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turtle.py
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turtle.py
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'''
Richard Dennis
https://www.quantopian.com/posts/turtle-trading-strategy#:~:text=Turtle%20trading%20is%20a%20well,of%20rules%20is%20more%20intricate.&text=This%20is%20a%20pretty%20fundamental%20strategy%20and%20it%20seems%20to%20work%20well.
https://bigpicture.typepad.com/comments/files/turtlerules.pdf
https://github.com/myquant/strategy/blob/master/Turtle/info.md
https://zhuanlan.zhihu.com/p/161882477
trend following
entry: price > 20 day High
add: for every 0.5 ATR, up to 3 times
stop: < 2 ATR
stop: < 10 day Low
It makes investments in units: one price unit is one ATR; one size unit is 1% of asset / ATR.
'''
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
# set browser full width
from IPython.core.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
class Turtle(qt.StrategyBase):
def __init__(self, short_window=10, long_window=20):
super(Turtle, self).__init__()
self.short_window = short_window
self.long_window = long_window
self.buy_count = 0
self.buyprice = 0.0
self.current_time = None
def on_fill(self, fill_event):
super().on_fill(fill_event)
if fill_event.fill_size > 0: # buy
self.buyprice = fill_event.fill_price
def on_tick(self, tick_event):
self.current_time = tick_event.timestamp
# print('Processing {}'.format(self.current_time))
symbol = self.symbols[0]
df_hist = self._data_board.get_hist_price(symbol, tick_event.timestamp)
# wait for enough bars
if df_hist.shape[0] <= self.long_window:
return
current_price = df_hist.iloc[-1].Close
prev_price = df_hist.iloc[-2].Close
current_size = self._position_manager.get_position_size(symbol)
npv = self._position_manager.current_total_capital
don_high = max(df_hist.High.iloc[-self.long_window-1:-1]) # 20d high
don_low = max(df_hist.High.iloc[-self.short_window - 1:-1]) # 10d low
TR = pd.concat([df_hist.High.iloc[-15:-1]-df_hist.Low.iloc[-15:-1],
abs((df_hist.Close.iloc[-15:].shift(-1) - df_hist.High.iloc[-15:-1]).dropna()),
abs((df_hist.Close.iloc[-15:].shift(-1) - df_hist.Low.iloc[-15:-1]).dropna())], axis=1).max(axis=1)
ATR = np.average(TR)
# Long
if current_price > don_high and self.buy_count == 0:
# one unit is 1% of total risk asset
target_size = int(npv * 0.01 / ATR)
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'LONG ORDER SENT, price: {current_price:.2f}, don_high: {don_high:.2f}')
self.buy_count = 1
# add; This is for futures; may go beyond notional; leverage is set to 4
elif current_price > self.buyprice + 0.5 * ATR and self.buy_count > 0 and self.buy_count <= 3:
target_size = int(npv * 0.01 / ATR)
target_size += current_size # on top of current size
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'ADD LONG ORDER SENT, add time: {self.buy_count}, price: {current_price:.2f}, don_high: {don_high:.2f}')
self.buy_count += 1
# flat
elif current_price < don_low and self.buy_count > 0:
target_size = 0
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'FLAT ORDER SENT, price: {current_price:.2f}, don_low: {don_low:.2f}')
self.buy_count = 0
# flat, stop loss
elif current_price < (self.buyprice - 2 * ATR) and self.buy_count > 0:
target_size = 0
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'FLAT ORDER SENT, price: {current_price:.2f}, {self.buyprice:.2f}, 2ATR: {2 * ATR:.2f}')
self.buy_count = 0
def parameter_search(engine, tag, 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)
"""
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[tag] = target_value
if __name__ == '__main__':
do_optimize = False
run_in_jupyter = False
symbol = 'SPX'
benchmark = 'SPX'
datapath = os.path.join('../data/', f'{symbol}.csv')
data = qt.util.read_ohlcv_csv(datapath)
init_capital = 100_000.0
test_start_date = datetime(2010,1,1, 8, 30, 0, 0, pytz.timezone('America/New_York'))
test_end_date = datetime(2019,12,31, 6, 0, 0, 0, pytz.timezone('America/New_York'))
if do_optimize: # parallel parameter search
params_list = []
for sw_ in [10, 20, 30, 40, 50]:
for lw_ in [10, 20, 30, 40, 50]:
if sw_ >= lw_:
continue
params_list.append({'short_window': sw_, 'long_window': lw_})
target_name = 'Sharpe ratio'
manager = multiprocessing.Manager()
return_dict = manager.dict()
jobs = []
for params in params_list:
strategy = Turtle()
strategy.set_capital(init_capital)
strategy.set_symbols([symbol])
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, data)
strategy.set_params({'short_window': params['short_window'], 'long_window': params['long_window']})
backtest_engine.set_strategy(strategy)
tag = (params['short_window'], params['long_window'])
p = multiprocessing.Process(target=parameter_search, args=(backtest_engine, tag, 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 = Turtle()
strategy.set_capital(init_capital)
strategy.set_symbols([symbol])
# strategy.set_params(None)
# 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, data)
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'
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()