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comdty_roll.py
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comdty_roll.py
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'''
Comdty roll according to roll schedule
'''
import os
import numpy as np
import pandas as pd
import pytz
from datetime import datetime, timezone
import multiprocessing
import talib
import quanttrader as qt
import matplotlib.pyplot as plt
import empyrical as ep
import pyfolio as pf
import futures_tools
import data_loader
# set browser full width
from IPython.core.display import display, HTML
pd.set_option('display.max_columns', None)
display(HTML("<style>.container { width:100% !important; }</style>"))
class ComdtyMonthlyRoll(qt.StrategyBase):
def __init__(self,
n_roll_ahead=0, # 0 is last day roll, 1 is penultimate day, and so on
n_rollout=0, # 0 is front month, 1 is second month, and so on
):
super(ComdtyMonthlyRoll, self).__init__()
self.n_roll_ahead = n_roll_ahead
self.n_rollout= n_rollout
self.sym = 'CL'
self.current_time = None
self.df_meta = data_loader.load_futures_meta(self.sym)
self.holding_contract = None
def on_tick(self, tick_event):
"""
front_contract decides when to roll
if not roll ==> if no holding_contract, buy rollout_contract; else do nothing
if roll ==> if no holding_contract, buy rollin contract (rollout+1); else sell rollout, buy rollin contract
"""
super().on_tick(tick_event)
self.current_time = tick_event.timestamp
#symbol = self.symbols[0]
#df_hist = self._data_board.get_hist_price(symbol, tick_event.timestamp)
df_time_idx = self._data_board.get_hist_time_index()
df_live_futures = futures_tools.get_futures_chain(meta_data = self.df_meta, asofdate = self.current_time.replace(tzinfo=None)) # remove tzinfo
# front_contract = df_live_futures.index[0]
rollout_contract = df_live_futures.index[self.n_rollout]
rollin_contract = df_live_futures.index[self.n_rollout+1]
exp_date = pytz.timezone('US/Eastern').localize(df_live_futures.Last_Trade_Date[0]) # front contract
dte = df_time_idx.searchsorted(exp_date) - df_time_idx.searchsorted(self.current_time) # 0 is expiry date
if self.n_roll_ahead < dte: # not ready to roll
if self.holding_contract is None: # empty
print(f'{self.current_time}, dte {dte}, buy {rollout_contract}')
self.adjust_position(rollout_contract, size_from=0, size_to=1, timestamp=self.current_time)
self.holding_contract = rollout_contract
else:
if self.holding_contract is None: # empty
print(f'{self.current_time}, dte {dte}, buy {rollin_contract}')
self.adjust_position(rollin_contract, size_from=0, size_to=1, timestamp=self.current_time)
self.holding_contract = rollin_contract
else:
if self.holding_contract == rollin_contract: # already rolled this month
pass
else:
print(f'{self.current_time}, dte {dte}, roll {rollout_contract} {rollin_contract}')
self.adjust_position(rollout_contract, size_from=1, size_to=0, timestamp=self.current_time)
self.adjust_position(rollin_contract, size_from=0, size_to=1, timestamp=self.current_time)
self.holding_contract = rollin_contract
def parameter_search(symbol, init_capital, sd, ed, df_data, params, target_name, return_dict):
"""
This function should be the same for all strategies.
