Dual Thrust Parameter Optimization

Code WonderTrader

Summary

This script configures WtCtaOptimizer to run parameter searches for a Dual Thrust strategy on a China Financial Futures Exchange index futures contract. It fixes inputs such as bar count, bar interval, lookback days, and contract, then varies the strategy coefficients across a range. Separate routines hold the core settings fixed while sweeping stop-loss ticks, take-profit ticks, or both together. The optimizer is configured with worker processes, CSV storage, a backtest configuration, date ranges, and output files for strategy markers and summary results.

The example illustrates how to structure parameter optimization around a base strategy and risk exits. However, it supplies configuration code rather than optimization findings: no selected parameter set, performance metrics, or robustness assessment is included. The script also contains two consecutive backtest-time configuration calls in the base routine, so the effective time-window behavior depends on the optimizer implementation. Optimization results would need validation against out-of-sample data and realistic costs before being treated as evidence of strategy quality.

Key ideas

The script uses WtCtaOptimizer to search Dual Thrust strategy parameters for an index futures contract.
It separates base coefficient optimization from stop-loss, take-profit, and combined exit parameter sweeps.
Fixed parameters, variable ranges, backtest data settings, date windows, and output files are configured programmatically.
The code provides no optimization results or evidence that any parameter set is profitable or robust.
The displayed base routine sets the backtest time twice, making the resulting window dependent on framework behavior.

