1. optimize binance ma quant strategy
2. support monitor 1m volume
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22
config.py
22
config.py
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@ -71,6 +71,28 @@ OKX_MONITOR_CONFIG = {
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},
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}
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OKX_REALTIME_MONITOR_CONFIG = {
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"volume_monitor": {
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"symbols": [
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"XCH-USDT",
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"BTC-USDT",
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"SOL-USDT",
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"ETH-USDT",
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"DOGE-USDT",
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],
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"bars": ["1m", "5m", "15m", "30m", "1H"],
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"initial_date": "2025-05-15 00:00:00",
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},
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"price_monitor": {
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"symbols": ["XCH-USDT"],
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"bats": [
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{"bar": "5m", "threshold": 0.025},
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{"bar": "15m", "threshold": 0.5},
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{"bar": "1H", "threshold": 0.1},
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],
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},
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}
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BINANCE_MONITOR_CONFIG = {
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"volume_monitor": {
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"symbols": [
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@ -8,12 +8,19 @@ from datetime import datetime, timedelta, timezone
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from core.utils import get_current_date_time
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import re
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import json
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import math
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from openpyxl import Workbook
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from openpyxl.drawing.image import Image
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import openpyxl
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from openpyxl.styles import Font
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from PIL import Image as PILImage
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from config import OKX_MONITOR_CONFIG, US_STOCK_MONITOR_CONFIG, MYSQL_CONFIG, WINDOW_SIZE, BINANCE_MONITOR_CONFIG
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from config import (
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OKX_MONITOR_CONFIG,
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US_STOCK_MONITOR_CONFIG,
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MYSQL_CONFIG,
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WINDOW_SIZE,
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BINANCE_MONITOR_CONFIG,
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)
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from core.db.db_market_data import DBMarketData
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from core.db.db_binance_data import DBBinanceData
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from core.db.db_huge_volume_data import DBHugeVolumeData
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@ -34,7 +41,12 @@ class MaBreakStatistics:
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之间的涨跌幅
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"""
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def __init__(self, is_us_stock: bool = False, is_binance: bool = False):
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def __init__(
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self,
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is_us_stock: bool = False,
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is_binance: bool = False,
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commission_per_share: float = 0.0008,
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):
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mysql_user = MYSQL_CONFIG.get("user", "xch")
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mysql_password = MYSQL_CONFIG.get("password", "")
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if not mysql_password:
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@ -65,16 +77,16 @@ class MaBreakStatistics:
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"symbols", ["BTC-USDT"]
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)
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self.bars = ["30m", "1H"]
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self.initial_date = BINANCE_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"initial_date", "2017-08-16 00:00:00"
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)
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self.initial_date = BINANCE_MONITOR_CONFIG.get(
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"volume_monitor", {}
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).get("initial_date", "2017-08-16 00:00:00")
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self.db_market_data = DBBinanceData(self.db_url)
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else:
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self.symbols = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"symbols", ["XCH-USDT"]
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"symbols", ["XCH-USDT"]
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)
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self.bars = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"bars", ["5m", "15m", "30m", "1H"]
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"bars", ["5m", "15m", "30m", "1H"]
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)
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self.initial_date = OKX_MONITOR_CONFIG.get("volume_monitor", {}).get(
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"initial_date", "2025-05-15 00:00:00"
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@ -82,7 +94,8 @@ class MaBreakStatistics:
