def test_account():
#if __name__ == "__main__":
from pyqstrat.pq_types import Contract, ContractGroup, Trade
from pyqstrat.orders import MarketOrder
import math
def get_close_price(contract, timestamps, idx, strategy_context):
if contract.symbol == "IBM":
price = idx + 10.1
elif contract.symbol == "MSFT":
price = idx + 15.3
else:
raise Exception(f'unknown contract: {contract}')
return price
ContractGroup.clear()
Contract.clear()
ibm_cg = ContractGroup.create('IBM')
msft_cg = ContractGroup.create('MSFT')
ibm_contract = Contract.create('IBM', contract_group = ibm_cg)
msft_contract = Contract.create('MSFT', contract_group = msft_cg)
timestamps = np.array(['2018-01-01 09:00', '2018-01-02 08:00', '2018-01-02 09:00', '2018-01-05 13:35'], dtype = 'M8[m]')
account = Account([ibm_cg, msft_cg], timestamps, get_close_price, None)
#account = Account([Contract(symbol)], timestamps, get_close_price)
trade_1 = Trade(ibm_contract, np.datetime64('2018-01-02 08:00'), 10, 10.1, commission = 0.01,
order = MarketOrder(ibm_contract, np.datetime64('2018-01-01 09:00'), 10))
trade_2 = Trade(ibm_contract, np.datetime64('2018-01-02 09:00'), -20, 15.1, commission = 0.02,
order = MarketOrder(ibm_contract, np.datetime64('2018-01-01 09:00'), -20))
trade_3 = Trade(msft_contract, timestamps[1], 20, 13.2, commission = 0.04, order = MarketOrder(msft_contract, timestamps[1], 15))
trade_4 = Trade(msft_contract, timestamps[2], 20, 16.2, commission = 0.05, order = MarketOrder(msft_contract, timestamps[2], 20))
account.add_trades([trade_1, trade_2, trade_3, trade_4])
account.calc(np.datetime64('2018-01-05 13:35'))
assert(len(account.df_trades()) == 4)
assert(len(account.df_pnl()) == 6)
assert(np.allclose(np.array([9.99, 61.96, 79.97, 103.91, 69.97, 143.91]),
account.df_pnl().net_pnl.values, rtol = 0))
assert(np.allclose(np.array([10, 20, -10, 40, -10, 40]), account.df_pnl().position.values, rtol = 0))
assert(np.allclose(np.array([1000000. , 1000183.88, 1000213.88]), account.df_account_pnl().equity.values, rtol = 0))
def test_strategy() -> Strategy:
import math
import numpy as np
import pandas as pd
import os
from types import SimpleNamespace
from pyqstrat.pq_types import Contract, ContractGroup, Trade
try:
# If we are running from unit tests
ko_file_path = os.path.dirname(os.path.realpath(__file__)) + '/notebooks/support/coke_15_min_prices.csv.gz'
pep_file_path = os.path.dirname(os.path.realpath(__file__)) + '/notebooks/support/pepsi_15_min_prices.csv.gz'
except NameError:
ko_file_path = '../notebooks/support/coke_15_min_prices.csv.gz'
pep_file_path = '../notebooks/support/pepsi_15_min_prices.csv.gz'
ko_prices = pd.read_csv(ko_file_path)
pep_prices = pd.read_csv(pep_file_path)
ko_prices['timestamp'] = pd.to_datetime(ko_prices.date)
pep_prices['timestamp'] = pd.to_datetime(pep_prices.date)
end_time = '2019-01-30 12:00'
ko_prices = ko_prices.query(f'timestamp <= "{end_time}"')
pep_prices = pep_prices.query(f'timestamp <= "{end_time}"')
timestamps = ko_prices.timestamp.values
ratio = ko_prices.c / pep_prices.c
def zscore_indicator(contract_group: ContractGroup,
timestamps: np.ndarray,
indicators: SimpleNamespace,
strategy_context: StrategyContextType) -> np.ndarray: # simple moving average
ratio = indicators.ratio
r = pd.Series(ratio).rolling(window=130)
mean = r.mean()
std = r.std(ddof=0)
zscore = (ratio - mean) / std
zscore = np.nan_to_num(zscore)
return zscore
def pair_strategy_signal(contract_group: ContractGroup,
timestamps: np.ndarray,
indicators: SimpleNamespace,
parent_signals: SimpleNamespace,
strategy_context: StrategyContextType) -> np.ndarray:
# We don't need any indicators since the zscore is already part of the market data
zscore = indicators.zscore
signal = np.where(zscore > 1, 2, 0)
signal = np.where(zscore < -1, -2, signal)
signal = np.where((zscore > 0.5) & (zscore < 1), 1, signal)
signal = np.where((zscore < -0.5) & (zscore > -1), -1, signal)
if contract_group.name == 'PEP': signal = -1. * signal
return signal
def pair_entry_rule(contract_group: ContractGroup,
i: int,
timestamps: np.ndarray,
indicators: SimpleNamespace,
signal: np.ndarray,
account: Account,
strategy_context: StrategyContextType) -> Sequence[Order]:
timestamp = timestamps[i]
