713 lines
24 KiB
Python
713 lines
24 KiB
Python
import pandas as pd
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import numpy as np
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import plotly.graph_objects as go
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from datetime import datetime, timezone, timedelta
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import warnings
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import os
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import uuid
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# ========== 配置 ==========
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KLINE_XLSX = "kline_data.xlsx" # K线数据文件名
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ORDERS_XLSX = "做市策略.xls" # 订单数据文件名
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OUTPUT_HTML = "kline_with_trades.html"
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SYMBOL = "ETH-USDT" # 交易对筛选
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# 时间与对齐配置
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ORDERS_TIME_IS_LOCAL_ASIA_SH = True # 订单时间是否为东八区时间
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SNAP_TRADES_TO_NEAREST_CANDLE = True # 对齐交易点到最近的K线时间
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SNAP_TOLERANCE_MULTIPLIER = 1.5 # 对齐容忍度倍数
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# 图表尺寸配置 - 更宽更扁
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CHART_WIDTH = 2200 # 更宽的图表
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CHART_HEIGHT = 600 # 更矮的图表
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FONT_SIZE = 12 # 字体大小
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ANNOTATION_FONT_SIZE = 10 # 标注字体大小
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MARKER_SIZE = 10 # 标记大小
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LINE_WIDTH = 1.5 # 连接线宽度
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# 颜色配置 - 所有文本使用黑色
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TEXT_COLOR = "black" # 所有文本使用黑色
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TEXT_OFFSET = 10 # 文本偏移量（像素）
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# ========== 工具函数 ==========
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def parse_numeric(x):
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"""高效解析数值类型，支持多种格式"""
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if pd.isna(x):
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return np.nan
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try:
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# 尝试直接转换（大多数情况）
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return float(x)
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except:
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# 处理特殊格式
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s = str(x).replace(",", "").replace("USDT", "").replace("张", "").strip()
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if s.endswith("%"):
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s = s[:-1]
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return float(s) if s else np.nan
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def epoch_to_dt(x):
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"""将时间戳转换为上海时区时间"""
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try:
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return pd.to_datetime(int(x), unit="s", utc=True).tz_convert("Asia/Shanghai")
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except:
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return pd.NaT
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def zh_side(row):
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"""解析交易方向"""
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direction = str(row.get("方向", "")).strip()
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if "开多" in direction: return "long_open"
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if "平多" in direction: return "long_close"
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if "开空" in direction: return "short_open"
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if "平空" in direction: return "short_close"
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return "unknown"
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# ========== 数据加载与预处理 ==========
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def load_kline_data():
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"""加载并预处理K线数据"""
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if not os.path.exists(KLINE_XLSX):
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raise FileNotFoundError(f"K线数据文件不存在: {KLINE_XLSX}")
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kdf = pd.read_excel(KLINE_XLSX, dtype=str)
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kdf.columns = [str(c).strip().lower() for c in kdf.columns]
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# 验证必要列
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required_cols = {"id", "open", "close", "low", "high"}
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missing = required_cols - set(kdf.columns)
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if missing:
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raise ValueError(f"K线表缺少列: {missing}")
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# 时间转换 - 确保id是秒级时间戳
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kdf["time"] = kdf["id"].apply(epoch_to_dt)
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# 数值转换（向量化操作提升性能）
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for col in ["open", "close", "low", "high"]:
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kdf[col] = pd.to_numeric(kdf[col].apply(parse_numeric), errors="coerce")
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# 清理无效数据
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kdf = kdf.dropna(subset=["time", "open", "close", "low", "high"])
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kdf = kdf.sort_values("time").reset_index(drop=True)
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# 计算K线周期（用于交易点对齐）
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if len(kdf) >= 3:
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median_step = kdf["time"].diff().median()
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else:
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median_step = pd.Timedelta(minutes=1)
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return kdf, median_step
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def load_order_data():