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self.db_market_data = DBMarketData(self.db_url)
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if len(self.initial_date) > 10:
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self.initial_date = self.initial_date[:10]
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self.end_date = get_current_date_time()
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self.end_date = get_current_date_time(format="%Y-%m-%d")
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self.commission_per_share = commission_per_share
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self.trade_strategy_config = self.get_trade_strategy_config()
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self.main_strategy = self.trade_strategy_config.get("均线系统策略", None)
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@ -93,14 +106,26 @@ class MaBreakStatistics:
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def batch_statistics(self, strategy_name: str = "全均线策略"):
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if self.is_us_stock:
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self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/"
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self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/"
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/us_stock/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/us_stock/chart/{strategy_name}/"
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)
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elif self.is_binance:
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self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/"
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self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/"
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/binance/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/binance/chart/{strategy_name}/"
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)
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else:
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self.stats_output_dir = f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/"
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self.stats_chart_dir = f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/"
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self.stats_output_dir = (
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f"./output/trade_sandbox/ma_strategy/okx/excel/{strategy_name}/"
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)
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self.stats_chart_dir = (
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f"./output/trade_sandbox/ma_strategy/okx/chart/{strategy_name}/"
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)
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os.makedirs(self.stats_output_dir, exist_ok=True)
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os.makedirs(self.stats_chart_dir, exist_ok=True)
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@ -111,7 +136,7 @@ class MaBreakStatistics:
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for symbol in self.symbols:
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for bar in self.bars:
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logger.info(
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f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}"
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f"开始计算{symbol} {bar}的MA突破区间涨跌幅统计, 策略: {strategy_name}，交易费率：{self.commission_per_share}"
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)
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ma_break_market_data, market_data_pct_chg = self.trade_simulate(
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symbol, bar, strategy_name
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@ -131,35 +156,14 @@ class MaBreakStatistics:
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ma_break_market_data = pd.concat(ma_break_market_data_list)
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market_data_pct_chg_df = pd.DataFrame(market_data_pct_chg_list)
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# 依据symbol和bar分组，统计每个symbol和bar的pct_chg的max, min, mean, std, median, count
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ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
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ma_break_market_data.sort_values(
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by="begin_timestamp", ascending=True, inplace=True
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)
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ma_break_market_data.reset_index(drop=True, inplace=True)
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pct_chg_df = (
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ma_break_market_data.groupby(["symbol", "bar"])["pct_chg"]
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.agg(
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pct_chg_sum="sum",
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pct_chg_max="max",
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pct_chg_min="min",
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pct_chg_mean="mean",
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pct_chg_std="std",
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pct_chg_median="median",
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pct_chg_count="count",
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)
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.reset_index()
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)
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pct_chg_df["strategy_name"] = strategy_name
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pct_chg_df["pct_chg_total"] = 0
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pct_chg_df["market_pct_chg"] = 0
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# 将pct_chg_total与market_pct_chg的值类型转换为float
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pct_chg_df["pct_chg_total"] = pct_chg_df["pct_chg_total"].astype(float)
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pct_chg_df["market_pct_chg"] = pct_chg_df["market_pct_chg"].astype(float)
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# 统计pct_chg_total
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# 算法要求，ma_break_market_data，然后pct_chg/100 + 1
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ma_break_market_data["pct_chg_total"] = (
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ma_break_market_data["pct_chg"] / 100 + 1
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)