The only reason not included in quanttrader is because of its dependency on pyfolio (to get perf_stats)
"""
strategy = ComdtyMonthlyRoll()
strategy.set_capital(init_capital)
strategy.set_symbols([symbol])
engine = qt.BacktestEngine(sd, ed)
engine.set_capital(init_capital) # capital or portfolio >= capital for one strategy
engine.add_data(symbol, df_data)
strategy.set_params({'n_roll_ahead': params['n_roll_ahead'], 'n_rollout': params['n_rollout']})
engine.set_strategy(strategy)
ds_equity, _, _ = engine.run()
try:
strat_ret = ds_equity.pct_change().dropna()
perf_stats_strat = pf.timeseries.perf_stats(strat_ret)
target_value = perf_stats_strat.loc[target_name] # first table in tuple
except KeyError:
target_value = 0
return_dict[(params['n_roll_ahead'], params['n_rollout'])] = target_value
if __name__ == '__main__':
do_optimize = False
run_in_jupyter = False
symbol = 'CL'
benchmark = None
init_capital = 100_000.0
df_future = data_loader.load_futures_hist_prices(symbol)
df_future.index = df_future.index.tz_localize('US/Eastern')
test_start_date = datetime(2019, 1, 1, 0, 0, 0, 0, pytz.timezone('US/Eastern'))
test_end_date = datetime(2021, 12, 30, 0, 0, 0, 0, pytz.timezone('US/Eastern'))
init_capital = 50.0
if do_optimize: # parallel parameter search
params_list = []
for n_roll_ahead in range(20):
for n_rollout in range(5):
params_list.append({'n_roll_ahead': n_roll_ahead, 'n_rollout': n_rollout})
target_name = 'Sharpe ratio'
manager = multiprocessing.Manager()
return_dict = manager.dict()
jobs = []
for params in params_list:
p = multiprocessing.Process(target=parameter_search, args=(symbol, init_capital, test_start_date, test_end_date, df_future, params, target_name, return_dict))
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
for k,v in return_dict.items():
print(k, v)
else:
strategy = ComdtyMonthlyRoll()
strategy.set_capital(init_capital)
strategy.set_symbols([symbol])
strategy.set_params({'n_roll_ahead': 0, 'n_rollout': 0})
# Create a Data Feed
backtest_engine = qt.BacktestEngine(test_start_date, test_end_date)
backtest_engine.set_capital(init_capital) # capital or portfolio >= capital for one strategy
backtest_engine.add_data(symbol, df_future)
backtest_engine.set_strategy(strategy)
ds_equity, df_positions, df_trades = backtest_engine.run()
# save to excel
qt.util.save_one_run_results('./output', ds_equity, df_positions, df_trades)
# ------------------------- Evaluation and Plotting -------------------------------------- #
strat_ret = ds_equity.pct_change().dropna()
strat_ret.name = 'strat'
# bm = qt.util.read_ohlcv_csv(os.path.join('../data/', f'{benchmark}.csv'))
# bm_ret = bm['Close'].pct_change().dropna()
# bm_ret.index = pd.to_datetime(bm_ret.index)
# bm_ret = bm_ret[strat_ret.index]
# bm_ret.name = 'benchmark'
bm_ret = strat_ret.copy()
bm_ret.name = 'benchmark'
perf_stats_strat = pf.timeseries.perf_stats(strat_ret)
perf_stats_all = perf_stats_strat
perf_stats_bm = pf.timeseries.perf_stats(bm_ret)
perf_stats_all = pd.concat([perf_stats_strat, perf_stats_bm], axis=1)
perf_stats_all.columns = ['Strategy', 'Benchmark']
drawdown_table = pf.timeseries.gen_drawdown_table(strat_ret, 5)
monthly_ret_table = ep.aggregate_returns(strat_ret, 'monthly')
monthly_ret_table = monthly_ret_table.unstack().round(3)
ann_ret_df = pd.DataFrame(ep.aggregate_returns(strat_ret, 'yearly'))
ann_ret_df = ann_ret_df.unstack().round(3)
print('-------------- PERFORMANCE ----------------')
print(perf_stats_all)
print('-------------- DRAWDOWN ----------------')
print(drawdown_table)
print('-------------- MONTHLY RETURN ----------------')
print(monthly_ret_table)
print('-------------- ANNUAL RETURN ----------------')
print(ann_ret_df)
if run_in_jupyter:
pf.create_full_tear_sheet(
strat_ret,
benchmark_rets=bm_ret,
positions=df_positions,
transactions=df_trades,
round_trips=False)
plt.show()
else:
f1 = plt.figure(1)
pf.plot_rolling_returns(strat_ret, factor_returns=bm_ret)
f1.show()
f2 = plt.figure(2)
pf.plot_rolling_volatility(strat_ret, factor_returns=bm_ret)
f2.show()
f3 = plt.figure(3)
pf.plot_rolling_sharpe(strat_ret)
f3.show()
f4 = plt.figure(4)
pf.plot_drawdown_periods(strat_ret)
f4.show()
f5 = plt.figure(5)
pf.plot_monthly_returns_heatmap(strat_ret)
f5.show()
f6 = plt.figure(6)
pf.plot_annual_returns(strat_ret)
f6.show()
f7 = plt.figure(7)
pf.plot_monthly_returns_dist(strat_ret)
plt.show()
f8 = plt.figure(8)
pf.create_position_tear_sheet(strat_ret, df_positions)
plt.show()
f9 = plt.figure(9)
pf.create_txn_tear_sheet(strat_ret, df_positions, df_trades)
plt.show()
f10 = plt.figure(10)
pf.create_round_trip_tear_sheet(strat_ret, df_positions, df_trades)
plt.show()