optimizing_parameters.rst

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": {
        "securities": "stock",
        "matching_type": "current_bar",
        "start_date": "2015-01-01",
        "end_date": "2016-01-01",
        "stock_starting_cash": 100000,
        "benchmark": "000001.XSHE",
        "frequency": "1d",
        "strategy_file": "rqalpha/examples/golden_cross.py",
    },
    "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)


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 -sc 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)


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])