287 lines
11 KiB
Markdown
287 lines
11 KiB
Markdown
|
|
|||
|
|
> Name
|
|||
|
|
|
|||
|
|
回测系统随机行情生成器
|
|||
|
|
|
|||
|
|
> Author
|
|||
|
|
|
|||
|
|
发明者量化-小小梦
|
|||
|
|
|
|||
|
|
> Strategy Description
|
|||
|
|
|
|||
|
|
相关文章： https://www.fmz.com/bbs-topic/10545
|
|||
|
|
|
|||
|
|
> Strategy Arguments
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|Argument|Default|Description|
|
|||
|
|
|----|----|----|
|
|||
|
|
|filePathForCSV|CSV文件路径|CSV文件路径|
|
|||
|
|
|startTime|生成K线数据的起始时间，格式：2023-11-01 00:00:00|起始时间(UTC时间)|
|
|||
|
|
|endTime|生成的K线数据的结束时间，格式：2023-11-02 00:00:00|结束时间（UTC时间）|
|
|||
|
|
|KLinePeriod|所要生成的K线数据的K线周期，1h、1d、1m 等。|K线周期|
|
|||
|
|
|firstPrice|K线数据的起始价格|K线数据的起始价格|
|
|||
|
|
|ratio|波动性系数|波动性系数|
|
|||
|
|
|trendType|波动类型|波动类型|
|
|||
|
|
|pround|价格精度，K线数据的价格精度|价格精度|
|
|||
|
|
|vround|订单量精度|订单量精度|
|
|||
|
|
|ct|合约代码，swap 为永续合约。|合约代码|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|
|||
|
|
|Button|Default|Description|
|
|||
|
|
|----|----|----|
|
|||
|
|
|createRecords|生成随机K线数据|生成随机K线数据|
|
|||
|
|
|
|||
|
|
|
|||
|
|
> Source (python)
|
|||
|
|
|
|||
|
|
``` python
|
|||
|
|
import _thread
|
|||
|
|
import json
|
|||
|
|
import math
|
|||
|
|
import csv
|
|||
|
|
import random
|
|||
|
|
import os
|
|||
|
|
import datetime as dt
|
|||
|
|
from http.server import HTTPServer, BaseHTTPRequestHandler
|
|||
|
|
from urllib.parse import parse_qs, urlparse
|
|||
|
|
|
|||
|
|
arrTrendType = ["down", "slow_up", "sharp_down", "sharp_up", "narrow_range", "wide_range", "neutral_random"]
|
|||
|
|
|
|||
|
|
def url2Dict(url):
|
|||
|
|
query = urlparse(url).query
|
|||
|
|
params = parse_qs(query)
|
|||
|
|
result = {key: params[key][0] for key in params}
|
|||
|
|
return result
|
|||
|
|
|
|||
|
|
class Provider(BaseHTTPRequestHandler):
|
|||
|
|
def do_GET(self):
|
|||
|
|
global filePathForCSV, pround, vround, ct
|
|||
|
|
try:
|
|||
|
|
self.send_response(200)
|
|||
|
|
self.send_header("Content-type", "application/json")
|
|||
|
|
self.end_headers()
|
|||
|
|
|
|||
|
|
dictParam = url2Dict(self.path)
|
|||
|
|
Log("自定义数据源服务接收到请求，self.path:", self.path, "query 参数：", dictParam)
|
|||
|
|
|
|||
|
|
eid = dictParam["eid"]
|
|||
|
|
symbol = dictParam["symbol"]
|
|||
|
|
arrCurrency = symbol.split(".")[0].split("_")
|
|||
|
|
baseCurrency = arrCurrency[0]
|
|||
|
|
quoteCurrency = arrCurrency[1]
|
|||
|
|
fromTS = int(dictParam["from"]) * int(1000)
|
|||
|
|
toTS = int(dictParam["to"]) * int(1000)
|
|||
|
|
priceRatio = math.pow(10, int(pround))
|
|||
|
|
amountRatio = math.pow(10, int(vround))
|
|||
|
|
|
|||
|
|
data = {
|
|||
|
|
"detail": {
|
|||
|
|
"eid": eid,
|
|||
|
|
"symbol": symbol,
|
|||
|
|
"alias": symbol,
|
|||
|
|
"baseCurrency": baseCurrency,
|
|||
|
|
"quoteCurrency": quoteCurrency,
|
|||
|
|
"marginCurrency": quoteCurrency,