assert(math.isclose(account.position(contract_group, timestamp), 0))
signal_value = signal[i]
risk_percent = 0.1
orders = []
symbol = contract_group.name
contract = contract_group.get_contract(symbol)
if contract is None: contract = Contract.create(symbol, contract_group=contract_group)
curr_equity = account.equity(timestamp)
order_qty = np.round(curr_equity * risk_percent / indicators.c[i] * np.sign(signal_value))
print(f'order_qty: {order_qty} curr_equity: {curr_equity} timestamp: {timestamp}'
f' risk_percent: {risk_percent} indicator: {indicators.c[i]} signal_value: {signal_value}')
reason_code = ReasonCode.ENTER_LONG if order_qty > 0 else ReasonCode.ENTER_SHORT
orders.append(MarketOrder(contract, timestamp, order_qty, reason_code=reason_code))
return orders
def pair_exit_rule(contract_group: ContractGroup,
i: int,
timestamps: np.ndarray,
indicators: SimpleNamespace,
signal: np.ndarray,
account: Account,
strategy_context: StrategyContextType) -> Sequence[Order]:
timestamp = timestamps[i]
curr_pos = account.position(contract_group, timestamp)
assert(not math.isclose(curr_pos, 0))
signal_value = signal[i]
orders = []
symbol = contract_group.name
contract = contract_group.get_contract(symbol)
if contract is None: contract = Contract.create(symbol, contract_group=contract_group)
if (curr_pos > 0 and signal_value == -1) or (curr_pos < 0 and signal_value == 1):
order_qty = -curr_pos
reason_code = ReasonCode.EXIT_LONG if order_qty < 0 else ReasonCode.EXIT_SHORT
orders.append(MarketOrder(contract, timestamp, order_qty, reason_code=reason_code))
return orders
def market_simulator(orders: Sequence[Order],
i: int,
timestamps: np.ndarray,
indicators: Dict[ContractGroup, SimpleNamespace],
signals: Dict[ContractGroup, SimpleNamespace],
strategy_context: StrategyContextType) -> Sequence[Trade]:
trades = []
timestamp = timestamps[i]
for order in orders:
trade_price = np.nan
cgroup = order.contract.contract_group
ind = indicators[cgroup]
o, h, l = ind.o[i], ind.h[i], ind.l[i] # noqa: E741 # l is ambiguous
assert isinstance(order, MarketOrder), f'Unexpected order type: {order}'
trade_price = 0.5 * (o + h) if order.qty > 0 else 0.5 * (o + l)
if np.isnan(trade_price): continue
trade = Trade(order.contract, order, timestamp, order.qty, trade_price, commission=0, fee=0)
order.status = 'filled'
print(f'trade: {trade}')
trades.append(trade)
return trades
def get_price(contract: Contract, timestamps: np.ndarray, i: int, strategy_context: StrategyContextType) -> float:
if contract.symbol == 'KO':
return strategy_context.ko_price[i]
elif contract.symbol == 'PEP':
return strategy_context.pep_price[i]
raise Exception(f'Unknown contract: {contract}')
Contract.clear()
ContractGroup.clear()
ko_contract_group = ContractGroup.create('KO')
pep_contract_group = ContractGroup.create('PEP')
strategy_context = SimpleNamespace(ko_price=ko_prices.c.values, pep_price=pep_prices.c.values)
strategy = Strategy(timestamps, [ko_contract_group, pep_contract_group], get_price, trade_lag=1, strategy_context=strategy_context)
for tup in [(ko_contract_group, ko_prices), (pep_contract_group, pep_prices)]:
for column in ['o', 'h', 'l', 'c']:
strategy.add_indicator(column, tup[1][column], contract_groups=[tup[0]])
strategy.add_indicator('ratio', ratio)
strategy.add_indicator('zscore', zscore_indicator, depends_on=['ratio'])
strategy.add_signal('pair_strategy_signal', pair_strategy_signal, depends_on_indicators=['zscore'])
# ask pqstrat to call our trading rule when the signal has one of the values [-2, -1, 1, 2]
strategy.add_rule('pair_entry_rule', pair_entry_rule,
signal_name='pair_strategy_signal', sig_true_values=[-2, 2], position_filter='zero')
strategy.add_rule('pair_exit_rule', pair_exit_rule,
signal_name='pair_strategy_signal', sig_true_values=[-1, 1], position_filter='nonzero')
strategy.add_market_sim(market_simulator)
strategy.run_indicators()
strategy.run_signals()
strategy.run_rules()
metrics = strategy.evaluate_returns(plot=False, display_summary=False, return_metrics=True)
assert metrics is not None
assert(round(metrics['gmean'], 6) == -0.062878)
assert(round(metrics['sharpe'], 4) == -9.7079) # -7.2709)
assert(round(metrics['mdd_pct'], 6) == 0.002574) # -0.002841)
return strategy