class Strategy:
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 = []
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
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
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
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
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))
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]))
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
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)
def marketdata(self, symbol):
'''Return MarketData object for this symbol'''
return self.account.marketdata[symbol]
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)
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)
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)
]
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
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)
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
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()
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()
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)}'
class Strategy:
def __init__(self,
timestamps: np.ndarray,
contract_groups: Sequence[ContractGroup],
price_function: PriceFunctionType,
starting_equity: float = 1.0e6,
pnl_calc_time: int = 15 * 60 + 1,
trade_lag: int = 0,
run_final_calc: bool = True,
strategy_context: StrategyContextType = None) -> None:
'''
Args:
timestamps (np.array of np.datetime64): The "heartbeat" of the strategy. We will evaluate trading rules and
simulate the market at these times.
contract_groups: The contract groups we will potentially trade.
price_function: A function that returns the price of a contract at a given timestamp
starting_equity: Starting equity in Strategy currency. Default 1.e6
pnl_calc_time: Time of day used to calculate PNL. Default 15 * 60 (3 pm)
trade_lag: Number of bars you want between the order and the trade. For example, if you think it will take
5 seconds to place your order in the market, and your bar size is 1 second, set this to 5. Set this to 0 if you
want to execute your trade at the same time as you place the order, for example, if you have daily bars. Default 0.
run_final_calc: If set, calculates unrealized pnl and net pnl as well as realized pnl when strategy is done.
If you don't need unrealized pnl, turn this off for faster run time. Default True
strategy_context: A storage class where you can store key / value pairs relevant to this strategy.
For example, you may have a pre-computed table of correlations that you use in the indicator or trade rule functions.
If not set, the __init__ function will create an empty member strategy_context object that you can access.
'''
self.name = 'main' # Set by portfolio when running multiple strategies
self.timestamps = timestamps
assert(len(contract_groups) and isinstance(contract_groups[0], ContractGroup))
self.contract_groups = contract_groups
if strategy_context is None: strategy_context = types.SimpleNamespace()
self.strategy_context = strategy_context
self.account = Account(contract_groups, timestamps, price_function, strategy_context, starting_equity, pnl_calc_time)
assert trade_lag >= 0, f'trade_lag cannot be negative: {trade_lag}'
self.trade_lag = trade_lag
self.run_final_calc = run_final_calc
self.indicators: Dict[str, IndicatorType] = {}
self.signals: Dict[str, SignalType] = {}
self.signal_values: Dict[ContractGroup, SimpleNamespace] = defaultdict(types.SimpleNamespace)
self.rule_names: List[str] = []
self.rules: Dict[str, RuleType] = {}
self.position_filters: Dict[str, Optional[str]] = {}
self.rule_signals: Dict[str, Tuple[str, Sequence]] = {}
self.market_sims: List[MarketSimulatorType] = []
self._trades: List[Trade] = []
self._orders: List[Order] = []
self._open_orders: Dict[int, List[Order]] = defaultdict(list)
self.indicator_deps: Dict[str, List[str]] = {}
self.indicator_cgroups: Dict[str, List[ContractGroup]] = {}
self.indicator_values: Dict[ContractGroup, SimpleNamespace] = defaultdict(types.SimpleNamespace)
self.signal_indicator_deps: Dict[str, List[str]] = {}
self.signal_deps: Dict[str, List[str]] = {}
self.signal_cgroups: Dict[str, List[ContractGroup]] = {}
self.trades_iter: List[List] = [[] for x in range(len(timestamps))] # For debugging, we don't really need this as a member variable
def add_indicator(self,
name: str,
indicator: IndicatorType,
contract_groups: Sequence[ContractGroup] = None,
depends_on: Sequence[str] = None) -> None:
'''
Args:
name: Name of the indicator
indicator: A function that takes strategy timestamps and other indicators and returns a numpy array
containing indicator values. The return array must have the same length as the timestamps object.
Can also be a numpy array or a pandas Series in which case we just store the values.
contract_groups: Contract groups that this indicator applies to. If not set, it applies to all contract groups. Default None.
depends_on: Names of other indicators that we need to compute this indicator. Default None.
'''
self.indicators[name] = indicator
self.indicator_deps[name] = [] if depends_on is None else list(depends_on)
if contract_groups is None: contract_groups = self.contract_groups
if isinstance(indicator, np.ndarray) or isinstance(indicator, pd.Series):
indicator_values = series_to_array(indicator)
for contract_group in contract_groups:
setattr(self.indicator_values[contract_group], name, indicator_values)
self.indicator_cgroups[name] = list(contract_groups)
def add_signal(self,
name: str,
signal_function: SignalType,
contract_groups: Sequence[ContractGroup] = None,
depends_on_indicators: Sequence[str] = None,
depends_on_signals: Sequence[str] = None) -> None:
'''
Args:
name (str): Name of the signal
signal_function (function): A function that takes timestamps and a dictionary of indicator value arrays and
returns a numpy array
containing signal values. The return array must have the same length as the input timestamps
contract_groups (list of :obj:`ContractGroup`, optional): Contract groups that this signal applies to.
If not set, it applies to all contract groups. Default None.
depends_on_indicators (list of str, optional): Names of indicators that we need to compute this signal. Default None.
depends_on_signals (list of str, optional): Names of other signals that we need to compute this signal. Default None.
'''
self.signals[name] = signal_function
self.signal_indicator_deps[name] = [] if depends_on_indicators is None else list(depends_on_indicators)
self.signal_deps[name] = [] if depends_on_signals is None else list(depends_on_signals)
if contract_groups is None: contract_groups = self.contract_groups
self.signal_cgroups[name] = list(contract_groups)
def add_rule(self,
name: str,
rule_function: RuleType,
signal_name: str,
sig_true_values: Sequence = None,
position_filter: str = None) -> None:
'''Add a trading rule. Trading rules are guaranteed to run in the order in which you add them. For example, if you set trade_lag to 0,
and want to exit positions and re-enter new ones in the same bar, make sure you add the exit rule before you add the entry rule to the
strategy.
Args:
name: Name of the trading rule
rule_function: A trading rule function that returns a list of Orders
signal_name: The strategy will call the trading rule function when the signal with this name matches sig_true_values
sig_true_values: If the signal value at a bar is equal to one of these values,
the Strategy will call the trading rule function. Default [TRUE]
position_filter: Can be "zero", "nonzero" or None. Zero rules are only triggered when the corresponding contract positions are 0
Nonzero rules are only triggered when the corresponding contract positions are non-zero.