|
|||
|
|
"basePrecision": vround,
|
|||
|
|
"quotePrecision": pround,
|
|||
|
|
"minQty": 0.00001,
|
|||
|
|
"maxQty": 9000,
|
|||
|
|
"minNotional": 5,
|
|||
|
|
"maxNotional": 9000000,
|
|||
|
|
"priceTick": 10 ** -pround,
|
|||
|
|
"volumeTick": 10 ** -vround,
|
|||
|
|
"marginLevel": 10,
|
|||
|
|
"contractType": ct
|
|||
|
|
},
|
|||
|
|
"schema" : ["time", "open", "high", "low", "close", "vol"],
|
|||
|
|
"data" : []
|
|||
|
|
}
|
|||
|
|
|
|||
|
|
listDataSequence = []
|
|||
|
|
with open(filePathForCSV, "r") as f:
|
|||
|
|
reader = csv.reader(f)
|
|||
|
|
header = next(reader)
|
|||
|
|
headerIsNoneCount = 0
|
|||
|
|
if len(header) != len(data["schema"]):
|
|||
|
|
Log("CSV文件格式有误，列数不同，请检查！", "#FF0000")
|
|||
|
|
return
|
|||
|
|
for ele in header:
|
|||
|
|
for i in range(len(data["schema"])):
|
|||
|
|
if data["schema"][i] == ele or ele == "":
|
|||
|
|
if ele == "":
|
|||
|
|
headerIsNoneCount += 1
|
|||
|
|
if headerIsNoneCount > 1:
|
|||
|
|
Log("CSV文件格式有误，请检查！", "#FF0000")
|
|||
|
|
return
|
|||
|
|
listDataSequence.append(i)
|
|||
|
|
break
|
|||
|
|
|
|||
|
|
while True:
|
|||
|
|
record = next(reader, -1)
|
|||
|
|
if record == -1:
|
|||
|
|
break
|
|||
|
|
index = 0
|
|||
|
|
arr = [0, 0, 0, 0, 0, 0]
|
|||
|
|
for ele in record:
|
|||
|
|
arr[listDataSequence[index]] = int(ele) if listDataSequence[index] == 0 else (int(float(ele) * amountRatio) if listDataSequence[index] == 5 else int(float(ele) * priceRatio))
|
|||
|
|
index += 1
|
|||
|
|
data["data"].append(arr)
|
|||
|
|
Log("数据data.detail：", data["detail"], "响应回测系统请求。")
|
|||
|
|
self.wfile.write(json.dumps(data).encode())
|
|||
|
|
except BaseException as e:
|
|||
|
|
Log("Provider do_GET error, e:", e)
|
|||
|
|
return
|
|||
|
|
|
|||
|
|
def createServer(host):
|
|||
|
|
try:
|
|||
|
|
server = HTTPServer(host, Provider)
|
|||
|
|
Log("Starting server, listen at: %s:%s" % host)
|
|||
|
|
server.serve_forever()
|
|||
|
|
except BaseException as e:
|
|||
|
|
Log("createServer error, e:", e)
|
|||
|
|
raise Exception("stop")
|
|||
|
|
|
|||
|
|
class KlineGenerator:
|
|||
|
|
def __init__(self, start_time, end_time, interval):
|
|||
|
|
self.start_time = dt.datetime.strptime(start_time, "%Y-%m-%d %H:%M:%S")
|
|||
|
|
self.end_time = dt.datetime.strptime(end_time, "%Y-%m-%d %H:%M:%S")
|
|||
|
|
self.interval = self._parse_interval(interval)
|
|||
|
|
self.timestamps = self._generate_time_series()
|
|||
|
|
|
|||
|
|
def _parse_interval(self, interval):
|
|||
|
|
unit = interval[-1]
|
|||
|
|
value = int(interval[:-1])
|
|||
|
|
|
|||
|
|
if unit == "m":
|
|||
|
|
return value * 60
|
|||
|
|
elif unit == "h":
|
|||
|
|
return value * 3600
|
|||
|
|
elif unit == "d":
|
|||
|
|
return value * 86400
|
|||
|
|
else:
|
|||
|
|
raise ValueError("不支持的K线周期，请使用 'm', 'h', 或 'd'.")