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# 遍历symbol和bar，按照end_timestamp排序，计算pct_chg_total的值，然后相乘
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for symbol in pct_chg_df["symbol"].unique():
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for bar in pct_chg_df["bar"].unique():
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account_value_chg_list = []
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for symbol in market_data_pct_chg_df["symbol"].unique():
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for bar in market_data_pct_chg_df["bar"].unique():
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symbol_bar_data = ma_break_market_data[
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(ma_break_market_data["symbol"] == symbol)
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& (ma_break_market_data["bar"] == bar)
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@ -169,51 +173,80 @@ class MaBreakStatistics:
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by="end_timestamp", ascending=True, inplace=True
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)
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symbol_bar_data.reset_index(drop=True, inplace=True)
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symbol_bar_data["pct_chg_total"] = symbol_bar_data[
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"pct_chg_total"
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].cumprod()
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# 将更新后的pct_chg_total数据同步更新到ma_break_market_data的对应数据行中
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for idx, row in symbol_bar_data.iterrows():
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mask = (ma_break_market_data["symbol"] == symbol) & \
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(ma_break_market_data["bar"] == bar) & \
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(ma_break_market_data["end_timestamp"] == row["end_timestamp"])
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ma_break_market_data.loc[mask, "pct_chg_total"] = row["pct_chg_total"]
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last_pct_chg_total = symbol_bar_data["pct_chg_total"].iloc[-1]
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last_pct_chg_total = (last_pct_chg_total - 1) * 100
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pct_chg_df.loc[
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(pct_chg_df["symbol"] == symbol)
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& (pct_chg_df["bar"] == bar),
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"pct_chg_total",
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] = last_pct_chg_total
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initial_capital = int(market_data_pct_chg_df.loc[
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(market_data_pct_chg_df["symbol"] == symbol)
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& (market_data_pct_chg_df["bar"] == bar),
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"initial_capital",
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].values[0])
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final_account_value = float(symbol_bar_data["end_account_value"].iloc[-1])
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account_value_chg = (final_account_value - initial_capital) / initial_capital * 100
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account_value_chg = round(account_value_chg, 4)
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market_pct_chg = market_data_pct_chg_df.loc[
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(market_data_pct_chg_df["symbol"] == symbol)
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& (market_data_pct_chg_df["bar"] == bar),
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"pct_chg",
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].values[0]
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pct_chg_df.loc[
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(pct_chg_df["symbol"] == symbol)
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& (pct_chg_df["bar"] == bar),
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"market_pct_chg",
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] = market_pct_chg
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pct_chg_df = pct_chg_df[
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total_buy_commission = float(symbol_bar_data["buy_commission"].sum())
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total_sell_commission = float(symbol_bar_data["sell_commission"].sum())
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total_commission = total_buy_commission + total_sell_commission
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total_commission = round(total_commission, 4)
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total_buy_commission = round(total_buy_commission, 4)
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total_sell_commission = round(total_sell_commission, 4)
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account_value_chg_list.append({
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"strategy_name": strategy_name,
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"symbol": symbol,
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"bar": bar,
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"total_buy_commission": total_buy_commission,
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"total_sell_commission": total_sell_commission,
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"total_commission": total_commission,
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"initial_account_value": initial_capital,
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"final_account_value": final_account_value,
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"account_value_chg": account_value_chg,
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"market_pct_chg": market_pct_chg,
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})
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account_value_chg_df = pd.DataFrame(account_value_chg_list)
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account_value_chg_df = account_value_chg_df[
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[
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"strategy_name",
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"symbol",
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"bar",
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"total_buy_commission",
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"total_sell_commission",
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"total_commission",