If not set, we don't look at position before triggering the rule. Default None
'''
if sig_true_values is None: sig_true_values = [True]
if name in self.rule_names:
raise Exception(f'Rule {name} already exists')
# Rules should be run in order
self.rule_names.append(name)
self.rule_signals[name] = (signal_name, sig_true_values)
self.rules[name] = rule_function
if position_filter is not None:
assert(position_filter in ['zero', 'nonzero'])
self.position_filters[name] = position_filter
def add_market_sim(self, market_sim_function: MarketSimulatorType) -> None:
'''Add a market simulator. A market simulator is a function that takes orders as input and returns trades.'''
self.market_sims.append(market_sim_function)
def run_indicators(self,
indicator_names: Sequence[str] = None,
contract_groups: Sequence[ContractGroup] = None,
clear_all: bool = False) -> None:
'''Calculate values of the indicators specified and store them.
Args:
indicator_names: List of indicator names. If None (default) run all indicators
contract_groups: Contract group to run this indicator for. If None (default), we run it for all contract groups.
clear_all: If set, clears all indicator values before running. Default False.
'''
if indicator_names is None: indicator_names = list(self.indicators.keys())
if contract_groups is None: contract_groups = self.contract_groups
if clear_all: self.indicator_values = defaultdict(types.SimpleNamespace)
ind_names = []
for ind_name, cgroup_list in self.indicator_cgroups.items():
if len(set(contract_groups).intersection(cgroup_list)): ind_names.append(ind_name)
indicator_names = list(set(ind_names).intersection(indicator_names))
for cgroup in contract_groups:
cgroup_ind_namespace = self.indicator_values[cgroup]
for indicator_name in indicator_names:
# First run all parents
parent_names = self.indicator_deps[indicator_name]
for parent_name in parent_names:
if cgroup in self.indicator_values and hasattr(cgroup_ind_namespace, parent_name): continue
self.run_indicators([parent_name], [cgroup])
# Now run the actual indicator
if cgroup in self.indicator_values and hasattr(cgroup_ind_namespace, indicator_name): continue
indicator_function = self.indicators[indicator_name]
parent_values = types.SimpleNamespace()
for parent_name in parent_names:
setattr(parent_values, parent_name, getattr(cgroup_ind_namespace, parent_name))
if isinstance(indicator_function, np.ndarray) or isinstance(indicator_function, pd.Series):
indicator_values = indicator_function
else:
indicator_values = indicator_function(cgroup, self.timestamps, parent_values, self.strategy_context)
setattr(cgroup_ind_namespace, indicator_name, series_to_array(indicator_values))
def run_signals(self,
signal_names: Sequence[str] = None,
contract_groups: Sequence[ContractGroup] = None,
clear_all: bool = False) -> None:
'''Calculate values of the signals specified and store them.
Args:
signal_names: List of signal names. If None (default) run all signals
contract_groups: Contract groups to run this signal for. If None (default), we run it for all contract groups.
clear_all: If set, clears all signal values before running. Default False.
'''
if signal_names is None: signal_names = list(self.signals.keys())
if contract_groups is None: contract_groups = self.contract_groups
if clear_all: self.signal_values = defaultdict(types.SimpleNamespace)
sig_names = []
for sig_name, cgroup_list in self.signal_cgroups.items():
if len(set(contract_groups).intersection(cgroup_list)):
sig_names.append(sig_name)
signal_names = list(set(sig_names).intersection(signal_names))
for cgroup in contract_groups:
for signal_name in signal_names:
if cgroup not in self.signal_cgroups[signal_name]: continue
# First run all parent signals
parent_names = self.signal_deps[signal_name]
for parent_name in parent_names:
if cgroup in self.signal_values and hasattr(self.signal_values[cgroup], parent_name): continue
self.run_signals([parent_name], [cgroup])
# Now run the actual signal
if cgroup in self.signal_values and hasattr(self.signal_values[cgroup], signal_name): continue
signal_function = self.signals[signal_name]
parent_values = types.SimpleNamespace()
for parent_name in parent_names:
sig_vals = getattr(self.signal_values[cgroup], parent_name)
setattr(parent_values, parent_name, sig_vals)
# Get indicators needed for this signal
indicator_values = types.SimpleNamespace()
for indicator_name in self.signal_indicator_deps[signal_name]:
setattr(indicator_values, indicator_name, getattr(self.indicator_values[cgroup], indicator_name))
signal_output = signal_function(cgroup, self.timestamps, indicator_values, parent_values, self.strategy_context)
setattr(self.signal_values[cgroup], signal_name, series_to_array(signal_output))
def _generate_order_iterations(self,
rule_names: Sequence[str] = None,
contract_groups: Sequence[ContractGroup] = None,
start_date: np.datetime64 = None,
end_date: np.datetime64 = None) -> None:
'''
>>> class MockStrat:
... def __init__(self):
... self.timestamps = timestamps
... self.account = self
... self.rules = {'rule_a': rule_a, 'rule_b': rule_b}
... 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: types.SimpleNamespace(sig_a=np.array([0., 1., 1.]),
... sig_b = np.array([0., 0., 0.]) ),
... aapl: types.SimpleNamespace(sig_a=np.array([0., 0., 0.]),
... sig_b=np.array([0., -1., -1])
... )}
... self.signal_cgroups = {'sig_a': [ibm, aapl], 'sig_b': [ibm, aapl]}
... self.indicator_values = {ibm: types.SimpleNamespace(), aapl: types.SimpleNamespace()}
>>>
>>> def market_sim_aapl(): pass
>>> def market_sim_ibm(): pass
>>> def rule_a(): pass
>>> def rule_b(): pass
>>> timestamps = np.array(['2018-01-01', '2018-01-02', '2018-01-03'], dtype = 'M8[D]')
>>> rule_names = ['rule_a', 'rule_b']
>>> ContractGroup.clear()
>>> ibm = ContractGroup.create('IBM')
>>> aapl = ContractGroup.create('AAPL')
>>> contract_groups = [ibm, aapl]
>>> start_date = np.datetime64('2018-01-01')
>>> end_date = np.datetime64('2018-02-05')
>>> strategy = MockStrat()
>>> Strategy._generate_order_iterations(strategy, rule_names, contract_groups, start_date, end_date)
>>> orders_iter = strategy.orders_iter
>>> 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.rule_names
if contract_groups is None: contract_groups = self.contract_groups
num_timestamps = len(self.timestamps)
# List of lists, i -> list of order tuple
orders_iter: List[List[OrderTupType]] = [[] for x in range(num_timestamps)]
for rule_name in rule_names:
rule_function = self.rules[rule_name]
for cgroup in contract_groups:
signal_name, sig_true_values = self.rule_signals[rule_name]
if cgroup not in self.signal_cgroups[signal_name]:
# We don't need to call this rule for this contract group
continue
sig_values = getattr(self.signal_values[cgroup], signal_name)
timestamps = self.timestamps
null_value = False if sig_values.dtype == np.dtype('bool') else np.nan
if start_date is not None:
start_idx: int = np.searchsorted(timestamps, start_date) # type: ignore
sig_values[0:start_idx] = null_value
if end_date is not None:
end_idx: int = np.searchsorted(timestamps, end_date) # type: ignore
sig_values[end_idx:] = null_value
indices = np.nonzero(np.isin(sig_values[:num_timestamps], sig_true_values))[0]
# Don't run rules on last index since we cannot fill any orders
if len(indices) and indices[-1] == len(sig_values) - 1: indices = indices[:-1]
indicator_values = self.indicator_values[cgroup]
iteration_params = {'indicator_values': indicator_values, 'signal_values': sig_values, 'rule_name': rule_name}
for idx in indices: orders_iter[idx].append((rule_function, cgroup, iteration_params))
self.orders_iter = orders_iter
def run_rules(self,
rule_names: Sequence[str] = None,
contract_groups: Sequence[ContractGroup] = None,
start_date: np.datetime64 = None,
end_date: np.datetime64 = None) -> None:
'''
Run trading rules.
