Article détaillé:https://www.fmz.com/bbs-topic/10545
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)
cos_value = math.cos(angle_radians)
if trend_type == "down":
upFactor = random.uniform(0, 0.5)
change = random.uniform(-0.5, 0.5 * upFactor) * volatility
elif trend_type == "slow_up":
downFactor = random.uniform(0, 0.5)
change = random.uniform(-0.5 * downFactor, 0.5) * volatility
elif trend_type == "sharp_down":
upFactor = random.uniform(0, 0.5)
change = random.uniform(-10, 0.5 * upFactor) * volatility
elif trend_type == "sharp_up":
downFactor = random.uniform(0, 0.5)
change = random.uniform(-0.5 * downFactor, 10) * volatility
elif trend_type == "narrow_range":
change = random.uniform(-0.2, 0.2) * volatility
elif trend_type == "wide_range":
change = random.uniform(-3, 3) * volatility
else:
change = random.uniform(-0.5, 0.5) * volatility
change = change + cos_value * random.uniform(-0.2, 0.2) * volatility
open_price = current_price
high_price = open_price + random.uniform(0, abs(change))
low_price = max(open_price - random.uniform(0, abs(change)), random.uniform(0, open_price))
close_price = random.uniform(low_price, high_price)
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")
high_price = max(high_price, open_price, close_price)
low_price = min(low_price, open_price, close_price)
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 += 5
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)