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"initial_account_value",
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"final_account_value",
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"account_value_chg",
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"market_pct_chg",
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"pct_chg_total",
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"pct_chg_sum",
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"pct_chg_max",
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"pct_chg_min",
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"pct_chg_mean",
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"pct_chg_std",
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"pct_chg_median",
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"pct_chg_count",
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]
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]
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account_value_statistics_df = (
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ma_break_market_data.groupby(["symbol", "bar"])["end_account_value"]
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.agg(
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account_value_max="max",
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account_value_min="min",
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account_value_mean="mean",
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account_value_std="std",
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account_value_median="median",
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account_value_count="count",
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)
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.reset_index()
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)
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account_value_statistics_df["strategy_name"] = strategy_name
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account_value_statistics_df = account_value_statistics_df[
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[
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"strategy_name",
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"symbol",
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"bar",
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"account_value_max",
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"account_value_min",
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"account_value_mean",
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"account_value_std",
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"account_value_median",
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"account_value_count",
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]
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]
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# 依据symbol和bar分组，统计每个symbol和bar的interval_minutes的max, min, mean, std, median, count
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interval_minutes_df = (
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ma_break_market_data.groupby(["symbol", "bar"])["interval_minutes"]
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@ -227,6 +260,20 @@ class MaBreakStatistics:
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)
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.reset_index()
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)
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interval_minutes_df["strategy_name"] = strategy_name
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interval_minutes_df = interval_minutes_df[
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[
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"strategy_name",
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"symbol",
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"bar",
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"interval_minutes_max",
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"interval_minutes_min",
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"interval_minutes_mean",
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"interval_minutes_std",
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"interval_minutes_median",
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"interval_minutes_count",
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]
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]
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earliest_market_date_time = ma_break_market_data["begin_date_time"].min()
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earliest_market_date_time = re.sub(
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@ -238,7 +285,10 @@ class MaBreakStatistics:
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latest_market_date_time = re.sub(
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r"[\:\-\s]", "", str(latest_market_date_time)
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)
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output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}.xlsx"
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if self.commission_per_share > 0:
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output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}_with_commission.xlsx"
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else:
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output_file_name = f"ma_break_stats_from_{earliest_market_date_time}_to_{latest_market_date_time}_{strategy_name}_without_commission.xlsx"
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output_file_path = os.path.join(self.stats_output_dir, output_file_name)
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logger.info(f"导出{output_file_path}")
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strategy_info_df = self.get_strategy_info(strategy_name)
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@ -247,16 +297,21 @@ class MaBreakStatistics:
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ma_break_market_data.to_excel(
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writer, sheet_name="买卖记录明细", index=False
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)
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pct_chg_df.to_excel(writer, sheet_name="买卖涨跌幅统计", index=False)
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account_value_chg_df.to_excel(writer, sheet_name="资产价值变化", index=False)
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account_value_statistics_df.to_excel(
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writer, sheet_name="买卖账户价值统计", index=False
