增加交易策略、交易指标、量化库代码等文件夹
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"ename": "ModuleNotFoundError",
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"evalue": "No module named 'vnpy_ctastrategy.strategies.vip09'",
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"output_type": "error",
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"traceback": [
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"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
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"Cell \u001b[1;32mIn[2], line 5\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtrader\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01moptimize\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m OptimizationSetting\n\u001b[0;32m 4\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy_ctastrategy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mbacktesting\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BacktestingEngine\n\u001b[1;32m----> 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mvnpy_ctastrategy\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mstrategies\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mvip09\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m vip09\n\u001b[0;32m 6\u001b[0m \u001b[38;5;66;03m# from vip09 import vip09\u001b[39;00m\n",
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"\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'vnpy_ctastrategy.strategies.vip09'"
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]
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}
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],
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"source": [
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"from datetime import datetime\n",
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"\n",
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"from vnpy.trader.optimize import OptimizationSetting\n",
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"from vnpy_ctastrategy.backtesting import BacktestingEngine\n",
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"# from vnpy_ctastrategy.strategies.vip09 import vip09\n",
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"from vip09 import vip09"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"engine = BacktestingEngine()\n",
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"engine.set_parameters(\n",
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" vt_symbol=\"rb00.SHFE\",\n",
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" interval=\"1m\",\n",
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" start=datetime(2020, 1, 1),\n",
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" end=datetime(2024, 3, 21),\n",
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" rate=1.5/10000,\n",
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" slippage=1,\n",
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" size=10,\n",
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" pricetick=1,\n",
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" capital=1_000_00,\n",
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")\n",
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"engine.add_strategy(vip09, {})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"engine.load_data()\n",
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"engine.run_backtesting()\n",
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"df = engine.calculate_result()\n",
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"engine.calculate_statistics()\n",
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"engine.show_chart()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"'''用于参数优化'''\n",
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"# setting = OptimizationSetting()\n",
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"# setting.set_target(\"sharpe_ratio\")\n",
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"# setting.add_parameter(\"Length\", 5, 100, 5)\n",
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"# setting.add_parameter(\"N\", 5, 100, 5)\n",
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"# setting.add_parameter(\"X\", 1, 20, 1)\n",
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"# setting.add_parameter(\"TS\", 5, 100, 5)\n",
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"# from multiprocessing import cpu_count\n",
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"# # 获取 CPU 核心数量\n",
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"# num_cores = cpu_count()\n",
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"# print(f\"获取 CPU 核心数量:\",round(num_cores/2))\n",
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"# engine.run_ga_optimization(setting, max_workers=round(num_cores/2))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"scrolled": false
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},
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"outputs": [],
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"source": [
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"#engine.run_bf_optimization(setting)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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},
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"vscode": {
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"interpreter": {
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"hash": "1b43cb0bd93d5abbadd54afed8252f711d4681fe6223ad6b67ffaee289648f85"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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@@ -0,0 +1,238 @@
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from vnpy_ctastrategy import (
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CtaTemplate,
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TargetPosTemplate,
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StopOrder,
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TickData,
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BarData,
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TradeData,
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OrderData,
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BarGenerator,
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ArrayManager,
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)
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import pandas as pd
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class vip09(TargetPosTemplate):
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""""""
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author = "松鼠Quant"
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Length = 30
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N=5
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X=10
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TS = 45
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lots=1
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current_bar = 0
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Highup=0
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Lowdown=0
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HigherAfterEntry = float('inf')
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LowerAfterEntry = -float('inf')
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liQKA = 0
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DliqPoint = 0
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KliqPoint = 0
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OBVValue=[0]
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MAOBV=[0]
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M2=[0]
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cond1=[0]*2
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cond2=[0]*2
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cond3=[0]*2
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cond4=[0]*2
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cond5=[0]*2
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kong_cond1=[0]*2
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kong_cond2=[0]*2
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kong_cond3=[0]*2
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kong_cond4=[0]*2
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kong_cond5=[0]*2
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MA1=0
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MA2=0
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MA3=0
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parameters = ["Length",'N','X',"TS",'lots']
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variables = ["HigherAfterEntry","LowerAfterEntry","liQKA","DliqPoint","KliqPoint"]
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def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
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""""""
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super().__init__(cta_engine, strategy_name, vt_symbol, setting)
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#df: pd.DataFrame = pd.read_csv(r"index_contract.csv")
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#self.contracts_sizes = {row.vt_symbol.split('.')[0]: row.contract_size for _, row in df.iterrows()}
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self.bg = BarGenerator(self.on_bar)
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self.am = ArrayManager(200)
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self.pos=0
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def on_init(self):
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"""
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Callback when strategy is inited.
