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增加交易策略、交易指标、量化库代码等文件夹

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2025-04-27 15:54:09 +08:00
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6970
1.交易策略/1.CTA策略/1.松鼠策略/5.松鼠SF13_基于抄底摸顶思路的震荡策略/使用文稿/VNPY/rb888.csv Normal file
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1.交易策略/1.CTA策略/1.松鼠策略/5.松鼠SF13_基于抄底摸顶思路的震荡策略/使用文稿/VNPY/vip13.ipynb Normal file
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{
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime\n",
"\n",
"from vnpy.trader.optimize import OptimizationSetting\n",
"from vnpy_ctastrategy.backtesting import BacktestingEngine\n",
"# from vnpy_ctastrategy.strategies.vip13 import vip13\n",
"from vip13 import vip13"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"engine = BacktestingEngine()\n",
"engine.set_parameters(\n",
" vt_symbol=\"rbJQ00.SHFE\",\n",
" interval=\"1h\",\n",
" start=datetime(2020, 1, 1),\n",
" end=datetime(2024, 3, 21),\n",
" rate=1.5/10000,\n",
" slippage=1,\n",
" size=10,\n",
" pricetick=1,\n",
" capital=1_000_00,\n",
")\n",
"engine.add_strategy(vip13, {})"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2025-01-30 22:14:21.167367\t开始加载历史数据\n",
"2025-01-30 22:14:21.167367\t加载进度：# [0%]\n",
"2025-01-30 22:14:21.167367\t加载进度：# [10%]\n",
"2025-01-30 22:14:21.167367\t加载进度：## [20%]\n",
"2025-01-30 22:14:21.167367\t加载进度：### [30%]\n",
"2025-01-30 22:14:21.167367\t加载进度：#### [40%]\n",
"2025-01-30 22:14:21.167367\t加载进度：##### [50%]\n",
"2025-01-30 22:14:21.167367\t加载进度：###### [60%]\n",
"2025-01-30 22:14:21.167367\t加载进度：####### [70%]\n",
"2025-01-30 22:14:21.167367\t加载进度：######## [80%]\n",
"2025-01-30 22:14:21.167367\t加载进度：######### [90%]\n",
"2025-01-30 22:14:21.167367\t加载进度：########## [100%]\n",
"2025-01-30 22:14:21.167367\t历史数据加载完成，数据量：0\n",
"2025-01-30 22:14:21.167367\t策略初始化完成\n",
"2025-01-30 22:14:21.167367\t开始回放历史数据\n",
"2025-01-30 22:14:21.167367\t历史数据回放结束\n",
"2025-01-30 22:14:21.167367\t开始计算逐日盯市盈亏\n",
"2025-01-30 22:14:21.167367\t回测成交记录为空\n"
]
},
{
"ename": "KeyError",
"evalue": "\"None of ['date'] are in the columns\"",
"output_type": "error",
"traceback": [
"\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[1;32mIn[10], line 3\u001b[0m\n\u001b[0;32m 1\u001b[0m engine\u001b[38;5;241m.\u001b[39mload_data()\n\u001b[0;32m 2\u001b[0m engine\u001b[38;5;241m.\u001b[39mrun_backtesting()\n\u001b[1;32m----> 3\u001b[0m df \u001b[38;5;241m=\u001b[39m \u001b[43mengine\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcalculate_result\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 4\u001b[0m engine\u001b[38;5;241m.\u001b[39mcalculate_statistics()\n\u001b[0;32m 5\u001b[0m engine\u001b[38;5;241m.\u001b[39mshow_chart()\n",
"File \u001b[1;32mc:\\veighna_studio\\lib\\site-packages\\vnpy_ctastrategy\\backtesting.py:288\u001b[0m, in \u001b[0;36mBacktestingEngine.calculate_result\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 285\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, value \u001b[38;5;129;01min\u001b[39;00m daily_result\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__dict__\u001b[39m\u001b[38;5;241m.\u001b[39mitems():\n\u001b[0;32m 286\u001b[0m results[key]\u001b[38;5;241m.\u001b[39mappend(value)\n\u001b[1;32m--> 288\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdaily_df \u001b[38;5;241m=\u001b[39m \u001b[43mDataFrame\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresults\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_index\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdate\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m 290\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moutput(_(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m逐日盯市盈亏计算完成\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[0;32m 291\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdaily_df\n",
"File \u001b[1;32mc:\\veighna_studio\\lib\\site-packages\\pandas\\core\\frame.py:6109\u001b[0m, in \u001b[0;36mDataFrame.set_index\u001b[1;34m(self, keys, drop, append, inplace, verify_integrity)\u001b[0m\n\u001b[0;32m 6106\u001b[0m missing\u001b[38;5;241m.\u001b[39mappend(col)\n\u001b[0;32m 6108\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m missing:\n\u001b[1;32m-> 6109\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNone of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmissing\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m are in the columns\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 6111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m inplace:\n\u001b[0;32m 6112\u001b[0m frame \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\n",