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"""加载并预处理订单数据"""
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if not os.path.exists(ORDERS_XLSX):
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raise FileNotFoundError(f"订单数据文件不存在: {ORDERS_XLSX}")
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odf = pd.read_excel(ORDERS_XLSX, dtype=str)
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# 验证必要列
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need_order_cols = ["时间", "交易对", "方向", "模式", "数量(张)", "成交价", "交易额", "消耗手续费", "用户盈亏"]
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missing = set(need_order_cols) - set(odf.columns)
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if missing:
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raise ValueError(f"订单表缺少列: {missing}")
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# 筛选交易对
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if SYMBOL and "交易对" in odf.columns:
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odf = odf[odf["交易对"].astype(str).str.strip() == SYMBOL]
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# 时间处理 - 确保时间格式正确
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with warnings.catch_warnings():
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warnings.simplefilter("ignore")
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if ORDERS_TIME_IS_LOCAL_ASIA_SH:
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# 尝试多种格式解析时间
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odf["时间"] = pd.to_datetime(odf["时间"], errors="coerce", format="mixed")
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# 本地化为上海时区
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odf["时间"] = odf["时间"].dt.tz_localize("Asia/Shanghai", ambiguous="NaT", nonexistent="shift_forward")
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else:
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# 如果Excel时间已经是UTC
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odf["时间"] = pd.to_datetime(odf["时间"], utc=True, errors="coerce").dt.tz_convert("Asia/Shanghai")
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# 数值转换
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numeric_cols = {
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"数量(张)": "数量",
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"成交价": "价格",
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"交易额": "交易额_num",
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"消耗手续费": "手续费",
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"用户盈亏": "盈亏"
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}
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for src, dest in numeric_cols.items():
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odf[dest] = pd.to_numeric(odf[src].apply(parse_numeric), errors="coerce")
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# 解析交易方向
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odf["side"] = odf.apply(zh_side, axis=1)
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# 为每个订单生成唯一ID
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odf["order_id"] = [str(uuid.uuid4()) for _ in range(len(odf))]
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# 计算本金（数量 * 价格）
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odf["本金"] = odf["数量"] * odf["价格"]
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# 清理无效数据
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odf = odf.dropna(subset=["时间", "价格"])
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odf = odf.sort_values("时间").reset_index(drop=True)
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return odf
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def align_trades_to_candles(kdf, odf, median_step):
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"""将交易点对齐到最近的K线时间"""
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if not SNAP_TRADES_TO_NEAREST_CANDLE or kdf.empty or odf.empty:
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return odf.assign(时间_x=odf["时间"])
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snap_tolerance = pd.Timedelta(seconds=max(1, int(median_step.total_seconds() * SNAP_TOLERANCE_MULTIPLIER)))
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# 使用merge_asof高效对齐 - 使用方向为'backward'确保交易点对齐到前一个K线
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anchor = kdf[["time"]].copy().rename(columns={"time": "k_time"})
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odf_sorted = odf.sort_values("时间")
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# 关键优化：使用'backward'方向确保交易点对齐到前一个K线
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aligned = pd.merge_asof(
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odf_sorted,
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anchor,
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left_on="时间",
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right_on="k_time",
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direction="backward", # 使用'backward'确保交易点对齐到前一个K线
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tolerance=snap_tolerance
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)
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# 保留原始时间作为参考
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aligned["原始时间"] = aligned["时间"]
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aligned["时间_x"] = aligned["k_time"].fillna(aligned["时间"])
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return aligned
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# ========== 持仓跟踪与盈亏计算 ==========
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class PositionTracker:
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"""FIFO持仓跟踪器，支持订单走向可视化"""
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def __init__(self):
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self.long_lots = [] # (数量, 价格, 时间, 手续费, 订单ID)
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self.short_lots = [] # (数量, 价格, 时间, 手续费, 订单ID)
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self.realized_pnl = 0.0
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self.history = [] # 记录所有交易历史
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self.trade_connections = [] # 记录开平仓连接关系
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def open_long(self, qty, price, time, fee, order_id):
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"""开多仓"""
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if qty > 1e-9:
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self.long_lots.append((qty, price, time, fee, order_id))
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def close_long(self, qty, price, time, fee, order_id):