Args:
rule_names: List of rule names. If None (default) run all rules
contract_groups: Contract groups to run this rule for. If None (default), we run it for all contract groups.
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)
self._generate_order_iterations(rule_names, contract_groups, start_date, end_date)
# Now we know which rules, contract groups 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 in range(len(self.orders_iter)):
self._run_iteration(i)
if self.run_final_calc:
self.account.calc(self.timestamps[-1])
def _run_iteration(self, i: int) -> None:
self._sim_market(i)
# Treat all orders as IOC, i.e. if the order was not executed, then its cancelled.
self._open_orders[i] = []
rules = self.orders_iter[i]
for (rule_function, contract_group, params) in rules:
orders = self._get_orders(i, rule_function, contract_group, params)
self._orders += orders
self._open_orders[i + self.trade_lag] += orders
# If the lag is 0, then run rules one by one, and after each rule, run market sim to generate trades and update
# positions. For example, if we have a rule to exit a position and enter a new one, we should make sure
# positions are updated after the first rule before running the second rule. If the lag is not 0,
# run all rules and collect the orders, we don't need to run market sim after each rule
if self.trade_lag == 0: self._sim_market(i)
# If we failed to fully execute any orders in this iteration, add them to the next iteration so we get another chance to execute
open_orders = self._open_orders.get(i)
if open_orders is not None and len(open_orders):
self._open_orders[i + 1] += open_orders
def run(self) -> None:
self.run_indicators()
self.run_signals()
self.run_rules()
def _get_orders(self, idx: int, rule_function: RuleType, contract_group: ContractGroup, params: Dict[str, Any]) -> Sequence[Order]:
try:
indicator_values, signal_values, rule_name = params['indicator_values'], params['signal_values'], params['rule_name']
position_filter = self.position_filters[rule_name]
if position_filter is not None:
curr_pos = self.account.position(contract_group, self.timestamps[idx])
if position_filter == 'zero' and not math.isclose(curr_pos, 0): return []
if position_filter == 'nonzero' and math.isclose(curr_pos, 0): return []
orders = rule_function(contract_group, idx, self.timestamps, indicator_values, signal_values, self.account,
self.strategy_context)
except Exception as e:
raise type(e)(
f'Exception: {str(e)} at rule: {type(rule_function)} contract_group: {contract_group} index: {idx}'
).with_traceback(sys.exc_info()[2])
return orders
def _sim_market(self, i: int) -> None:
'''
Go through all open orders and run market simulators to generate a list of trades and return any orders that were not filled.
'''
open_orders = self._open_orders.get(i)
if open_orders is None or len(open_orders) == 0: return
for market_sim_function in self.market_sims:
try:
trades = market_sim_function(open_orders, i, self.timestamps, self.indicator_values, self.signal_values, self.strategy_context)
if len(trades): self.account.add_trades(trades)
self._trades += trades
except Exception as e:
raise type(e)(f'Exception: {str(e)} at index: {i} function: {market_sim_function}').with_traceback(sys.exc_info()[2])
self._open_orders[i] = [order for order in open_orders if order.status != 'filled']
def df_data(self,
contract_groups: Sequence[ContractGroup] = None,
add_pnl: bool = True,
start_date: Union[str, np.datetime64] = None,
end_date: Union[str, np.datetime64] = None) -> pd.DataFrame:
'''
Add indicators and signals to end of market data and return as a pandas dataframe.
Args:
contract_groups (list of:obj:`ContractGroup`, optional): list of contract groups 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 contract_groups is None: contract_groups = self.contract_groups
timestamps = self.timestamps
if start_date: timestamps = timestamps[timestamps >= start_date]
if end_date: timestamps = timestamps[timestamps <= end_date]
dfs = []
for contract_group in contract_groups:
df = pd.DataFrame({'timestamp': self.timestamps})
if add_pnl:
df_pnl = self.df_pnl(contract_group)
indicator_values = self.indicator_values[contract_group]
for k in sorted(indicator_values.__dict__):
name = k
# Avoid name collisions
if name in df.columns: name = name + '.ind'
df.insert(len(df.columns), name, getattr(indicator_values, k))
signal_values = self.signal_values[contract_group]
for k in sorted(signal_values.__dict__):
name = k
if name in df.columns: name = name + '.sig'
df.insert(len(df.columns), name, getattr(signal_values, k))
if add_pnl: df = pd.merge(df, df_pnl, on=['timestamp'], how='left')
# Add counter column for debugging
df.insert(len(df.columns), 'i', np.arange(len(df)))
dfs.append(df)
return pd.concat(dfs)
def trades(self,
contract_group: ContractGroup = None,
start_date: np.datetime64 = None,
end_date: np.datetime64 = None) -> Sequence[Trade]:
'''Returns a list of trades with the given contract group and with trade date between (and including) start date
and end date if they are specified.