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)
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interval_minutes_df.to_excel(
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writer, sheet_name="买卖时间间隔统计", index=False
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)
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chart_dict = self.draw_quant_pct_chg_bar_chart(pct_chg_df, strategy_name)
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chart_dict = self.draw_quant_pct_chg_bar_chart(account_value_chg_df, strategy_name)
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self.output_chart_to_excel(output_file_path, chart_dict)
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chart_dict = self.draw_quant_line_chart(ma_break_market_data, strategy_name)
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chart_dict = self.draw_quant_line_chart(
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ma_break_market_data, market_data_pct_chg_df, strategy_name
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)
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self.output_chart_to_excel(output_file_path, chart_dict)
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return pct_chg_df
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return account_value_chg_df
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else:
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return None
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@ -333,6 +388,14 @@ class MaBreakStatistics:
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date_time_field = "date_time_us"
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else:
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date_time_field = "date_time"
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close_mean = market_data["close"].mean()
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self.update_initial_capital(close_mean)
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logger.info(
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f"成功获取{symbol}数据：{len(market_data)}根{bar}K线,开始日期={market_data[date_time_field].min()}，结束日期={market_data[date_time_field].max()}"
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)
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account_value = self.initial_capital
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for index, row in market_data.iterrows():
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ma_cross = row["ma_cross"]
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timestamp = row["timestamp"]
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@ -345,7 +408,6 @@ class MaBreakStatistics:
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macd_diff = float(row["dif"])
|
||||
macd_dea = float(row["dea"])
|
||||
macd = float(row["macd"])
|
||||
|
||||
if ma_break_market_data_pair.get("begin_timestamp", None) is None:
|
||||
buy_condition = self.fit_strategy(
|
||||
strategy_name=strategy_name,
|
||||
|
|
@ -355,6 +417,18 @@ class MaBreakStatistics:
|
|||
)
|
||||
|
||||
if buy_condition:
|
||||
entry_price = close
|
||||
# 计算交易股数
|
||||
shares, account_value = self.calculate_shares(
|
||||
account_value, entry_price
|
||||
)
|
||||
if shares == 0:
|
||||
# 股数为0→不交易
|
||||
continue
|
||||
|
||||
# 计算佣金
|
||||
buy_commission = shares * close * self.commission_per_share
|
||||
|
||||
ma_break_market_data_pair = {}
|
||||
ma_break_market_data_pair["symbol"] = symbol
|
||||
ma_break_market_data_pair["bar"] = bar
|
||||
|
|
@ -368,6 +442,9 @@ class MaBreakStatistics:
|
|||
ma_break_market_data_pair["begin_macd_diff"] = macd_diff
|
||||
ma_break_market_data_pair["begin_macd_dea"] = macd_dea
|
||||
ma_break_market_data_pair["begin_macd"] = macd
|
||||
ma_break_market_data_pair["shares"] = shares
|
||||
ma_break_market_data_pair["buy_commission"] = buy_commission
|
||||
ma_break_market_data_pair["begin_account_value"] = account_value
|
||||
continue
|
||||
else:
|
||||
sell_condition = self.fit_strategy(
|
||||
|
|
@ -378,6 +455,20 @@ class MaBreakStatistics:
|
|||
)
|
||||
|
||||
if sell_condition:
|
||||
shares = ma_break_market_data_pair["shares"]
|
||||
entry_price = ma_break_market_data_pair["begin_close"]
|
||||
exit_price = close
|
||||
sell_commission = (
|
||||
shares * exit_price * self.commission_per_share
|
||||
)
|
||||
profit_loss = (exit_price - entry_price) * shares
|
||||
begin_account_value = ma_break_market_data_pair[
|
||||
"begin_account_value"
|
||||
]
|
||||
account_value = (
|
||||
begin_account_value + profit_loss - sell_commission
|
||||
)
|
||||
|
||||
ma_break_market_data_pair["end_timestamp"] = timestamp
|
||||
ma_break_market_data_pair["end_date_time"] = date_time
|
||||
ma_break_market_data_pair["end_close"] = close
|
||||
|
|
@ -389,11 +480,14 @@ class MaBreakStatistics:
|
|||
ma_break_market_data_pair["end_macd_dea"] = macd_dea
|
||||
ma_break_market_data_pair["end_macd"] = macd
|
||||
ma_break_market_data_pair["pct_chg"] = (
|
||||
close - ma_break_market_data_pair["begin_close"]
|
||||
) / ma_break_market_data_pair["begin_close"]
|
||||
exit_price - entry_price
|
||||
) / entry_price
|
||||
ma_break_market_data_pair["pct_chg"] = round(
|
||||
ma_break_market_data_pair["pct_chg"] * 100, 4
|
||||
)
|
||||
ma_break_market_data_pair["profit_loss"] = profit_loss
|
||||
ma_break_market_data_pair["sell_commission"] = sell_commission
|
||||
ma_break_market_data_pair["end_account_value"] = account_value
|
||||
ma_break_market_data_pair["interval_seconds"] = (
|
||||
timestamp - ma_break_market_data_pair["begin_timestamp"]
|
||||
) / 1000
|
||||
|
|
@ -413,7 +507,9 @@ class MaBreakStatistics:
|
|||
if len(ma_break_market_data_pair_list) > 0:
|
||||
ma_break_market_data = pd.DataFrame(ma_break_market_data_pair_list)
|
||||
# sort by end_timestamp
|
||||
ma_break_market_data.sort_values(by="begin_timestamp", ascending=True, inplace=True)
|
||||
ma_break_market_data.sort_values(
|
||||
by="begin_timestamp", ascending=True, inplace=True
|
||||
)
|
||||
ma_break_market_data.reset_index(drop=True, inplace=True)
|
||||
logger.info(
|
||||
f"获取{symbol} {bar} 的买卖记录明细成功, 买卖次数: {len(ma_break_market_data)}"
|
||||
|
|
@ -421,16 +517,85 @@ class MaBreakStatistics:
|
|||
# 量化期间，市场的波动率：
|
||||
# ma_break_market_data(最后一条数据的end_close - 第一条数据的begin_close) / 第一条数据的begin_close * 100
|
||||
pct_chg = (
|
||||
(ma_break_market_data["end_close"].iloc[-1] - ma_break_market_data["begin_close"].iloc[0])
|
||||
(
|
||||
ma_break_market_data["end_close"].iloc[-1]
|
||||
- ma_break_market_data["begin_close"].iloc[0]
|
||||
)
|
||||
/ ma_break_market_data["begin_close"].iloc[0]
|
||||
* 100
|
||||
)
|
||||
pct_chg = round(pct_chg, 4)
|
||||
market_data_pct_chg = {"symbol": symbol, "bar": bar, "pct_chg": pct_chg}
|
||||
