled_line')], ylabel = 'Position', height_ratio = 0.167) plot = Plot([main_subplot, signal_subplot, pos_subplot, pnl_subplot], figsize = figsize, date_range = date_range, date_format = date_format, sampling_frequency = sampling_frequency, title = title, hspace = hspace) plot.draw()
[docs] def evaluate_returns(self, symbol = None, plot = True, float_precision = 4): '''Returns a dictionary of common return metrics. Args: symbol (str): Date frequency. Default 'D' for daily so we downsample to daily returns before computing metrics plot (bool): If set to True, display plots of equity, drawdowns and returns. Default False float_precision (float, optional): Number of significant figures to show in returns. Default 4 ''' returns = self.df_returns(symbol) ev = compute_return_metrics(returns.index.values, returns.ret.values, self.account.starting_equity) display_return_metrics(ev.metrics(), float_precision = float_precision) if plot: plot_return_metrics(ev.metrics()) return ev.metrics()
[docs] def plot_returns(self, symbol = None): '''Display plots of equity, drawdowns and returns for the given symbol or for all symbols if symbol is None (default)''' if symbol is None: symbols = self.symbols() else: symbols = [symbol] df_list = [] for symbol in symbols: df_list.append(self.df_returns(symbol)) df = pd.concat(df_list, axis = 1) ev = compute_return_metrics(returns.index.values, returns.ret.values, self.account.starting_equity) plot_return_metrics(ev.metrics())
def __repr__(self): return f'{pformat(self.indicators)} {pformat(self.rules)} {pformat(self.account)}'