If contract_group is None trades for all contract_groups are returned'''
start_date, end_date = str2date(start_date), str2date(end_date)
return self.account.trades(contract_group, start_date, end_date)
def df_trades(self,
contract_group: ContractGroup = None,
start_date: np.datetime64 = None,
end_date: np.datetime64 = None) -> pd.DataFrame:
'''Returns a dataframe with data from trades with the given contract group and with trade date between (and including)
start date and end date
if they are specified. If contract_group is None trades for all contract_groups are returned'''
start_date, end_date = str2date(start_date), str2date(end_date)
return self.account.df_trades(contract_group, start_date, end_date)
def orders(self,
contract_group: ContractGroup = None,
start_date: Union[np.datetime64, str] = None,
end_date: Union[np.datetime64, str] = None) -> Sequence[Order]:
'''Returns a list of orders with the given contract group and with order date between (and including) start date and
end date if they are specified.
If contract_group is None orders for all contract_groups are returned'''
orders: List[Order] = []
start_date, end_date = str2date(start_date), str2date(end_date)
if contract_group is None:
orders += [order for order in self._orders if (
start_date is None or order.timestamp >= start_date) and (end_date is None or order.timestamp <= end_date)]
else:
for contract in contract_group.contracts:
orders += [order for order in self._orders if (contract is None or order.contract == contract) and (
start_date is None or order.timestamp >= start_date) and (end_date is None or order.timestamp <= end_date)]
return orders
def df_orders(self, contract_group=None, start_date=None, end_date=None) -> pd.DataFrame:
'''Returns a dataframe with data from orders with the given contract group and with order date between (and including)
start date and end date
if they are specified. If contract_group is None orders for all contract_groups are returned'''
start_date, end_date = str2date(start_date), str2date(end_date)
orders = self.orders(contract_group, start_date, end_date)
order_records = [(order.contract.symbol, type(order).__name__, order.timestamp, order.qty,
order.reason_code,
(str(order.properties.__dict__) if order.properties.__dict__ else ''),
(str(order.contract.properties.__dict__) if order.contract.properties.__dict__ else '')) for order in orders]
df_orders = pd.DataFrame.from_records(order_records,
columns=['symbol', 'type', 'timestamp', 'qty', 'reason_code', 'order_props', 'contract_props'])
return df_orders
def df_pnl(self, contract_group=None) -> pd.DataFrame:
'''Returns a dataframe with P&L columns. If contract group is set to None (default), sums up P&L across all contract groups'''
return self.account.df_account_pnl(contract_group)
def df_returns(self,
contract_group: ContractGroup = None,
sampling_frequency: str = 'D') -> pd.DataFrame:
'''Return a dataframe of returns and equity indexed by date.
Args:
contract_group: The contract group to get returns for.
If set to None (default), we return the sum of PNL for all contract groups
sampling_frequency: Downsampling frequency. Default is None. See pandas frequency strings for possible values
'''
pnl = self.df_pnl(contract_group)[['timestamp', 'net_pnl', 'equity']]
pnl.equity = pnl.equity.ffill()
pnl = pnl.set_index('timestamp').resample(sampling_frequency).last().reset_index()
pnl = pnl.dropna(subset=['equity'])
pnl['ret'] = pnl.equity.pct_change()
return pnl
def plot(self,
contract_groups: Sequence[ContractGroup] = None,
primary_indicators: Sequence[str] = None,
primary_indicators_dual_axis: Sequence[str] = None,
secondary_indicators: Sequence[str] = None,
secondary_indicators_dual_axis: Sequence[str] = None,
indicator_properties: PlotPropertiesType = None,
signals: Sequence[str] = None,
signal_properties: PlotPropertiesType = None,
pnl_columns: Sequence[str] = None,
title: str = None,
figsize: Tuple[int, int] = (20, 15),
_date_range: DateRangeType = None,
date_format: str = None,
sampling_frequency: str = None,
trade_marker_properties: PlotPropertiesType = None,
hspace: float = 0.15) -> None:
'''
Plot indicators, signals, trades, position, pnl
Args:
contract_groups: Contract groups to plot or None (default) for all contract groups.
primary indicators: List of indicators to plot in the main indicator section. Default None (plot everything)
primary indicators: List of indicators to plot in the secondary indicator section. Default None (don't plot anything)
indicator_properties: If set, we use the line color, line type indicated for the given indicators
signals: Signals to plot. Default None (plot everything).
plot_equity: If set, we plot the equity curve. Default is True
title: Title of plot. Default None
figsize: Figure size. Default (20, 15)
date_range: Used to restrict the date range of 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. The graph may get too busy if you have too many bars of data,
so you may want to downsample before plotting. See pandas frequency strings for possible values. Default None.
trade_marker_properties: A dictionary of order reason code -> marker shape, marker size, marker color for plotting trades
with different reason codes. By default we use the dictionary from the :obj:`ReasonCode` class
hspace: Height (vertical) space between subplots. Default is 0.15
'''
date_range = strtup2date(_date_range)
if contract_groups is None: contract_groups = self.contract_groups
if isinstance(contract_groups, ContractGroup): contract_groups = [contract_groups]
if pnl_columns is None: pnl_columns = ['equity']
for contract_group in contract_groups:
primary_indicator_names = [ind_name for ind_name in self.indicator_values[contract_group].__dict__
if hasattr(self.indicator_values[contract_group], ind_name)]
if primary_indicators:
primary_indicator_names = list(set(primary_indicator_names).intersection(primary_indicators))
secondary_indicator_names: List[str] = []
if secondary_indicators:
secondary_indicator_names = list(secondary_indicators)
signal_names = [sig_name for sig_name in self.signals.keys() if hasattr(self.signal_values[contract_group], sig_name)]
if signals:
signal_names = list(set(signal_names).intersection(signals))
primary_indicator_list = _get_time_series_list(self.timestamps, primary_indicator_names,
self.indicator_values[contract_group], indicator_properties)
secondary_indicator_list = _get_time_series_list(self.timestamps, secondary_indicator_names,
self.indicator_values[contract_group], indicator_properties)
signal_list = _get_time_series_list(self.timestamps, signal_names, self.signal_values[contract_group], signal_properties)
df_pnl_ = self.df_pnl(contract_group)
pnl_list = [TimeSeries(pnl_column,
timestamps=df_pnl_.timestamp.values,
values=df_pnl_[pnl_column].values) for pnl_column in pnl_columns]
trades = [trade for trade in self._trades if trade.order.contract.contract_group == contract_group]
if trade_marker_properties:
trade_sets = trade_sets_by_reason_code(trades, trade_marker_properties, remove_missing_properties=True)
else:
trade_sets = trade_sets_by_reason_code(trades)
primary_indicator_subplot = Subplot(
primary_indicator_list + trade_sets, # type: ignore # mypy does not allow adding heterogeneous lists
secondary_y=primary_indicators_dual_axis,
height_ratio=0.5,
ylabel='Primary Indicators')
if len(secondary_indicator_list):
secondary_indicator_subplot = Subplot(secondary_indicator_list,
secondary_y=secondary_indicators_dual_axis,
height_ratio=0.5,
ylabel='Secondary 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
disp_attribs = FilledLinePlotAttributes()
pos_subplot = Subplot(
[TimeSeries('position', timestamps=df_pnl_.timestamp, values=position, display_attributes=disp_attribs)],
ylabel='Position', height_ratio=0.167)
title_full = title
if len(contract_groups) > 1:
if title is None: title = ''
title_full = f'{title} {contract_group.name}'
plot_list = []
if len(primary_indicator_list): plot_list.append(primary_indicator_subplot)
if len(secondary_indicator_list): plot_list.append(secondary_indicator_subplot)
if len(signal_list): plot_list.append(signal_subplot)
if len(position): plot_list.append(pos_subplot)
if len(pnl_list): plot_list.append(pnl_subplot)
if not len(plot_list): return
plot = Plot(plot_list,
figsize=figsize,
date_range=date_range,
date_format=date_format,
sampling_frequency=sampling_frequency,
title=title_full,
hspace=hspace)
plot.draw()
def evaluate_returns(self,
contract_group: ContractGroup = None,
plot: bool = True,
display_summary: bool = True,
float_precision: int = 4,
return_metrics: bool = False) -> Optional[Mapping]:
'''Returns a dictionary of common return metrics.