|
|||
|
|
|
|||
|
|
def _generate_time_series(self):
|
|||
|
|
timestamps = []
|
|||
|
|
current_time = self.start_time
|
|||
|
|
while current_time <= self.end_time:
|
|||
|
|
timestamps.append(int(current_time.timestamp() * 1000))
|
|||
|
|
current_time += dt.timedelta(seconds=self.interval)
|
|||
|
|
return timestamps
|
|||
|
|
|
|||
|
|
def generate(self, initPrice, trend_type="neutral", volatility=1):
|
|||
|
|
data = []
|
|||
|
|
current_price = initPrice
|
|||
|
|
angle = 0
|
|||
|
|
for timestamp in self.timestamps:
|
|||
|
|
angle_radians = math.radians((angle + random.uniform(0, 360)) % 360)
|
|||
|
|
cos_value = math.cos(angle_radians) # -1 ~ 1
|
|||
|
|
|
|||
|
|
if trend_type == "down":
|
|||
|
|
change = random.uniform(-0.5, 0.4) * volatility * abs(cos_value)
|
|||
|
|
elif trend_type == "slow_up":
|
|||
|
|
change = random.uniform(-0.4, 0.5) * volatility * abs(cos_value)
|
|||
|
|
elif trend_type == "sharp_down":
|
|||
|
|
change = random.uniform(-10, 7) * volatility * abs(cos_value)
|
|||
|
|
elif trend_type == "sharp_up":
|
|||
|
|
change = random.uniform(-7, 10) * volatility * abs(cos_value)
|
|||
|
|
elif trend_type == "narrow_range":
|
|||
|
|
change = random.uniform(-0.2, 0.2) * volatility * abs(cos_value)
|
|||
|
|
elif trend_type == "wide_range":
|
|||
|
|
change = random.uniform(-20, 20) * volatility * abs(cos_value)
|
|||
|
|
else:
|
|||
|
|
change = random.uniform(-1, 1) * volatility * abs(cos_value)
|
|||
|
|
|
|||
|
|
open_price = current_price
|
|||
|
|
close_price = open_price + change
|
|||
|
|
high_price = open_price + random.uniform(0, abs(change)) if open_price > close_price else close_price + random.uniform(0, abs(change))
|
|||
|
|
low_price = close_price - random.uniform(0, abs(change)) if open_price > close_price else open_price - random.uniform(0, abs(change))
|
|||
|
|
|
|||
|
|
if low_price <= 0:
|
|||
|
|
change = random.uniform(1, 5) * volatility * abs(cos_value)
|
|||
|
|
close_price = open_price + change
|
|||
|
|
high_price = open_price + random.uniform(0, abs(change)) if open_price > close_price else close_price + random.uniform(0, abs(change))
|
|||
|
|
low_price = open_price * random.uniform(0.8, 1)
|
|||
|
|
|
|||
|
|
|
|||
|
|
if (high_price >= open_price and open_price >= close_price and close_price >= low_price) or (high_price >= close_price and close_price >= open_price and open_price >= low_price):
|
|||
|
|
pass
|
|||
|
|
else:
|
|||
|
|
Log("异常数据：", high_price, open_price, low_price, close_price, "#FF0000")
|
|||