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"""
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self.write_log("策略初始化")
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self.load_bar(1)
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def on_start(self):
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"""
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Callback when strategy is started.
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"""
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self.write_log("策略启动")
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def on_stop(self):
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"""
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Callback when strategy is stopped.
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"""
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self.write_log("策略停止")
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def on_tick(self, tick: TickData):
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"""
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Callback of new tick data update.
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"""
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self.bg.update_tick(tick)
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def on_bar(self, bar: BarData):
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"""
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Callback of new bar data update.
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"""
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am = self.am
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am.update_bar(bar)
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if not am.inited:
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return
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# 如果历史数据还没有初始化完毕，则直接返回
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# 注意：am 是交易系统中的一个数据管理器，可能用于管理历史数据
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# bar 是当前的K线数据，其中包含了开盘价、最高价、最低价、收盘价和成交量等信息
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if self.current_bar == 0:
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self.OBVValue = [0]
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else:
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if am.close_array[-1] > am.close_array[-2]:
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self.OBVValue += [self.OBVValue[-1] + am.volume_array[-1]]
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elif am.close_array[-1] < am.close_array[-2]:
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self.OBVValue += [self.OBVValue[-1] - am.volume_array[-1]]
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# 根据收盘价的涨跌更新 OBV 值
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# 如果当前收盘价大于昨日收盘价，则 OBV 值加上当前成交量
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# 如果当前收盘价小于昨日收盘价，则 OBV 值减去当前成交量
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# 注意：OBV 是一种基于成交量变化来反映买卖力量的技术指标，用于判断市场的买卖压力
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self.current_bar=self.current_bar+1 #Bar线计数
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#print(bar.datetime,"current_bar:",self.current_bar)
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# 记录当前Bar的计数器值，并打印当前Bar的时间和计数器值
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if(self.current_bar<self.Length*3):
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return
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# 如果当前Bar的计数器值小于三倍的Length，则直接返回
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# 这可能是因为需要一定数量的历史数据才能计算出所需的指标值
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# print(f'{self.OBVValue},OBVValue')
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self.MA1=am.ema(self.Length,array=True)
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self.MA2=am.ema(self.Length*2,array=True)
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self.MA3=am.ema(self.Length*3,array=True)
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# 计算三条指定长度的指数移动平均线（EMA）
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# 分别记为MA1、MA2和MA3
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self.MAOBV += [(sum(self.OBVValue[-self.Length:]) / self.Length)]
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self.M2 += [(sum(self.OBVValue[-self.X:]) / self.X)]
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# 计算 OBV 和 M2 的移动平均值
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self.cond1=self.MA1 if am.close_array[-1] > self.MA1[-2] else self.cond1
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self.cond2=self.MA1 if self.M2[-1] > self.M2[-2] else self.cond2
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self.cond3=self.MA2 if am.close_array[-1] > self.MA2[-1] and self.MAOBV[-1] > self.MAOBV[-2] else self.cond3
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self.cond4=self.MA2 if am.close_array[-1] > self.MA2[-1] else self.cond4
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self.cond5=self.MA3 if am.close_array[-1] > self.MA3[-1] and self.MAOBV[-1] > self.MAOBV[-2] else self.cond5
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# 根据条件更新多头条件指标cond1到cond5
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# 根据条件更新空头条件指标kong_cond1到kong_cond5
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self.kong_cond1=self.MA1 if am.close_array[-1] < self.MA1[-2] else self.kong_cond1
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self.kong_cond2=self.MA1 if self.M2[-1] < self.M2[-2] else self.kong_cond2
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self.kong_cond3=self.MA2 if am.close_array[-1] < self.MA2[-1] and self.MAOBV[-1] < self.MAOBV[-2] else self.kong_cond3
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self.kong_cond4=self.MA2 if am.close_array[-1] < self.MA2[-1] else self.kong_cond4
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self.kong_cond5=self.MA3 if am.close_array[-1] < self.MA3[-1] and self.MAOBV[-1] < self.MAOBV[-2] else self.kong_cond5