"\u001b[1;31mKeyError\u001b[0m: \"None of ['date'] are in the columns\""
]
}
],
"source": [
"engine.load_data()\n",
"engine.run_backtesting()\n",
"df = engine.calculate_result()\n",
"engine.calculate_statistics()\n",
"engine.show_chart()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# '''用于参数优化'''\n",
"# setting = OptimizationSetting()\n",
"# setting.set_target(\"sharpe_ratio\")\n",
"# setting.add_parameter(\"boll_n\", 5, 60, 5)\n",
"# setting.add_parameter(\"offset\", 0.5,3, 0.5)\n",
"# setting.add_parameter(\"trailing_stop_rate\", 5, 100, 5)\n",
"# setting.add_parameter(\"x\", 5, 80, 5)\n",
"# from multiprocessing import cpu_count\n",
"# # 获取 CPU 核心数量\n",
"# num_cores = cpu_count()\n",
"# print(f\"获取 CPU 核心数量:\",round(num_cores/2))\n",
"# engine.run_ga_optimization(setting, max_workers=round(num_cores/2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": false
},
"outputs": [],
"source": [
"# engine.run_bf_optimization(setting)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.10.9"
},
"vscode": {
"interpreter": {
"hash": "1b43cb0bd93d5abbadd54afed8252f711d4681fe6223ad6b67ffaee289648f85"
}
}
},
"nbformat": 4,
"nbformat_minor": 2
}

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1.交易策略/1.CTA策略/1.松鼠策略/5.松鼠SF13_基于抄底摸顶思路的震荡策略/使用文稿/VNPY/vip13.py Normal file
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from vnpy_ctastrategy import (
CtaTemplate,
TargetPosTemplate,
StopOrder,
TickData,
BarData,
TradeData,
OrderData,
BarGenerator,
ArrayManager,
)
import numpy as np
class vip13(CtaTemplate):
author = "松鼠Quant"
boll_n = 20
offset = 1.5
trailing_stop_rate =60
x = 30
rsi_n = 14
over_sold = 30
over_bought = 70
parameters = ["boll_n", "offset", "trailing_stop_rate", "x"]
variables = ["lots", "up_line", "down_line", "mid_line", "band", "rsi_value", "kg", "highest_low_after_entry", "lowest_high_after_entry", "rsi_n", "over_sold", "over_bought"]
def __init__(self, cta_engine, strategy_name, vt_symbol, setting):
""""""
super().__init__(cta_engine, strategy_name, vt_symbol, setting)
self.lots = 0
self.up_line = 0
self.down_line = 0
self.mid_line = 0
self.band = 0
self.rsi_value = 0
self.kaiduo = False
self.kaikong = False
self.kg = 0
self.highest_low_after_entry = 0
self.lowest_high_after_entry = 0
self.bg = BarGenerator(self.on_bar)
self.am = ArrayManager(300)
self.pos=0
def on_init(self):
self.write_log("策略初始化")
self.load_bar(10)
def on_start(self):
self.write_log("策略启动")
def on_stop(self):
self.write_log("策略停止")
def on_tick(self, tick: TickData):
pass
def on_bar(self, bar: BarData):
self.am.update_bar(bar)
if not self.am.inited:
return
self.lots = 1
self.calculate_boll()
self.calculate_rsi()
self.kaikong = self.rsi_value > self.over_bought and self.am.close[-1] < self.up_line
self.kaiduo = self.rsi_value < self.over_sold and self.am.close[-1] > self.down_line
if self.kaiduo:
self.kg = 1
self.hd = self.am.high[-self.x:-1].max()
if self.kaikong:
self.kg = -1
self.ld = self.am.low[-self.x:-1].min()
if self.pos != 1 and self.kg == 1 and bar.close_price >= self.hd :
self.buy(bar.close_price, self.lots)
self.highest_low_after_entry = bar.close_price
self.pos=1
if self.pos != -1 and self.kg == -1 and bar.close_price <= self.ld:
self.short(bar.close_price, self.lots)
self.lowest_high_after_entry = bar.close_price
self.pos=-1
self.trailing_stop(bar)
self.put_event()
def calculate_boll(self):
self.mid_line = self.am.sma(self.boll_n)
self.band = self.am.std(self.boll_n)
self.up_line = self.mid_line + self.offset * self.band
self.down_line = self.mid_line - self.offset * self.band
def calculate_rsi(self):
self.rsi_value = self.am.rsi(self.rsi_n)
def trailing_stop(self, bar: BarData):
if self.pos > 0:
self.highest_low_after_entry = max(self.highest_low_after_entry, bar.low_price)
dliq_point = self.highest_low_after_entry - bar.open_price * (self.trailing_stop_rate/1000)
if bar.close_price < dliq_point:
self.sell(bar.close_price, self.lots)
self.pos=0
if self.pos < 0:
self.lowest_high_after_entry = min(self.lowest_high_after_entry, bar.high_price)
kliq_point = self.lowest_high_after_entry + bar.open_price * (self.trailing_stop_rate/1000)
if bar.close_price > kliq_point:
self.cover(bar.close_price, self.lots)
self.pos=0