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)
 
 
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)
 
 
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])