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"""平多仓"""
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remaining = qty
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local_pnl = 0.0
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connections = [] # 本次平仓的连接关系
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while remaining > 1e-9 and self.long_lots:
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lot_qty, lot_price, lot_time, lot_fee, open_order_id = self.long_lots[0]
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take = min(lot_qty, remaining)
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pnl = (price - lot_price) * take
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local_pnl += pnl
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lot_qty -= take
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remaining -= take
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# 记录开平仓连接
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connection = {
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"open_time": lot_time,
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"close_time": time,
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"open_price": lot_price,
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"close_price": price,
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"qty": take,
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"pnl": pnl,
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"type": "long",
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"open_order_id": open_order_id,
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"close_order_id": order_id
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}
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self.trade_connections.append(connection)
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connections.append(connection)
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# 记录平仓详情
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self.history.append({
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"开仓时间": lot_time,
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"平仓时间": time,
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"数量": take,
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"开仓价": lot_price,
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"平仓价": price,
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"盈亏": pnl,
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"类型": "平多",
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"开仓订单ID": open_order_id,
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"平仓订单ID": order_id
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})
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if lot_qty <= 1e-9:
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self.long_lots.pop(0)
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else:
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self.long_lots[0] = (lot_qty, lot_price, lot_time, lot_fee, open_order_id)
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local_pnl -= fee
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self.realized_pnl += local_pnl
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return local_pnl, connections
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def open_short(self, qty, price, time, fee, order_id):
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"""开空仓"""
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if qty > 1e-9:
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self.short_lots.append((qty, price, time, fee, order_id))
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def close_short(self, qty, price, time, fee, order_id):
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"""平空仓"""
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remaining = qty
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local_pnl = 0.0
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connections = [] # 本次平仓的连接关系
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while remaining > 1e-9 and self.short_lots:
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lot_qty, lot_price, lot_time, lot_fee, open_order_id = self.short_lots[0]
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take = min(lot_qty, remaining)
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pnl = (lot_price - price) * take
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local_pnl += pnl
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lot_qty -= take
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remaining -= take
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# 记录开平仓连接
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connection = {
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"open_time": lot_time,
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"close_time": time,
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"open_price": lot_price,
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"close_price": price,
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"q极": take,
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"pnl": pnl,
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"type": "short",
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"open_order_id": open_order_id,
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"close_order_id": order_id
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}
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self.trade_connections.append(connection)
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connections.append(connection)
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# 记录平仓详情
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self.history.append({
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"开仓时间": lot_time,
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"平仓时间": time,
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"数量": take,
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"开仓价": lot_price,
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"平仓价": price,
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"盈亏": pnl,
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"类型": "平空",
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"开仓订单ID": open_order_id,
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"平仓订单ID": order_id
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})
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if lot_qty <= 1e-9:
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self.short_lots.pop(0)