Tags

Futures China markets Backtesting Risk management

Full text

# runOptmizer.py


```py
from wtpy.apps import WtCtaOptimizer

import sys
sys.path.append('../Strategies')
from DualThrust import StraDualThrust

def runBaseOptimizer():
    # 新建一个优化器，并设置最大工作进程数为8
    optimizer = WtCtaOptimizer(worker_num=8)

    # 设置要使用的策略，只需要传入策略类型即可，同时设置策略ID的前缀，用于区分每个策略的实例
    optimizer.set_strategy(StraDualThrust, "Dt_IF_")

    # 添加固定参数
    optimizer.add_fixed_param(name="barCnt", val=50)
    optimizer.add_fixed_param(name="period", val="m5")
    optimizer.add_fixed_param(name="days", val=30)
    optimizer.add_fixed_param(name="code", val="CFFEX.IF.HOT")

    # 添加预设范围的参数，即参数只能在预设列表中选择，适用于标的代码、周期等参数
    # optimizer.add_listed_param(name="code", val_list=["CFFEX.IF.HOT","CFFEX.IC.HOT"])

    # 添加可变参数，适用于一般数值类参数
    optimizer.add_mutable_param(name="k1", start_val=0.1, end_val=1.0, step_val=0.1, ndigits = 1)
    optimizer.add_mutable_param(name="k2", start_val=0.1, end_val=1.0, step_val=0.1, ndigits = 1)

    # 配置回测环境，主要是将直接回测的一些参数通过这种方式动态传递，优化器中会在每个子进程动态构造回测引擎
    optimizer.config_backtest_env(deps_dir='../common/', cfgfile='configbt.yaml', storage_type="csv", storage_path="../storage/")
    optimizer.config_backtest_time(start_time=201909100930, end_time=202009251500)
    optimizer.config_backtest_time(start_time=201909260930, end_time=202010121500)

    # 启动优化器
    optimizer.go(out_marker_file="strategies.json",out_summary_file="total_summary.csv")



def runStopLossOptimizer():
    # 新建一个优化器，并设置最大工作进程数为8
    optimizer = WtCtaOptimizer(worker_num=4)

    # 设置要使用的策略，只需要传入策略类型即可，同时设置策略ID的前缀，用于区分每个策略的实例
    optimizer.set_strategy(StraDualThrust, "Dt_IF_SL_")

    # 添加固定参数
    optimizer.add_fixed_param(name="barCnt", val=50)
    optimizer.add_fixed_param(name="period", val="m5")
    optimizer.add_fixed_param(name="days", val=30)
    optimizer.add_fixed_param(name="code", val="CFFEX.IF.HOT")
    optimizer.add_fixed_param(name="k1", val=0.4)
    optimizer.add_fixed_param(name="k2", val=0.4)

    # 添加可变参数，适用于一般数值类参数
    optimizer.add_mutable_param(name="slTicks", start_val=-10, end_val=0, step_val=0.2, ndigits = 1)

    # 配置回测环境，主要是将直接回测的一些参数通过这种方式动态传递，优化器中会在每个子进程动态构造回测引擎
    optimizer.config_backtest_env(deps_dir='../common/', cfgfile='configbt.yaml', storage_type="csv", storage_path="../storage/")
    optimizer.config_backtest_time(start_time=201909100930, end_time=202010121500)

    # 启动优化器
    optimizer.go(out_marker_file="strategies.json",out_summary_file="total_summary_sl.csv")

def runStopProfOptimizer():
    # 新建一个优化器，并设置最大工作进程数为8
    optimizer = WtCtaOptimizer(worker_num=4)

    # 设置要使用的策略，只需要传入策略类型即可，同时设置策略ID的前缀，用于区分每个策略的实例
    optimizer.set_strategy(StraDualThrust, "Dt_IF_SP_")

    # 添加固定参数
    optimizer.add_fixed_param(name="barCnt", val=50)
    optimizer.add_fixed_param(name="period", val="m5")
    optimizer.add_fixed_param(name="days", val=30)
    optimizer.add_fixed_param(name="code", val="CFFEX.IF.HOT")
    optimizer.add_fixed_param(name="k1", val=0.4)
    optimizer.add_fixed_param(name="k2", val=0.4)

    # 添加可变参数，适用于一般数值类参数
    optimizer.add_mutable_param(name="spTicks", start_val=0, end_val=500, step_val=5, ndigits = 0)

    # 配置回测环境，主要是将直接回测的一些参数通过这种方式动态传递，优化器中会在每个子进程动态构造回测引擎
    optimizer.config_backtest_env(deps_dir='../common/', cfgfile='configbt.yaml', storage_type="csv", storage_path="../storage/")
    optimizer.config_backtest_time(start_time=201909100930, end_time=202010121500)

    # 启动优化器
    optimizer.go(out_marker_file="strategies.json",out_summary_file="total_summary_sp.csv")

def runStopAllOptimizer():
    # 新建一个优化器，并设置最大工作进程数为8
    optimizer = WtCtaOptimizer(worker_num=4)

    # 设置要使用的策略，只需要传入策略类型即可，同时设置策略ID的前缀，用于区分每个策略的实例
    optimizer.set_strategy(StraDualThrust, "Dt_IF_ALL_")

    # 添加固定参数
    optimizer.add_fixed_param(name="barCnt", val=50)
    optimizer.add_fixed_param(name="period", val="m5")
    optimizer.add_fixed_param(name="days", val=30)
    optimizer.add_fixed_param(name="code", val="CFFEX.IF.HOT")
    optimizer.add_fixed_param(name="k1", val=0.4)
    optimizer.add_fixed_param(name="k2", val=0.4)

    # 添加可变参数，适用于一般数值类参数
    optimizer.add_mutable_param(name="slTicks", start_val=-30, end_val=-10, step_val=1, ndigits = 1)
    optimizer.add_mutable_param(name="spTicks", start_val=150, end_val=230, step_val=2, ndigits = 1)

    # 配置回测环境，主要是将直接回测的一些参数通过这种方式动态传递，优化器中会在每个子进程动态构造回测引擎
    optimizer.config_backtest_env(deps_dir='../common/', cfgfile='configbt.yaml', storage_type="csv", storage_path="../storage/")
    optimizer.config_backtest_time(start_time=201909100930, end_time=202010121500)

    # 启动优化器
    optimizer.go(out_marker_file="strategies.json",out_summary_file="total_summary_all.csv")



if __name__ == "__main__":
    runBaseOptimizer()
    kw = input('press any key to exit\n')
```

Shown in full with attribution under the source's licence. Licence: MIT

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.

Optimizing Dual Thrust Futures Strategy Parameters with WtCtaOptimizer

Summary

Key ideas

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