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# 更新空头条件指标kong_cond1到kong_cond5
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self.Highup=max(am.high_array[-self.N:-1])
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self.Lowdown=min(am.low_array[-self.N:-1])
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# 计算最近N个Bar的最高价和最低价，分别记为Highup和Lowdown
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Long_line=am.close_array[-1]>self.MA3[-1] and self.MAOBV[-1]>self.MAOBV[-2]
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short_line=am.close_array[-1]<self.MA3[-1] and self.MAOBV[-1]<self.MAOBV[-2]
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#print(f'{self.MAOBV[-1]},MAOBV')
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#print(f'{Long_line},Long_line')
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#print(f'{short_line},short_line')
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if Long_line:
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if self.cond4[-1]>self.cond5[-2] and self.cond5[-1]>self.cond5[-2] and self.cond4[-1]>self.cond4[-2] and self.cond3[-1]>self.cond3[-2] and self.cond2[-1]>self.cond2[-2] and self.cond1[-1]>self.cond1[-2] :
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if self.pos!=1 and bar.high_price>=self.Highup :
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if self.pos == 0:
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self.buy(bar.close_price, self.lots)
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elif self.pos < 0:
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self.cover(bar.close_price, self.lots)
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self.buy(bar.close_price, self.lots)
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self.LowerAfterEntry = bar.close_price
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if short_line:
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if self.kong_cond4[-1]<self.kong_cond5[-1] and self.kong_cond5[-1]<self.kong_cond5[-2] and self.kong_cond4[-1]<self.kong_cond4[-2] and self.kong_cond3[-1]<self.kong_cond3[-2] and self.kong_cond2[-1]<self.kong_cond2[-2] and self.kong_cond1[-1]<self.kong_cond1[-2]:
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if self.pos!=-1 and bar.low_price<=self.Lowdown :
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if self.pos == 0:
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self.short(bar.close_price, self.lots)
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elif self.pos > 0:
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self.sell(bar.close_price, self.lots)
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self.short(bar.close_price, self.lots)
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self.HigherAfterEntry = bar.close_price
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#记录入场后的最高价和最低价
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if self.pos>0:
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#self.HigherAfterEntry = HigherAfterEntry
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self.LowerAfterEntry = max(self.LowerAfterEntry, bar.low_price)
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elif self.pos<0:
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self.HigherAfterEntry = min(self.HigherAfterEntry, bar.high_price)
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#self.LowerAfterEntry = LowerAfterEntry
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if self.pos == 0:
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self.liQKA = 1
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else:
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self.liQKA = self.liQKA - 0.1
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self.liQKA = max(self.liQKA, 0.5)
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if self.pos>0:
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DliqPoint = self.LowerAfterEntry - ((bar.open_price)*self.TS/1000)*self.liQKA
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#print(bar.datetime,"DliqPoint",DliqPoint,"low_price",bar.low_price,"self.LowerAfterEntry",self.LowerAfterEntry)
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if self.pos<0:
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KliqPoint = self.HigherAfterEntry + ((bar.open_price)*self.TS/1000)*self.liQKA
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#print(bar.datetime,"KliqPoint",KliqPoint,"high_price",bar.high_price,"self.HigherAfterEntry",self.HigherAfterEntry)
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if (self.pos>0 and bar.low_price <= DliqPoint and DliqPoint>0):
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#多头出场
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self.sell(bar.close_price, self.lots)
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elif (self.pos<0 and bar.high_price > KliqPoint and KliqPoint>0):
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#空头出场
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self.cover(bar.close_price, self.lots)
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self.current_bar += 1
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self.put_event()
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def on_trade(self, trade: TradeData):
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"""
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Callback of new trade data update.
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"""
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self.put_event()
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def on_stop_order(self, stop_order: StopOrder):
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"""
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Callback of stop order update.
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"""
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pass
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