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else:
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self.short_lots[0] = (lot_qty, lot_price, lot_time, lot_fee, open_order_id)
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local_pnl -= fee
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self.realized_pnl += local_pnl
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return local_pnl, connections
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def calculate_pnl(odf):
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"""计算持仓盈亏和订单连接关系"""
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tracker = PositionTracker()
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all_connections = []
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for idx, r in odf.iterrows():
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qty = r["数量"]
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price = r["价格"]
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ts = r["时间"]
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fee = r["手续费"]
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side = r["side"]
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order_id = r["order_id"]
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if side == "long_open":
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tracker.open_long(qty, price, ts, fee, order_id)
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elif side == "long_close":
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_, connections = tracker.close_long(qty, price, ts, fee, order_id)
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all_connections.extend(connections)
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elif side == "short_open":
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tracker.open_short(qty, price, ts, fee, order_id)
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elif side == "short_close":
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_, connections = tracker.close_short(qty, price, ts, fee, order_id)
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all_connections.extend(connections)
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# 创建盈亏DataFrame
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if tracker.history:
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pnl_df = pd.DataFrame(tracker.history)
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# 添加对齐后的时间
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pnl_df["时间_x"] = pnl_df["平仓时间"].apply(
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lambda x: odf.loc[odf["时间"] == x, "时间_x"].values[0] if not odf.empty else x
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)
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else:
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pnl_df = pd.DataFrame()
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# 创建连接关系DataFrame
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connections_df = pd.DataFrame(all_connections) if all_connections else pd.DataFrame()
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return pnl_df, tracker.realized_pnl, connections_df
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# ========== 可视化 ==========
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def create_trade_scatter(df, name, color, symbol):
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"""创建交易点散点图"""
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if df.empty:
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return None
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# 为不同类型的交易点创建不同的文本标签
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if name == "开多":
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text = "开多\n" + df["价格"].apply(lambda x: f"{x:.2f}") + "\n" + df["本金"].apply(lambda x: f"{x:.0f}")
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elif name == "平多":
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text = "平多\n" + df["价格"].apply(lambda x: f"{x:.2f}") + "\n" + df["盈亏"].apply(lambda x: f"{x:.0f}")
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elif name == "开空":
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text = "开空\n" + df["价格"].apply(lambda x: f"{x:.2f}") + "\n" + df["本金"].apply(lambda x: f"{x:.0f}")
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elif name == "平空":
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text = "平空\n" + df["价格"].apply(lambda x: f"{x:.2f}") + "\n" + df["盈亏"].apply(lambda x: f"{x:.0f}")
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else:
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text = name
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return go.Scatter(
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x=df["时间_x"],
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y=df["价格"],
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mode="markers+text",
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name=name,
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text=text,
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textposition="middle right", # 文本放在右侧中间位置
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textfont=dict(size=ANNOTATION_FONT_SIZE, color=TEXT_COLOR), # 使用黑色文本
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marker=dict(
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size=MARKER_SIZE,
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color=color,
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symbol=symbol,
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line=dict(width=1.5, color="black")
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),
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customdata=np.stack([
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df["数量"].to_numpy(),
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df["价格"].to_numpy(),
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df["手续费"].to_numpy(),
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df.get("盈亏", np.nan).to_numpy(),
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df.get("原始时间", df["时间"]).dt.strftime("%Y-%m-%d %H:%M:%S").to_numpy(),
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df["order_id"].to_numpy(),
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df["本金"].to_numpy()
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], axis=-1),
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hovertemplate=(
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f"<b>{name}</b><br>"
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"数量: %{customdata[0]:.0f}张<br>"