|
|
|
|||
|
|
base_volume = random.uniform(1000, 5000)
|
|||
|
|
volume = base_volume * (1 + abs(change) * 0.2)
|
|||
|
|
|
|||
|
|
kline = {
|
|||
|
|
"Time": timestamp,
|
|||
|
|
"Open": round(open_price, 2),
|
|||
|
|
"High": round(high_price, 2),
|
|||
|
|
"Low": round(low_price, 2),
|
|||
|
|
"Close": round(close_price, 2),
|
|||
|
|
"Volume": round(volume, 2),
|
|||
|
|
}
|
|||
|
|
data.append(kline)
|
|||
|
|
current_price = close_price
|
|||
|
|
angle += 1
|
|||
|
|
return data
|
|||
|
|
|
|||
|
|
def save_to_csv(self, filename, data):
|
|||
|
|
with open(filename, mode="w", newline="") as csvfile:
|
|||
|
|
writer = csv.writer(csvfile)
|
|||
|
|
writer.writerow(["", "open", "high", "low", "close", "vol"])
|
|||
|
|
for idx, kline in enumerate(data):
|
|||
|
|
writer.writerow(
|
|||
|
|
[kline["Time"], kline["Open"], kline["High"], kline["Low"], kline["Close"], kline["Volume"]]
|
|||
|
|
)
|
|||
|
|
|
|||
|
|
Log("当前路径：", os.getcwd())
|
|||
|
|
with open("data.csv", "r") as file:
|
|||
|
|
lines = file.readlines()
|
|||
|
|
if len(lines) > 1:
|
|||
|
|
Log("文件写入成功，以下是文件内容的一部分：")
|
|||
|
|
Log("".join(lines[:5]))
|
|||
|
|
else:
|
|||
|
|
Log("文件写入失败，文件为空！")
|
|||
|
|
|
|||
|
|
def main():
|
|||
|
|
Chart({})
|
|||
|
|
LogReset(1)
|
|||
|
|
|
|||
|
|
try:
|
|||
|
|
# _thread.start_new_thread(createServer, (("localhost", 9090), ))
|
|||
|
|
_thread.start_new_thread(createServer, (("0.0.0.0", 9090), ))
|
|||
|
|
Log("开启自定义数据源服务线程，数据由CSV文件提供。", ", 地址/端口：0.0.0.0:9090", "#FF0000")
|
|||
|
|
except BaseException as e:
|
|||
|
|
Log("启动自定义数据源服务失败！")
|
|||
|
|
Log("错误信息：", e)
|
|||
|
|
raise Exception("stop")
|
|||
|
|
|
|||
|
|
while True:
|
|||
|
|
cmd = GetCommand()
|
|||
|
|
if cmd:
|
|||
|
|
if cmd == "createRecords":
|
|||
|
|
Log("生成器参数：", "起始时间：", startTime, "结束时间：", endTime, "K线周期：", KLinePeriod, "初始价格：", firstPrice, "波动类型：", arrTrendType[trendType], "波动性系数：", ratio)
|
|||
|
|
generator = KlineGenerator(
|
|||
|
|
start_time=startTime,
|
|||
|
|
end_time=endTime,
|
|||
|
|
interval=KLinePeriod,
|
|||
|
|
)
|
|||
|
|
kline_data = generator.generate(firstPrice, trend_type=arrTrendType[trendType], volatility=ratio)
|
|||
|
|
generator.save_to_csv("data.csv", kline_data)
|
|||
|
|
ext.PlotRecords(kline_data, "%s_%s" % ("records", KLinePeriod))
|
|||
|
|
LogStatus(_D())
|
|||
|
|
Sleep(2000)
|
|||
|
|
```
|
|||
|
|
|
|||
|
|
> Detail
|
|||
|
|
|
|||
|
|
https://www.fmz.com/strategy/473286
|
|||
|
|
|
|||
|
|
> Last Modified
|
|||
|
|
|
|||
|
|
2024-12-07 18:32:50
|