Args:
contract_group (:obj:`ContractGroup`, optional): Contract group to evaluate or None (default) for all contract groups
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
return_metrics (bool, optional): If set, we return the computed metrics as a dictionary
'''
returns = self.df_returns(contract_group)
ev = compute_return_metrics(returns.timestamp.values, returns.ret.values, self.account.starting_equity)
if display_summary:
display_return_metrics(ev.metrics(), float_precision=float_precision)
if plot:
plot_return_metrics(ev.metrics())
if return_metrics:
return ev.metrics()
return None
def plot_returns(self, contract_group: ContractGroup = None) -> Optional[Tuple[mpl.figure.Figure, mpl.axes.Axes]]:
'''Display plots of equity, drawdowns and returns for the given contract group or for all contract groups if contract_group
is None (default)'''
if contract_group is None:
returns = self.df_returns()
else:
returns = self.df_returns(contract_group)
ev = compute_return_metrics(returns.timestamp.values, returns.ret.values, self.account.starting_equity)
return plot_return_metrics(ev.metrics())
def __repr__(self):
return f'{pformat(self.indicators)} {pformat(self.rules)} {pformat(self.account)}'
class Strategy:
def __init__(self, timestamps, contract_groups, price_function, starting_equity = 1.0e6, pnl_calc_time = 15 * 60 + 1,
run_final_calc = True, strategy_context = None):
'''
Args:
timestamps (np.array of np.datetime64): The "heartbeat" of the strategy. We will evaluate trading rules and
simulate the market at these times.
price_function: A function that returns the price of a contract at a given timestamp
contract_groups (list of :obj:`ContractGroup`): The contract groups we will potentially trade.
starting_equity (float, optional): Starting equity in Strategy currency. Default 1.e6
pnl_calc_time (int, optional): Time of day used to calculate PNL. Default 15 * 60 (3 pm)
run_final_calc (bool, optional): If set, calculates unrealized pnl and net pnl as well as realized pnl when strategy is done.
If you don't need unrealized pnl, turn this off for faster run time. Default True
strategy_context (:obj:`types.SimpleNamespace`, optional): A storage class where you can store key / value pairs
relevant to this strategy. For example, you may have a pre-computed table of correlations that you use in the
indicator or trade rule functions.
If not set, the __init__ function will create an empty member strategy_context object that you can access.
'''
self.name = None
self.timestamps = timestamps
assert(len(contract_groups)) and isinstance(contract_groups[0], ContractGroup)
self.contract_groups = contract_groups
if strategy_context is None: strategy_context = types.SimpleNamespace()
self.strategy_context = strategy_context
self.account = Account(contract_groups, timestamps, price_function, strategy_context, starting_equity, pnl_calc_time)
self.run_final_calc = run_final_calc
self.indicators = {}
self.signals = {}
self.signal_values = defaultdict(types.SimpleNamespace)
self.rules = {}
self.position_filters = {}
self.rule_signals = {}
self.market_sims = {}
self._trades = defaultdict(list)
self._orders = []
self.indicator_deps = {}
self.indicator_cgroups = {}
self.indicator_values = defaultdict(types.SimpleNamespace)
self.signal_indicator_deps = {}
self.signal_deps = {}
self.signal_cgroups = {}
def add_indicator(self, name, indicator, contract_groups = None, depends_on = None):
'''
Args:
name: Name of the indicator
indicator: A function that takes strategy timestamps and other indicators and returns a numpy array
containing indicator values. The return array must have the same length as the timestamps object.
Can also be a numpy array or a pandas Series in which case we just store the values.
contract_groups (list of :obj:`ContractGroup`, optional): Contract groups that this indicator applies to.
If not set, it applies to all contract groups. Default None.
depends_on (list of str, optional): Names of other indicators that we need to compute this indicator.
Default None.
'''
self.indicators[name] = indicator
self.indicator_deps[name] = [] if depends_on is None else depends_on
if contract_groups is None: contract_groups = self.contract_groups
if isinstance(indicator, np.ndarray) or isinstance(indicator, pd.Series):
indicator_values = series_to_array(indicator)
for contract_group in contract_groups:
setattr(self.indicator_values[contract_group], name, indicator_values)
self.indicator_cgroups[name] = contract_groups
def add_signal(self, name, signal_function, contract_groups = None, depends_on_indicators = None, depends_on_signals = None):
'''
Args:
name (str): Name of the signal
signal_function (function): A function that takes timestamps and a dictionary of indicator value arrays and
returns a numpy array
containing signal values. The return array must have the same length as the input timestamps
contract_groups (list of :obj:`ContractGroup`, optional): Contract groups that this signal applies to.
If not set, it applies to all contract groups. Default None.
depends_on_indicators (list of str, optional): Names of indicators that we need to compute this signal. Default None.
depends_on_signals (list of str, optional): Names of other signals that we need to compute this signal. Default None.
'''
self.signals[name] = signal_function
self.signal_indicator_deps[name] = [] if depends_on_indicators is None else depends_on_indicators
self.signal_deps[name] = [] if depends_on_signals is None else depends_on_signals
if contract_groups is None: contract_groups = self.contract_groups
self.signal_cgroups[name] = contract_groups
def add_rule(self, name, rule_function, signal_name, sig_true_values = None, position_filter = 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]
position_filter (str, optional): Can be "zero", "nonzero" or None. Zero rules are only triggered when
the corresponding contract positions are 0
Nonzero rules are only triggered when the corresponding contract positions are non-zero.
