参数调优¶
对于以下双均线策略,我们希望对其进行参数调优,我们可以通过命令行参数 --extra-vars
或者通过配置 extra.context_vars
传递变量到 context
对象中。
from rqalpha.api import *
import talib
def init(context):
context.s1 = "000001.XSHE"
context.SHORTPERIOD = 20
context.LONGPERIOD = 120
def handle_bar(context, bar_dict):
prices = history_bars(context.s1, context.LONGPERIOD+1, '1d', 'close')
short_avg = talib.SMA(prices, context.SHORTPERIOD)
long_avg = talib.SMA(prices, context.LONGPERIOD)
cur_position = context.portfolio.positions[context.s1].quantity
shares = context.portfolio.cash / bar_dict[context.s1].close
if short_avg[-1] - long_avg[-1] < 0 and short_avg[-2] - long_avg[-2] > 0 and cur_position > 0:
order_target_value(context.s1, 0)
if short_avg[-1] - long_avg[-1] > 0 and short_avg[-2] - long_avg[-2] < 0:
order_shares(context.s1, shares)
通过函数调用传递参数¶
import concurrent.futures
import multiprocessing
from rqalpha import run
tasks = []
for short_period in range(3, 10, 2):
for long_period in range(30, 90, 5):
config = {
"extra": {
"context_vars": {
"SHORTPERIOD": short_period,
"LONGPERIOD": long_period,
},
"log_level": "error",
},
"base": {
"matching_type": "current_bar",
"start_date": "2015-01-01",
"end_date": "2016-01-01",
"benchmark": "000001.XSHE",
"frequency": "1d",
"strategy_file": "rqalpha/examples/golden_cross.py",
"accounts": {
"stock": 100000
}
},
"mod": {
"sys_progress": {
"enabled": True,
"show": True,
},
"sys_analyser": {
"enabled": True,
"output_file": "results/out-{short_period}-{long_period}.pkl".format(
short_period=short_period,
long_period=long_period,
)
},
},
}
tasks.append(config)
def run_bt(config):
run(config)
if __name__ == '__main__':
with concurrent.futures.ProcessPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
for task in tasks:
executor.submit(run_bt, task)
通过命令行传递参数¶
import os
import json
import concurrent.futures
import multiprocessing
tasks = []
for short_period in range(3, 10, 2):
for long_period in range(30, 90, 5):
extra_vars = {
"SHORTPERIOD": short_period,
"LONGPERIOD": long_period,
}
vars_params = json.dumps(extra_vars).encode("utf-8").decode("utf-8")
cmd = ("rqalpha run -fq 1d -f rqalpha/examples/golden_cross.py --start-date 2015-01-01 --end-date 2016-01-01 "
"-o results/out-{short_period}-{long_period}.pkl --account stock 100000 --progress -bm 000001.XSHE --extra-vars '{params}' ").format(
short_period=short_period,
long_period=long_period,
params=vars_params)
tasks.append(cmd)
def run_bt(cmd):
print(cmd)
os.system(cmd)
if __name__ == '__main__':
with concurrent.futures.ProcessPoolExecutor(max_workers=multiprocessing.cpu_count()) as executor:
for task in tasks:
executor.submit(run_bt, task)
分析批量回测结果¶
import glob
import pandas as pd
results = []
for name in glob.glob("results/*.pkl"):
result_dict = pd.read_pickle(name)
summary = result_dict["summary"]
results.append({
"name": name,
"annualized_returns": summary["annualized_returns"],
"sharpe": summary["sharpe"],
"max_drawdown": summary["max_drawdown"],
})
results_df = pd.DataFrame(results)
print("-" * 50)
print("Sort by sharpe")
print(results_df.sort_values("sharpe", ascending=False)[:10])
print("-" * 50)
print("Sort by annualized_returns")
print(results_df.sort_values("annualized_returns", ascending=False)[:10])