market_data_pct_chg = {
|
||||
"symbol": symbol,
|
||||
"bar": bar,
|
||||
"pct_chg": pct_chg,
|
||||
"initial_capital": self.initial_capital,
|
||||
}
|
||||
return ma_break_market_data, market_data_pct_chg
|
||||
else:
|
||||
return None, None
|
||||
|
||||
def update_initial_capital(self, close_mean: float):
|
||||
self.initial_capital = 25000
|
||||
if close_mean > 10000:
|
||||
self.initial_capital = self.initial_capital * 10000
|
||||
elif close_mean > 5000:
|
||||
self.initial_capital = self.initial_capital * 5000
|
||||
elif close_mean > 1000:
|
||||
self.initial_capital = self.initial_capital * 1000
|
||||
elif close_mean > 500:
|
||||
self.initial_capital = self.initial_capital * 500
|
||||
elif close_mean > 100:
|
||||
self.initial_capital = self.initial_capital * 100
|
||||
else:
|
||||
pass
|
||||
logger.info(f"收盘价均值：{close_mean}")
|
||||
logger.info(f"初始资金调整为：{self.initial_capital}")
|
||||
|
||||
def calculate_shares(self, account_value, entry_price):
|
||||
"""
|
||||
根据ORB公式计算交易股数
|
||||
:param account_value: 当前账户价值（美元）
|
||||
:param entry_price: 交易买入价格
|
||||
:param commission_per_share: 交易佣金, 默认为0.0008
|
||||
:return: 整数股数（Shares）
|
||||
"""
|
||||
logger.info(
|
||||
f"开始计算交易股数：账户价值={account_value}，买入价格={entry_price}"
|
||||
)
|
||||
try:
|
||||
# 验证输入参数
|
||||
if account_value <= 0 or entry_price <= 0 or self.commission_per_share < 0:
|
||||
logger.error("账户价值、买入价格或佣金不能为负或零")
|
||||
return 0, account_value # 返回0股，账户价值不变
|
||||
|
||||
# 计算考虑手续费后的每单位BTC总成本
|
||||
total_cost_per_share = entry_price * (1 + self.commission_per_share)
|
||||
|
||||
# 计算可购买的BTC数量（向下取整）
|
||||
shares = math.floor(account_value / total_cost_per_share)
|
||||
|
||||
# 计算总成本（含手续费）
|
||||
total_cost = shares * total_cost_per_share
|
||||
|
||||
# 计算剩余现金
|
||||
remaining_cash = account_value - total_cost
|
||||
|
||||
# 计算总资产价值 = (购买的BTC数量 × 买入价格) + 剩余现金
|
||||
remaining_value = (shares * entry_price) + remaining_cash
|
||||
|
||||
# 记录计算结果
|
||||
logger.info(
|
||||
f"计算结果：可购买股数={shares}，总成本={total_cost:.2f}美元，"
|
||||
f"剩余现金={remaining_cash:.2f}美元，总资产价值={remaining_value:.2f}美元"
|
||||
)
|
||||
|
||||
return shares, remaining_value
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"计算股数或账户价值时出错：{str(e)}")
|
||||
return 0, account_value # 出错时返回0股，账户价值不变
|
||||
|
||||
def get_full_data(self, symbol: str, bar: str = "5m"):
|
||||
"""
|
||||
分段获取数据，并将数据合并为完整数据
|
||||
|
|
@ -464,9 +629,7 @@ class MaBreakStatistics:
|
|||
current_end_date = min(start_date + timedelta(days=180), end_date)
|
||||
start_date_str = start_date.strftime("%Y-%m-%d")
|
||||
current_end_date_str = current_end_date.strftime("%Y-%m-%d")
|
||||
logger.info(
|
||||
f"获取{symbol}数据：{start_date_str}至{current_end_date_str}"
|
||||
)
|
||||
logger.info(f"获取{symbol}数据：{start_date_str}至{current_end_date_str}")
|
||||
current_data = self.db_market_data.query_market_data_by_symbol_bar(
|
||||
symbol, bar, fields, start=start_date_str, end=current_end_date_str
|
||||
)
|
||||
|
|
@ -483,7 +646,6 @@ class MaBreakStatistics:
|
|||
data.reset_index(drop=True, inplace=True)
|
||||
return data
|
||||
|
||||
|
||||
def fit_strategy(
|
||||
self,
|
||||
strategy_name: str = "全均线策略",
|
||||
|
|
@ -626,92 +788,105 @@ class MaBreakStatistics:
|
|||
plt.rcParams["font.size"] = 11 # 设置字体大小
|
||||
plt.rcParams["axes.unicode_minus"] = False # 解决负号显示问题
|
||||
chart_dict = {}
|
||||
column_name_dict = {"pct_chg_total": "量化策略涨跌", "pct_chg_mean": "量化策略涨跌均值"}
|
||||
column_name_dict = {
|
||||
"account_value_chg": "量化策略涨跌",
|
||||
}
|
||||
for column_name, column_name_text in column_name_dict.items():
|
||||
for bar in data["bar"].unique():
|
||||
bar_data = data[data["bar"] == bar].copy() # 一次筛选即可
|
||||
if bar_data.empty:
|
||||
continue
|
||||
bar_data.rename(columns={column_name: column_name_text}, inplace=True)
|
||||
if column_name == "pct_chg_total":
|
||||
bar_data.rename(columns={"market_pct_chg": "市场自然涨跌"}, inplace=True)
|
||||
bar_data.rename(
|
||||
columns={"market_pct_chg": "市场自然涨跌"}, inplace=True
|
||||
)
|
||||
# 可选：按均值排序
|
||||
bar_data.sort_values(by=column_name_text, ascending=False, inplace=True)
|
||||
bar_data.reset_index(drop=True, inplace=True)
|
||||
|
||||
# 如果column_name_text是"量化策略涨跌"，则柱状图，同时绘制量化策略涨跌与市场自然涨跌的柱状图，并绘制在同一个图表中
|
||||
if column_name == "pct_chg_total":
|
||||
plt.figure(figsize=(12, 7))
|
||||
plt.figure(figsize=(12, 7))
|
||||
|
||||
# 设置x轴位置，为并列柱状图做准备
|
||||
x = np.arange(len(bar_data))
|
||||
width = 0.35 # 柱状图宽度
|
||||
# 设置x轴位置，为并列柱状图做准备
|
||||
x = np.arange(len(bar_data))
|
||||
width = 0.35 # 柱状图宽度
|
||||
|
||||
# 确保symbol列是字符串类型，避免matplotlib警告
|
||||
bar_data["symbol"] = bar_data["symbol"].astype(str)
|
||||
# 确保symbol列是字符串类型，避免matplotlib警告
|
||||
bar_data["symbol"] = bar_data["symbol"].astype(str)
|
||||
|
||||
# 绘制量化策略涨跌柱状图（蓝色渐变色）
|
||||
bars1 = plt.bar(x - width/2, bar_data[column_name_text], width,
|
||||
label=column_name_text,
|
||||
color=plt.cm.Blues(np.linspace(0.6, 0.9, len(bar_data))))
|
||||
# 绘制量化策略涨跌柱状图（蓝色渐变色）
|
||||
bars1 = plt.bar(
|
||||
x - width / 2,
|
||||
bar_data[column_name_text],
|
||||
width,
|
||||
label=column_name_text,
|
||||
color=plt.cm.Blues(np.linspace(0.6, 0.9, len(bar_data))),
|
||||
)
|
||||
|
||||
# 绘制市场自然涨跌柱状图（绿色渐变色）
|
||||
bars2 = plt.bar(x + width/2, bar_data["市场自然涨跌"], width,
|
||||
label="市场自然涨跌",
|
||||
color=plt.cm.Greens(np.linspace(0.6, 0.9, len(bar_data))))
|
||||
# 绘制市场自然涨跌柱状图（绿色渐变色）
|
||||
bars2 = plt.bar(
|
||||
x + width / 2,
|
||||
bar_data["市场自然涨跌"],
|
||||
width,
|
||||
label="市场自然涨跌",
|
||||
color=plt.cm.Greens(np.linspace(0.6, 0.9, len(bar_data))),
|
||||
)
|
||||
|
||||
# 设置图表标题和标签
|
||||
plt.title(f"{bar}趋势{column_name_text}与市场自然涨跌对比(%)", fontsize=14, fontweight='bold')
|
||||
plt.xlabel("Symbol", fontsize=12)
|
||||
plt.ylabel("涨跌幅(%)", fontsize=12)
|
||||
plt.xticks(x, bar_data['symbol'], rotation=45, ha='right')
|
||||
plt.legend()
|
||||
plt.grid(True, alpha=0.3)
|
||||
# 设置图表标题和标签
|
||||
plt.title(
|
||||
f"{bar}趋势{column_name_text}与市场自然涨跌对比(%)",
|
||||
fontsize=14,
|
||||
fontweight="bold",
|
||||
)
|
||||
plt.xlabel("Symbol", fontsize=12)
|
||||
plt.ylabel("涨跌幅(%)", fontsize=12)
|
||||
plt.xticks(x, bar_data["symbol"], rotation=45, ha="right")
|
||||
plt.legend()
|
||||
plt.grid(True, alpha=0.3)
|
||||
|
||||
# 在量化策略涨跌柱状图上方添加数值标签
|
||||
for i, (bar1, value1) in enumerate(zip(bars1, bar_data[column_name_text])):
|
||||
plt.text(bar1.get_x() + bar1.get_width()/2, value1 + (0.01 if value1 >= 0 else -0.01),
|
||||
f'{value1:.3f}%', ha='center', va='bottom' if value1 >= 0 else 'top',
|
||||
fontsize=9, fontweight='bold', color='darkblue')
|
||||
|
||||
# 在市场自然涨跌柱状图上方添加数值标签
|
||||
for i, (bar2, value2) in enumerate(zip(bars2, bar_data["市场自然涨跌"])):
|
||||
plt.text(bar2.get_x() + bar2.get_width()/2, value2 + (0.01 if value2 >= 0 else -0.01),
