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r_breaker.py
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'''
https://programming.vip/docs/r-breaker-strategy-for-commodity-futures.html
https://github.com/myquant/strategy/blob/master/R-Breaker/info.md
The R-Breaker strategy was developed by Richard Saidenberg and published in 1994.
After that, it was ranked one of the top 10 most profitable trading strategies
by Futures Truth magazine in the United States for 15 consecutive years.
Simply put, the R-Breaker strategy is a support and resistance level strategy,
which calculates seven prices based on yesterday's highest, lowest and closing prices
The R - Breaker strategy draws grid - like price lines based on yesterday's prices and updates them once a day.
The support position and resistance position in technical analysis, and their roles can be converted to each other.
When the price successfully breaks up the resistance level, the resistance level becomes the support level;
when the price successfully breaks down the support level, the support level becomes the resistance level.
In the Forex trading system, the Pivot Points trading method is a classic trading strategy.
Pivot Points is a very simple resistance support system.
Based on yesterday's highest, lowest and closing prices, seven price points are calculated,
including one pivot point, three resistance levels and three support levels.
It is a day trading strategy, generally not overnight.
Here I'm using close price, performance is expected to deteriorate.
'''
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 RBreaker(qt.StrategyBase):
def __init__(self):
super(RBreaker, self).__init__()
self.price_entered = 0.0
self.stop_loss_price = 10
self.current_time = None
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)
# need yesterday's prices
if df_hist.shape[0] <= 1:
return
yesterday_open = df_hist.iloc[-2].Open
yesterday_high = df_hist.iloc[-2].High
yesterday_low = df_hist.iloc[-2].Low
yesterday_close = df_hist.iloc[-2].Close
# center or middle price
pivot = (yesterday_high + yesterday_close + yesterday_low) / 3 # pivot point
# r3 > r2 > r1
r1 = 2 * pivot - yesterday_low # Resistance Level 1; Reverse Selling price
r2 = pivot + (yesterday_high - yesterday_low) # Resistance Level 2; setup; Observed Sell Price
r3 = yesterday_high + 2 * (pivot - yesterday_low) # Resistance Level 3; break through buy
# s1 > s2 > s3
s1 = 2 * pivot - yesterday_high # Support 1; reverse buying
s2 = pivot - (yesterday_high - yesterday_low) # Support Position 2; setup; Observed Buy Price
s3 = yesterday_low - 2 * (yesterday_high - pivot) # Support 3; break through sell
today_high = df_hist.iloc[-1].High
today_low = df_hist.iloc[-1].Low
current_price = df_hist.iloc[-1].Close
current_size = self._position_manager.get_position_size(symbol)
npv = self._position_manager.current_total_capital
if current_size == 0: # If no position
if current_price > r3: # If the current price breaks through resistance level 3/highest, go long
target_size = int(npv / current_price)
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'BUY ORDER SENT, price: {current_price:.2f}, r1 {r1:.2f}, r2 {r2:.2f} r3 {r3:.2f}, s1 {s1:.2f} s2 {s2:.2f}, s3 {s3:.2f} size: {target_size}')
if current_price < s3: # If the current price break-through support level 3/lowest, go short
target_size = -int(npv / current_price)
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'SELL ORDER SENT, price: {current_price:.2f}, r1 {r1:.2f}, r2 {r2:.2f} r3 {r3:.2f}, s1 {s1:.2f} s2 {s2:.2f}, s3 {s3:.2f} size: {target_size}')
elif current_size > 0:
if (today_high > r2 and current_price < r1) or current_price < s3: # price reverses. flip from long to short
target_size = -int(npv / current_price)
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'FLIP TO SHORT ORDER SENT, price: {current_price:.2f}, r1 {r1:.2f}, r2 {r2:.2f} r3 {r3:.2f}, s1 {s1:.2f} s2 {s2:.2f}, s3 {s3:.2f} size: {target_size}')
elif current_size < 0:
if (today_low < s2 and current_price > s1) or current_price > r3: # price reverses, flip from short to long
target_size = int(npv / current_price * 0.95)
self.adjust_position(symbol, size_from=current_size, size_to=target_size, timestamp=self.current_time)
print(f'FLIP TO LONG ORDER SENT, price: {current_price:.2f}, r1 {r1:.2f}, r2 {r2:.2f} r3 {r3:.2f}, s1 {s1:.2f} s2 {s2:.2f}, s3 {s3:.2f} size: {target_size}')
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
pass
else:
strategy = RBreaker()
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()