If not set, we don't look at position before triggering the rule.
Default None
'''
if sig_true_values is None: sig_true_values = [True]
self.rule_signals[name] = (signal_name, sig_true_values)
self.rules[name] = rule_function
if position_filter is not None:
assert(position_filter in ['zero', 'nonzero'])
self.position_filters[name] = position_filter
def add_market_sim(self, market_sim_function, contract_groups = 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 (function): A function that takes a list of Orders and Indicators as input
and returns a list of Trade objects
contract_groups (list of :obj:`ContractGroup`, optional): Contract groups that this simulator applies to.
If not set, it applies to all contract groups. Default None.
'''
if contract_groups is None: contract_groups = self.contract_groups
for contract_group in contract_groups: self.market_sims[contract_group] = market_sim_function
def run_indicators(self, indicator_names = None, contract_groups = None, clear_all = False):
'''Calculate values of the indicators specified and store them.
Args:
indicator_names (list of str, optional): List of indicator names. If None (default) run all indicators
contract_groups (list of :obj:`ContractGroup`, optional): Contract group to run this indicator for.
If None (default), we run it for all contract groups.
clear_all (bool, optional): If set, clears all indicator values before running. Default False.
'''
if indicator_names is None: indicator_names = self.indicators.keys()
if contract_groups is None: contract_groups = self.contract_groups
if clear_all: self.indicator_values = defaultdict(types.SimpleNamespace)
ind_names = []
for ind_name, cgroup_list in self.indicator_cgroups.items():
if len(set(contract_groups).intersection(cgroup_list)): ind_names.append(ind_name)
indicator_names = list(set(ind_names).intersection(indicator_names))
for cgroup in contract_groups:
cgroup_ind_namespace = self.indicator_values[cgroup]
for indicator_name in indicator_names:
# First run all parents
parent_names = self.indicator_deps[indicator_name]
for parent_name in parent_names:
if cgroup in self.indicator_values and hasattr(cgroup_ind_namespace, parent_name): continue
self.run_indicators([parent_name], [cgroup])
# Now run the actual indicator
if cgroup in self.indicator_values and hasattr(cgroup_ind_namespace, indicator_name): continue
indicator_function = self.indicators[indicator_name]
parent_values = types.SimpleNamespace()
for parent_name in parent_names:
setattr(parent_values, parent_name, getattr(cgroup_ind_namespace, parent_name))
if isinstance(indicator_function, np.ndarray) or isinstance(indicator_function, pd.Series):
indicator_values = indicator_function
else:
indicator_values = indicator_function(cgroup, self.timestamps, parent_values, self.strategy_context)
setattr(cgroup_ind_namespace, indicator_name, series_to_array(indicator_values))
def run_signals(self, signal_names = None, contract_groups = None, clear_all = False):
'''Calculate values of the signals specified and store them.
Args:
signal_names (list of str, optional): List of signal names. If None (default) run all signals
contract_groups (list of :obj:`ContractGroup`, optional): Contract groups to run this signal for.
If None (default), we run it for all contract groups.
clear_all (bool, optional): If set, clears all signal values before running. Default False.
'''
if signal_names is None: signal_names = self.signals.keys()
if contract_groups is None: contract_groups = self.contract_groups
if clear_all: self.signal_values = defaultdict(types.SimpleNamespace)
sig_names = []
for sig_name, cgroup_list in self.signal_cgroups.items():
if len(set(contract_groups).intersection(cgroup_list)):
sig_names.append(sig_name)
signal_names = list(set(sig_names).intersection(signal_names))
for cgroup in contract_groups:
for signal_name in signal_names:
if cgroup not in self.signal_cgroups[signal_name]: continue
# First run all parent signals
parent_names = self.signal_deps[signal_name]
for parent_name in parent_names:
if cgroup in self.signal_values and hasattr(self.signal_values[cgroup], parent_name): continue
self.run_signals([parent_name], [cgroup])
# Now run the actual signal
if cgroup in self.signal_values and hasattr(self.signal_values[cgroup], signal_name): continue
signal_function = self.signals[signal_name]
parent_values = types.SimpleNamespace()
for parent_name in parent_names:
sig_vals = getattr(self.signal_values[cgroup], parent_name)
setattr(parent_values, parent_name, sig_vals)
# Get indicators needed for this signal
indicator_values = types.SimpleNamespace()
for indicator_name in self.signal_indicator_deps[signal_name]:
setattr(indicator_values, indicator_name, getattr(self.indicator_values[cgroup], indicator_name))
setattr(self.signal_values[cgroup], signal_name, series_to_array(
signal_function(cgroup, self.timestamps, indicator_values, parent_values, self.strategy_context)))
def _get_iteration_indices(self, rule_names = None, contract_groups = None, start_date = None, end_date = None):
'''
>>> class MockStrat:
... def __init__(self):
... self.timestamps = timestamps
... self.account = self
... self.rules = {'rule_a' : rule_a, 'rule_b' : rule_b}
... 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 : types.SimpleNamespace(sig_a = np.array([0., 1., 1.]),
... sig_b = np.array([0., 0., 0.]) ),
... aapl : types.SimpleNamespace(sig_a = np.array([0., 0., 0.]),
... sig_b = np.array([0., -1., -1])
... )}
... self.signal_cgroups = {'sig_a' : [ibm, aapl], 'sig_b' : [ibm, aapl]}
... self.indicator_values = {ibm : types.SimpleNamespace(), aapl : types.SimpleNamespace()}
>>>
>>> def market_sim_aapl(): pass
>>> def market_sim_ibm(): pass
>>> def rule_a(): pass
>>> def rule_b(): pass
>>> timestamps = np.array(['2018-01-01', '2018-01-02', '2018-01-03'], dtype = 'M8[D]')
>>> rule_names = ['rule_a', 'rule_b']
>>> ContractGroup.clear()
>>> ibm = ContractGroup.create('IBM')
>>> aapl = ContractGroup.create('AAPL')
>>> contract_groups = [ibm, aapl]
>>> start_date = np.datetime64('2018-01-01')
>>> end_date = np.datetime64('2018-02-05')
>>> timestamps, orders_iter, trades_iter = Strategy._get_iteration_indices(MockStrat(), rule_names, contract_groups,
... start_date, end_date)
>>> 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 contract_groups is None: contract_groups = self.contract_groups
num_timestamps = len(self.timestamps)
orders_iter = [[] for x in range(num_timestamps)]
trades_iter = [[] for x in range(num_timestamps)]
for rule_name in rule_names:
rule_function = self.rules[rule_name]
for cgroup in contract_groups:
market_sim = self.market_sims[cgroup]
signal_name, sig_true_values = self.rule_signals[rule_name]
if cgroup not in self.signal_cgroups[signal_name]:
# We don't need to call this rule for this contract group
continue
sig_values = getattr(self.signal_values[cgroup], signal_name)
timestamps = self.timestamps
null_value = False if sig_values.dtype == np.dtype('bool') else np.nan
if start_date: sig_values[0:np.searchsorted(timestamps, start_date)] = null_value
if end_date: sig_values[np.searchsorted(timestamps, end_date):] = null_value
indices = np.nonzero(np.isin(sig_values[:num_timestamps], sig_true_values))[0]
# Don't run rules on last index since we cannot fill any orders
if len(indices) and indices[-1] == len(sig_values) -1: indices = indices[:-1]
indicator_values = self.indicator_values[cgroup]
iteration_params = {'market_sim' : market_sim, 'indicator_values' : indicator_values,
'signal_values' : sig_values, 'rule_name' : rule_name}
for idx in indices: orders_iter[idx].append((rule_function, cgroup, iteration_params))
self.orders_iter = orders_iter
self.trades_iter = trades_iter # For debugging
return self.timestamps, orders_iter, trades_iter
def run_rules(self, rule_names = None, contract_groups = None, start_date = None, end_date = None):
'''Run trading rules.