|
||||
f'{value2:.3f}%', ha='center', va='bottom' if value2 >= 0 else 'top',
|
||||
fontsize=9, fontweight='bold', color='darkgreen')
|
||||
|
||||
else:
|
||||
plt.figure(figsize=(10, 6))
|
||||
|
||||
# 确保symbol列是字符串类型，避免matplotlib警告
|
||||
bar_data["symbol"] = bar_data["symbol"].astype(str)
|
||||
|
||||
ax = sns.barplot(
|
||||
x="symbol", y=column_name_text, data=bar_data, palette="Blues_d"
|
||||
# 在量化策略涨跌柱状图上方添加数值标签
|
||||
for i, (bar1, value1) in enumerate(
|
||||
zip(bars1, bar_data[column_name_text])
|
||||
):
|
||||
plt.text(
|
||||
bar1.get_x() + bar1.get_width() / 2,
|
||||
value1 + (0.01 if value1 >= 0 else -0.01),
|
||||
f"{value1:.3f}%",
|
||||
ha="center",
|
||||
va="bottom" if value1 >= 0 else "top",
|
||||
fontsize=9,
|
||||
fontweight="bold",
|
||||
color="darkblue",
|
||||
)
|
||||
plt.title(f"{bar}趋势{column_name_text}(%)")
|
||||
plt.xlabel("symbol")
|
||||
plt.ylabel(column_name_text)
|
||||
plt.xticks(rotation=45, ha="right")
|
||||
|
||||
# 在柱状图上添加数值标签
|
||||
for i, v in enumerate(bar_data[column_name_text]):
|
||||
ax.text(
|
||||
i,
|
||||
v,
|
||||
f"{v:.3f}",
|
||||
ha="center",
|
||||
va="bottom",
|
||||
fontsize=10,
|
||||
fontweight="bold",
|
||||
)
|
||||
# 在市场自然涨跌柱状图上方添加数值标签
|
||||
for i, (bar2, value2) in enumerate(
|
||||
zip(bars2, bar_data["市场自然涨跌"])
|
||||
):
|
||||
plt.text(
|
||||
bar2.get_x() + bar2.get_width() / 2,
|
||||
value2 + (0.01 if value2 >= 0 else -0.01),
|
||||
f"{value2:.3f}%",
|
||||
ha="center",
|
||||
va="bottom" if value2 >= 0 else "top",
|
||||
fontsize=9,
|
||||
fontweight="bold",
|
||||
color="darkgreen",
|
||||
)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
save_path = os.path.join(
|
||||
self.stats_chart_dir,
|
||||
f"{bar}_bar_chart_{column_name}_{strategy_name}.png",
|
||||
)
|
||||
if self.commission_per_share > 0:
|
||||
save_path = os.path.join(
|
||||
self.stats_chart_dir,
|
||||
f"{bar}_bar_chart_{column_name}_{strategy_name}_with_commission.png",
|
||||
)
|
||||
else:
|
||||
save_path = os.path.join(
|
||||
self.stats_chart_dir,
|
||||
f"{bar}_bar_chart_{column_name}_{strategy_name}_without_commission.png",
|
||||
)
|
||||
|
||||
plt.savefig(save_path, dpi=150)
|
||||
plt.close()
|
||||
|
||||
|
|
@ -719,10 +894,16 @@ class MaBreakStatistics:
|
|||
chart_dict[sheet_name] = save_path
|
||||
return chart_dict
|
||||
|
||||
def draw_quant_line_chart(self, data: pd.DataFrame, strategy_name: str = "全均线策略"):
|
||||
def draw_quant_line_chart(
|
||||
self,
|
||||
data: pd.DataFrame,
|
||||
market_data_pct_chg_df: pd.DataFrame,
|
||||
strategy_name: str = "全均线策略",
|
||||
):
|
||||
"""
|
||||
根据量化策略买卖明细记录，绘制量化策略涨跌与市场自然涨跌的折线图
|
||||
:param data: 量化策略买卖明细记录
|
||||
:param market_data_pct_chg_df: 市场自然涨跌记录
|
||||
:param strategy_name: 策略名称
|
||||
:return: None
|
||||
"""
|
||||
|
|
@ -731,16 +912,27 @@ class MaBreakStatistics:
|
|||
chart_dict = {}
|
||||
for symbol in symbols:
|
||||
for bar in bars:
|
||||
symbol_bar_data = data[(data["symbol"] == symbol) & (data["bar"] == bar)]
|
||||
symbol_bar_data = data[
|
||||
(data["symbol"] == symbol) & (data["bar"] == bar)
|
||||
]
|
||||
if symbol_bar_data.empty:
|
||||
continue
|
||||
|
||||
# 获取第一行数据作为基准
|
||||
first_row = symbol_bar_data.iloc[0].copy()
|
||||
|
||||
initial_capital = int(
|
||||
market_data_pct_chg_df.loc[
|
||||
(market_data_pct_chg_df["symbol"] == symbol)
|
||||
& (market_data_pct_chg_df["bar"] == bar),
|
||||
"initial_capital",
|
||||
].values[0]
|
||||
)
|
||||
# 创建初始化行，设置基准值
|
||||
init_row = first_row.copy()
|
||||
init_row.loc["pct_chg_total"] = 1.0 # 量化策略初始值为1
|
||||
init_row.loc["profit_loss"] = 0
|
||||
init_row.loc["end_account_value"] = (
|
||||
initial_capital # 量化策略初始值为初始资金
|
||||
)
|
||||
init_row.loc["end_timestamp"] = first_row["begin_timestamp"]
|
||||
init_row.loc["end_date_time"] = first_row["begin_date_time"]
|
||||
init_row.loc["end_close"] = first_row["begin_close"]
|
||||
|
|
@ -759,29 +951,62 @@ class MaBreakStatistics:
|
|||
|
||||
# 将初始化行添加到数据开头
|
||||
symbol_bar_data = pd.concat([pd.DataFrame([init_row]), symbol_bar_data])
|
||||
symbol_bar_data.sort_values(by="end_timestamp", ascending=True, inplace=True)
|
||||
symbol_bar_data.sort_values(
|
||||
by="end_timestamp", ascending=True, inplace=True
|
||||
)
|
||||
symbol_bar_data.reset_index(drop=True, inplace=True)
|
||||
|
||||
# 确保时间列是datetime类型，避免matplotlib警告
|
||||
symbol_bar_data["end_date_time"] = pd.to_datetime(symbol_bar_data["end_date_time"])
|
||||
symbol_bar_data["end_date_time"] = pd.to_datetime(
|
||||
symbol_bar_data["end_date_time"]
|
||||
)
|
||||
|
||||
# 计算持仓价值归一化数据（相对于初始价格）
|
||||
symbol_bar_data["end_account_value_to_1"] = (
|
||||
symbol_bar_data["end_account_value"] / initial_capital
|
||||
)
|
||||
symbol_bar_data["end_account_value_to_1"] = symbol_bar_data[
|
||||
"end_account_value_to_1"
|
||||
].round(4)
|
||||
|
||||
# 计算市场价位归一化数据（相对于初始价格）
|
||||
symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close"] / init_row["end_close"]
|
||||
symbol_bar_data["end_close_to_1"] = symbol_bar_data["end_close_to_1"].round(4)
|
||||
symbol_bar_data["end_close_to_1"] = (
|
||||
symbol_bar_data["end_close"] / init_row["end_close"]
|
||||
)
|
||||
symbol_bar_data["end_close_to_1"] = symbol_bar_data[
|
||||
"end_close_to_1"
|
||||
].round(4)
|
||||
|
||||
# 绘制折线图
|
||||
plt.figure(figsize=(12, 7))
|
||||
|
||||
# 绘制量化策略涨跌线（蓝色）
|
||||
plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["pct_chg_total"],
|
||||
label="量化策略涨跌", color='blue', linewidth=2, marker='o', markersize=4)
|
||||
plt.plot(
|
||||
symbol_bar_data["end_date_time"],
|
||||
symbol_bar_data["end_account_value_to_1"],
|
||||
label="量化策略涨跌",
|
||||
color="blue",
|
||||
linewidth=2,
|
||||
marker="o",
|
||||
markersize=4,
|
||||
)
|
||||
|
||||
# 绘制市场自然涨跌线（绿色）
|
||||
plt.plot(symbol_bar_data["end_date_time"], symbol_bar_data["end_close_to_1"],
|
||||
label="市场自然涨跌", color='green', linewidth=2, marker='s', markersize=4)
|
||||
plt.plot(
|
||||
symbol_bar_data["end_date_time"],
|
||||
symbol_bar_data["end_close_to_1"],
|
||||
label="市场自然涨跌",
|
||||
color="green",
|
||||
linewidth=2,
|
||||
marker="s",
|
||||
markersize=4,
|
||||
)
|
||||
|
||||
plt.title(f"{symbol} {bar} 量化与市场折线图_{strategy_name}",
|
||||
fontsize=14, fontweight='bold')
|
||||
plt.title(
|
||||
f"{symbol} {bar} 量化与市场折线图_{strategy_name}",
|
||||
fontsize=14,
|
||||
fontweight="bold",
|
||||
)
|
||||
plt.xlabel("时间", fontsize=12)
|
||||
plt.ylabel("涨跌变化", fontsize=12)
|
||||
plt.legend(fontsize=11)
|
||||
|
|
@ -805,22 +1030,37 @@ class MaBreakStatistics:
|
|||
label_indices.append(len(symbol_bar_data) - 1)
|
||||
|
||||
# 设置x轴标签
|
||||
plt.xticks(symbol_bar_data["end_date_time"].iloc[label_indices],
|
||||
symbol_bar_data["end_date_time"].iloc[label_indices].dt.strftime('%Y%m%d %H:%M'),
|
||||
rotation=45, ha='right')
