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