Args:
rule_names: List of rule names. If None (default) run all rules
contract_groups (list of :obj:`ContractGroup`, optional): Contract groups to run this rule for.
If None (default), we run it for all contract groups.
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)
timestamps, orders_iter, trades_iter = self._get_iteration_indices(rule_names, contract_groups, start_date, end_date)
# Now we know which rules, contract groups 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)
if self.run_final_calc:
self.account.calc(self.timestamps[-1])
def run(self):
self.run_indicators()
self.run_signals()
self.run_rules()
def _check_for_trades(self, i, tup_list):
for tup in tup_list:
try:
open_orders, contract_group, params = tup
open_orders, trades = self._sim_market(i, open_orders, contract_group, params)
if len(open_orders): self.trades_iter[i + 1].append((open_orders, contract_group, params))
except Exception as e:
raise type(e)(f'Exception: {str(e)} at rule: {type(tup[0])} contract_group: {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, contract_group, params = tup
open_orders = self._get_orders(i, rule_function, contract_group, params)
self._orders += open_orders
if not len(open_orders): continue
self.trades_iter[i + 1].append((open_orders, contract_group, params))
except Exception as e:
raise type(e)(f'Exception: {str(e)} at rule: {type(tup[0])} contract_group: {tup[1]} index: {i}'
).with_traceback(sys.exc_info()[2])
def _get_orders(self, idx, rule_function, contract_group, params):
indicator_values, signal_values, rule_name = (params['indicator_values'], params['signal_values'], params['rule_name'])
position_filter = self.position_filters[rule_name]
if position_filter is not None:
curr_pos = self.account.position(contract_group, self.timestamps[idx])
if position_filter == 'zero' and not math.isclose(curr_pos, 0): return []
if position_filter == 'nonzero' and math.isclose(curr_pos, 0): return []
open_orders = rule_function(contract_group, idx, self.timestamps, indicator_values, signal_values, self.account,
self.strategy_context)
return open_orders
def _sim_market(self, idx, open_orders, contract_group, 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(open_orders, idx, self.timestamps, params['indicator_values'], params['signal_values'],
self.strategy_context)
if len(trades) == 0: return [], []
self.account.add_trades(trades)
for trade in trades: self._trades[trade.order.contract.contract_group].append(trade)
open_orders = [order for order in open_orders if order.status == 'open']
return open_orders, trades
def df_data(self, contract_groups = 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:
contract_groups (list of :obj:`ContractGroup`, optional): list of contract groups 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 contract_groups is None: contract_groups = self.contract_groups
timestamps = self.timestamps
if start_date: timestamps = timestamps[timestamps >= start_date]
if end_date: timestamps = timestamps[timestamps <= end_date]
dfs = []
for contract_group in contract_groups:
df = pd.DataFrame({'timestamp' : self.timestamps})
if add_pnl:
df_pnl = self.df_pnl(contract_group)
indicator_values = self.indicator_values[contract_group]
for k in sorted(indicator_values.__dict__):
name = k
# Avoid name collisions
if name in df.columns: name = name + '.ind'
df.insert(len(df.columns), name, getattr(indicator_values, k))
signal_values = self.signal_values[contract_group]
for k in sorted(signal_values.__dict__):
name = k
if name in df.columns: name = name + '.sig'
df.insert(len(df.columns), name, getattr(signal_values, k))
if add_pnl: df = pd.merge(df, df_pnl, on = ['timestamp'], how = 'left')
# Add counter column for debugging
df.insert(len(df.columns), 'i', np.arange(len(df)))
dfs.append(df)
return pd.concat(dfs)
def trades(self, contract_group = None, start_date = None, end_date = None):
'''Returns a list of trades with the given contract group and with trade date between (and including) start date
and end date if they are specified.
If contract_group is None trades for all contract_groups are returned'''
start_date, end_date = str2date(start_date), str2date(end_date)
return self.account.trades(contract_group, start_date, end_date)
def df_trades(self, contract_group = None, start_date = None, end_date = None):
'''Returns a dataframe with data from trades with the given contract group and with trade date between (and including)
start date and end date
if they are specified. If contract_group is None trades for all contract_groups are returned'''
start_date, end_date = str2date(start_date), str2date(end_date)
return self.account.df_trades(contract_group, start_date, end_date)
def orders(self, contract_group = None, start_date = None, end_date = None):
'''Returns a list of orders with the given contract group and with order date between (and including) start date and
end date if they are specified.