|
||||
plt.xticks(
|
||||
symbol_bar_data["end_date_time"].iloc[label_indices],
|
||||
symbol_bar_data["end_date_time"]
|
||||
.iloc[label_indices]
|
||||
.dt.strftime("%Y%m%d %H:%M"),
|
||||
rotation=45,
|
||||
ha="right",
|
||||
)
|
||||
else:
|
||||
# 如果数据点较少，全部显示
|
||||
plt.xticks(symbol_bar_data["end_date_time"],
|
||||
symbol_bar_data["end_date_time"].dt.strftime('%Y%m%d %H:%M'),
|
||||
rotation=45, ha='right')
|
||||
plt.xticks(
|
||||
symbol_bar_data["end_date_time"],
|
||||
symbol_bar_data["end_date_time"].dt.strftime("%Y%m%d %H:%M"),
|
||||
rotation=45,
|
||||
ha="right",
|
||||
)
|
||||
|
||||
plt.tight_layout()
|
||||
|
||||
save_path = os.path.join(
|
||||
self.stats_chart_dir,
|
||||
f"{symbol}_{bar}_line_chart_{strategy_name}.png",
|
||||
)
|
||||
plt.savefig(save_path, dpi=150, bbox_inches='tight')
|
||||
if self.commission_per_share > 0:
|
||||
save_path = os.path.join(
|
||||
self.stats_chart_dir,
|
||||
f"{symbol}_{bar}_line_chart_{strategy_name}_with_commission.png",
|
||||
)
|
||||
else:
|
||||
save_path = os.path.join(
|
||||
self.stats_chart_dir,
|
||||
f"{symbol}_{bar}_line_chart_{strategy_name}_without_commission.png",
|
||||
)
|
||||
|
||||
plt.savefig(save_path, dpi=150, bbox_inches="tight")
|
||||
plt.close()
|
||||
|
||||
sheet_name = f"{symbol}_{bar}_折线图_{strategy_name}"
|
||||
|
|
|
|||
|
|
@ -4,7 +4,7 @@ from huge_volume_main import HugeVolumeMain
|
|||
from core.biz.market_monitor import create_metrics_report
|
||||
from core.db.db_market_monitor import DBMarketMonitor
|
||||
from core.wechat import Wechat
|
||||
from config import OKX_MONITOR_CONFIG, MYSQL_CONFIG, WECHAT_CONFIG
|
||||
from config import OKX_MONITOR_CONFIG, OKX_REALTIME_MONITOR_CONFIG, MYSQL_CONFIG, WECHAT_CONFIG
|
||||
from core.utils import timestamp_to_datetime, transform_date_time_to_timestamp
|
||||
import core.logger as logging
|
||||
|
||||
|
|
@ -52,6 +52,14 @@ class MarketMonitorMain:
|
|||
else:
|
||||
self.is_binance = True
|
||||
|
||||
|
||||
self.symbols = OKX_REALTIME_MONITOR_CONFIG.get("volume_monitor", {}).get(
|
||||
"symbols", ["XCH-USDT"]
|
||||
)
|
||||
self.bars = OKX_REALTIME_MONITOR_CONFIG.get("volume_monitor", {}).get(
|
||||
"bars", ["1m", "5m", "15m", "30m", "1H"]
|
||||
)
|
||||
|
||||
def get_latest_record(self):
|
||||
"""
|
||||
获取最新记录
|
||||
|
|
@ -252,13 +260,13 @@ class MarketMonitorMain:
|
|||
获取下一个长周期实时数据
|
||||
"""
|
||||
if next:
|
||||
# 获得bar在self.market_data_main.bars中的索引
|
||||
bar_index = self.market_data_main.bars.index(bar)
|
||||
if bar_index == len(self.market_data_main.bars) - 1:
|
||||
# 获得bar在self.bars中的索引
|
||||
bar_index = self.bars.index(bar)
|
||||
if bar_index == len(self.bars) - 1:
|
||||
logger.error(f"已经是最后一个bar: {bar}")
|
||||
return None
|
||||
# 获得下一个bar
|
||||
bar = self.market_data_main.bars[bar_index + 1]
|
||||
bar = self.bars[bar_index + 1]
|
||||
# 获得下一个bar的实时数据
|
||||
data = self.market_data_main.market_data.get_realtime_kline_data(
|
||||
symbol=symbol, bar=bar, end_time=end_time, limit=100
|
||||
|
|
@ -278,11 +286,11 @@ class MarketMonitorMain:
|
|||
only_output_over_mean_volume: bool = True,
|
||||
only_output_rise: bool = True,
|
||||
):
|
||||
for symbol in self.market_data_main.symbols:
|
||||
for symbol in self.symbols:
|
||||
if self.is_binance and symbol == "XCH-USDT":
|
||||
logger.info(f"币安交易所无: {symbol}")
|
||||
continue
|
||||
for bar in self.market_data_main.bars:
|
||||
for bar in self.bars:
|
||||
logger.info(
|
||||
f"开始监控: {symbol} {bar} 窗口大小: {self.window_size} 行情数据"
|
||||
)
|
||||
|
|
@ -305,7 +313,7 @@ if __name__ == "__main__":
|
|||
market_monitor_main = MarketMonitorMain()
|
||||
market_monitor_main.monitor_realtime_market(
|
||||
symbol="BTC-USDT",
|
||||
bar="5m",
|
||||
bar="1m",
|
||||
only_output_huge_volume=False,
|
||||
only_output_rise=False,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -24,9 +24,19 @@ from config import (
|
|||
|
||||
logger = logging.logger
|
||||
|
||||
|
||||
class TradeMaStrategyMain:
|
||||
def __init__(self, is_us_stock: bool = False, is_binance: bool = False):
|
||||
self.ma_break_statistics = MaBreakStatistics(is_us_stock=is_us_stock, is_binance=is_binance)
|
||||
def __init__(
|
||||
self,
|
||||
is_us_stock: bool = False,
|
||||
is_binance: bool = False,
|
||||
commission_per_share: float = 0,
|
||||
):
|
||||
self.ma_break_statistics = MaBreakStatistics(
|
||||
is_us_stock=is_us_stock,
|
||||
is_binance=is_binance,
|
||||
commission_per_share=commission_per_share,
|
||||
)
|
||||
|
||||
def batch_ma_break_statistics(self):
|
||||
"""
|
||||
|
|
@ -34,32 +44,28 @@ class TradeMaStrategyMain:
|
|||
"""
|
||||
logger.info("开始批量计算MA突破统计")
|
||||
strategy_dict = self.ma_break_statistics.main_strategy
|
||||
pct_chg_df_list = []
|
||||
account_value_chg_df_list = []
|
||||
for strategy_name, strategy_info in strategy_dict.items():
|
||||
if "macd" in strategy_name:
|
||||
# 只计算macd策略
|
||||
pct_chg_df = self.ma_break_statistics.batch_statistics(strategy_name=strategy_name)
|
||||
pct_chg_df_list.append(pct_chg_df)
|
||||
|
||||
pct_chg_df = pd.concat(pct_chg_df_list)
|
||||
|
||||
def statistics_pct_chg(self, pct_chg_df: pd.DataFrame):
|
||||
"""
|
||||
1. 将各个symbol, 各个bar, 各个策略的pct_chg_total构建为新的数据结构,如：
|
||||
symbol, bar, stratege_name_1, stratege_name_2, stratege_name_3, ...
|
||||
stratege_name_1的值, 为该策略的pct_chg_total的值
|
||||
2. 构建新的数据结构: symbol, bar, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name
|
||||
如: BCT-USDT, 15m, 均线macd结合策略2, 全均线策略
|
||||
3. 构建新的数据结构, bar, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name
|
||||
如: 15m, 均线macd结合策略2, 全均线策略
|
||||
4. 构建新的数据结构, symbol, max_pct_chg_total_strategy_name, min_pct_chg_total_strategy_name
|
||||
如: BCT-USDT, 均线macd结合策略2, 全均线策略
|
||||
"""
|
||||
logger.info("开始统计pct_chg")
|
||||
account_value_chg_df = self.ma_break_statistics.batch_statistics(
|
||||
strategy_name=strategy_name
|
||||
)
|
||||
account_value_chg_df_list.append(account_value_chg_df)
|
||||
|
||||
total_account_value_chg_df = pd.concat(account_value_chg_df_list)
|
||||
return total_account_value_chg_df
|
||||
|
||||
def statistics_account_value_chg(self, account_value_chg_df: pd.DataFrame):
|
||||
logger.info("开始统计account_value_chg")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
trade_ma_strategy_main = TradeMaStrategyMain(is_us_stock=False, is_binance=True)
|
||||
trade_ma_strategy_main.batch_ma_break_statistics()
|
||||
commission_per_share_list = [0, 0.0008]
|
||||
for commission_per_share in commission_per_share_list:
|
||||
trade_ma_strategy_main = TradeMaStrategyMain(
|
||||
is_us_stock=False,
|
||||
is_binance=True,
|
||||
commission_per_share=commission_per_share,
|
||||
)
|
||||
trade_ma_strategy_main.batch_ma_break_statistics()
|
||||
|
|
|
|||
Loading…
Reference in New Issue