If contract_group is None orders for all contract_groups are returned'''
orders = []
start_date, end_date = str2date(start_date), str2date(end_date)
if contract_group is None:
orders += [order for order in self._orders if (
start_date is None or order.date >= start_date) and (end_date is None or order.date <= end_date)]
else:
for contract in contract_group.contracts:
orders += [order for order in self._orders if (contract is None or order.contract == contract) and (
start_date is None or order.date >= start_date) and (end_date is None or order.date <= end_date)]
return orders
def df_orders(self, contract_group = None, start_date = None, end_date = None):
'''Returns a dataframe with data from orders with the given contract group and with order date between (and including)
start date and end date
if they are specified. If contract_group is None orders for all contract_groups are returned'''
start_date, end_date = str2date(start_date), str2date(end_date)
orders = self.orders(contract_group, start_date, end_date)
order_records = [(order.contract.symbol, type(order).__name__, order.timestamp, order.qty,
order.reason_code,
(str(order.properties.__dict__) if order.properties.__dict__ else ''),
(str(order.contract.properties.__dict__) if order.contract.properties.__dict__ else '')) for order in orders]
df_orders = pd.DataFrame.from_records(order_records,
columns = ['symbol', 'type', 'timestamp', 'qty', 'reason_code', 'order_props', 'contract_props'])
return df_orders
def df_pnl(self, contract_group = None):
'''Returns a dataframe with P&L columns. If contract group is set to None (default), sums up P&L across all contract groups'''
return self.account.df_account_pnl(contract_group)
def df_returns(self, contract_group = None, sampling_frequency = 'D'):
'''Return a dataframe of returns and equity indexed by date.
Args:
contract_group (:obj:`ContractGroup`, optional) : The contract group to get returns for.
If set to None (default), we return the sum of PNL for all contract groups
sampling_frequency: Downsampling frequency. Default is None. See pandas frequency strings for possible values
'''
pnl = self.df_pnl(contract_group)[['timestamp', 'net_pnl', 'equity']]
pnl.equity = pnl.equity.ffill()
pnl = pnl.set_index('timestamp').resample(sampling_frequency).last().reset_index()
pnl = pnl.dropna(subset = ['equity'])
pnl['ret'] = pnl.equity.pct_change()
return pnl
def plot(self,
contract_groups = None,
primary_indicators = None,
primary_indicators_dual_axis = None,
secondary_indicators = None,
secondary_indicators_dual_axis = None,
indicator_properties = None,
signals = None,
signal_properties = None,
pnl_columns = None,
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:
contract_groups (list of :obj:`ContractGroup`, optional): Contract groups to plot or None (default) for all
contract groups.
primary indicators (list of str, optional): List of indicators to plot in the main indicator section.
Default None (plot everything)
primary indicators (list of str, optional): List of indicators to plot in the secondary indicator section.
Default None (don't plot anything)
indicator_properties (dict of str : dict, optional): If set, we use the line color, line type indicated
for the given indicators
signals (list of str, optional): Signals to plot. Default None (plot everything).
plot_equity (bool, optional): If set, we plot the equity curve. Default is True
title (list of str, optional): Title of plot. Default None
figsize (tuple of int): Figure size. Default (20, 15)
date_range (tuple of str or np.datetime64, optional): Used to restrict the date range of the graph.
Default None
date_format (str, optional): 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 (str, optional): Downsampling frequency. 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. Default None.
trade_marker_properties (dict of str : tuple, optional): 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 (float, optional): Height (vertical) space between subplots. Default is 0.15
'''
date_range = strtup2date(date_range)
if contract_groups is None: contract_groups = self.contract_groups
if isinstance(contract_groups, ContractGroup): contract_groups = [contract_groups]
if pnl_columns is None: pnl_columns = ['equity']
for contract_group in contract_groups:
primary_indicator_names = [ind_name for ind_name in self.indicator_values[contract_group].__dict__ if hasattr(self.indicator_values[contract_group], ind_name)]
if primary_indicators:
primary_indicator_names = list(set(primary_indicator_names).intersection(primary_indicators))
secondary_indicator_names = []
if secondary_indicators:
secondary_indicator_names = secondary_indicators
signal_names = [sig_name for sig_name in self.signals.keys() if hasattr(self.signal_values[contract_group], sig_name)]
if signals:
signal_names = list(set(signal_names).intersection(signals))
primary_indicator_list = _get_time_series_list(self.timestamps, primary_indicator_names,
self.indicator_values[contract_group], indicator_properties)
secondary_indicator_list = _get_time_series_list(self.timestamps, secondary_indicator_names,
self.indicator_values[contract_group], indicator_properties)
signal_list = _get_time_series_list(self.timestamps, signal_names, self.signal_values[contract_group], signal_properties)
df_pnl_ = self.df_pnl(contract_group)
pnl_list = [TimeSeries(pnl_column, timestamps = df_pnl_.timestamp.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[contract_group], trade_marker_properties,
remove_missing_properties = True)
else:
trade_sets = trade_sets_by_reason_code(self._trades[contract_group])
primary_indicator_subplot = Subplot(primary_indicator_list + trade_sets,
secondary_y = primary_indicators_dual_axis,
height_ratio = 0.5, ylabel = 'Primary Indicators')
if len(secondary_indicator_list):
secondary_indicator_subplot = Subplot(secondary_indicator_list,
secondary_y = secondary_indicators_dual_axis,
height_ratio = 0.5, ylabel = 'Secondary 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', timestamps = df_pnl_.timestamp, values = position,
plot_type = 'filled_line')], ylabel = 'Position', height_ratio = 0.167)
title_full = title
if len(contract_groups) > 1:
if title is None: title = ''
title_full = f'{title} {contract_group.name}'
plot_list = []
if len(primary_indicator_list): plot_list.append(primary_indicator_subplot)
if len(secondary_indicator_list): plot_list.append(secondary_indicator_subplot)
if len(signal_list) : plot_list.append(signal_subplot)
if len(position): plot_list.append(pos_subplot)
if len(pnl_list): plot_list.append(pnl_subplot)
if not len(plot_list): return
plot = Plot(plot_list, figsize = figsize, date_range = date_range, date_format = date_format,
sampling_frequency = sampling_frequency,
title = title_full, hspace = hspace)
plot.draw()
def evaluate_returns(self, contract_group = None, plot = True, display_summary = True, float_precision = 4):
'''Returns a dictionary of common return metrics.
Args:
contract_group (:obj:`ContractGroup`, optional): Contract group to evaluate or None (default) for all contract groups
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(contract_group)
ev = compute_return_metrics(returns.timestamp.values, returns.ret.values, self.account.starting_equity)
if display_summary:
display_return_metrics(ev.metrics(), float_precision = float_precision)
if plot: plot_return_metrics(ev.metrics())
return ev.metrics()
def plot_returns(self, contract_group = None):
'''Display plots of equity, drawdowns and returns for the given contract group or for all contract groups if contract_group
is None (default)'''
if contract_group is None:
contract_groups = self.contract_groups()
else:
contract_groups = [contract_groups]
df_list = []
for contract_group in contract_groups:
df_list.append(self.df_returns(contract_group))
df = pd.concat(df_list, axis = 1)
ev = compute_return_metrics(returns.timestamp.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)}'