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"价格: %{customdata[1]:.2f}<br>"
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"手续费: %{customdata[2]:.6f}<br>"
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"盈亏: %{customdata[3]:.4f}<br>"
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"本金: %{customdata[6]:.0f}<br>"
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"时间: %{customdata[4]}<br>"
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"订单ID: %{customdata[5]}<extra></extra>"
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)
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)
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def add_trade_connections(fig, connections_df, odf):
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"""添加开平仓连接线"""
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if connections_df.empty:
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return
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# 为盈利和亏损的连接线分别创建轨迹
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profit_lines = []
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loss_lines = []
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for _, conn in connections_df.iterrows():
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# 获取开仓点和平仓点的坐标
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open_point = odf[odf["order_id"] == conn["open_order_id"]].iloc[0]
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close_point = odf[odf["order_id"] == conn["close_order_id"]].iloc[0]
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line_data = {
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"x": [open_point["时间_x"], close_point["时间_x"]],
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"y": [open_point["价格"], close_point["价格"]],
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"pnl": conn["pnl"],
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"type": conn["type"],
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"open_order_id": conn["open_order_id"],
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"close_order_id": conn["close_order_id"]
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}
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if conn["pnl"] >= 0:
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profit_lines.append(line_data)
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else:
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loss_lines.append(line_data)
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# 添加盈利连接线（绿色）
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if profit_lines:
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x_profit = []
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y_profit = []
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customdata_profit = []
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for line in profit_lines:
|
||
x_profit.extend(line["x"])
|
||
y_profit.extend(line["y"])
|
||
x_profit.append(None)
|
||
y_profit.append(None)
|
||
|
||
# 为每个点添加自定义数据
|
||
customdata_profit.append([
|
||
line["open_order_id"],
|
||
line["close_order_id"],
|
||
line["pnl"],
|
||
line["type"]
|
||
])
|
||
customdata_profit.append([
|
||
line["open_order_id"],
|
||
line["close_order_id"],
|
||
line["pnl"],
|
||
line["type"]
|
||
])
|
||
customdata_profit.append(None)
|
||
|
||
fig.add_trace(go.Scatter(
|
||
x=x_profit,
|
||
y=y_profit,
|
||
mode="lines",
|
||
name="盈利订单",
|
||
line=dict(color="rgba(46, 204, 113, 0.7)", width=LINE_WIDTH),
|
||
hoverinfo="text",
|
||
text=[f"盈利: {d[2]:.2f}" if d else None for d in customdata_profit],
|
||
customdata=customdata_profit,
|
||
hovertemplate=(
|
||
"<b>%{text}</b><br>"
|
||
"类型: %{customdata[3]}<br>"
|
||
"开仓订单ID: %{customdata[0]}<br>"
|
||
"平仓订单ID: %{customdata[1]}<extra></extra>"
|
||
)
|
||
))
|
||
|
||
# 添加亏损连接线（红色）
|
||
if loss_lines:
|
||
x_loss = []
|
||
y_loss = []
|
||
customdata_loss = []
|
||
|
||
for line in loss_lines:
|
||
x_loss.extend(line["x"])
|
||
y_loss.extend(line["y"])
|
||
x_loss.append(None)
|
||
y_loss.append(None)
|
||
|
||
# 为每个点添加自定义数据
|
||
customdata_loss.append([
|
||
line["open_order_id"],
|
||
line["close_order_id"],
|
||
line["pnl"],
|
||
line["type"]
|
||
])
|
||
customdata_loss.append([
|
||
line["open_order_id"],
|
||
line["close_order_id"],
|
||
line["pnl"],
|
||
line["type"]
|
||
])
|
||
customdata_loss.append(None)
|
||
|
||
fig.add_trace(go.Scatter(
|
||
x=x_loss,
|
||
y=y_loss,
|
||
mode="lines",
|
||
name="亏损订单",
|
||
line=dict(color="rgba(231, 76, 60, 0.7)", width=LINE_WIDTH),
|
||
hoverinfo="text",
|
||
text=[f"亏损: {abs(d[2]):.2f}" if d else None for d in customdata_loss],
|
||
customdata=customdata_loss,
|
||
hovertemplate=(
|
||
"<b>%{text}</b><br>"
|
||
"类型: %{customdata[3]}<br>"
|
||
"开仓订单ID: %{customdata[0]}<极>"
|
||
"平仓订单ID: %{customdata[1]}<extra></extra>"
|
||
)
|
||
))
|
||
|
||
|
||
def generate_chart(kdf, odf, pnl_df, cum_realized, connections_df):
|
||
"""生成K线图与交易标注"""
|
||
fig = go.Figure()
|
||
|
||
# K线主图
|
||
fig.add_trace(go.Candlestick(
|
||
x=kdf["time"],
|
||
open=kdf["open"],
|
||
high=kdf["high"],
|
||
low=kdf["low"],
|
||
close=kdf["close"],
|
||
name="K线",
|
||
increasing_line_color="#2ecc71",
|
||
decreasing_line_color="#e74c3c"
|
||
))
|
||
|
||
# 添加交易点
|
||
trade_types = [
|
||
(odf[odf["side"] == "long_open"], "开多", "#2ecc71", "triangle-up"),
|
||
(odf[odf["side"] == "long_close"], "平多", "#27ae60", "circle"),
|
||
(odf[odf["side"] == "short_open"], "开空", "#e74c3c", "triangle-down"),
|
||
(odf[odf["side"] == "short_close"], "平空", "#c0392b", "x")
|
||
]
|
||
|
||
for data, name, color, symbol in trade_types:
|
||
trace = create_trade_scatter(data, name, color, symbol)
|
||
if trace:
|
||
fig.add_trace(trace)
|
||
|
||
# 添加开平仓连接线
|
||
add_trade_connections(fig, connections_df, odf)
|
||
|
||
# 计算时间范围，确保所有点都显示在图表中
|
||
all_times = pd.concat([kdf["time"], odf["时间_x"]])
|
||
min_time = all_times.min() - pd.Timedelta(minutes=10)
|
||
max_time = all_times.max() + pd.Timedelta(minutes=10)
|
||
|
||
# 计算价格范围，确保所有点都显示在图表中
|
||
min_price = min(kdf["low"].min(), odf["价格"].min()) * 0.99
|
||
max_price = max(kdf["high"].max(), odf["价格"].max()) * 1.01
|
||
|
||
# 布局配置 - 更宽更扁的图表
|
||
fig.update_layout(
|
||
xaxis_title="时间",
|
||
yaxis_title="价格 (USDT)",
|
||
legend=dict(
|
||
orientation="h",
|
||
yanchor="bottom",
|
||
y=1.02,
|
||
xanchor="left",
|
||
x=0,
|
||
font=dict(size=FONT_SIZE, color=TEXT_COLOR) # 图例文字使用黑色
|
||
),
|
||
xaxis=dict(
|
||
rangeslider=dict(visible=False),
|
||
type="date",
|
||
gridcolor="rgba(128, 128, 128, 0.2)",
|
||
range=[min_time, max_time], # 设置时间范围
|
||
title_font=dict(color=TEXT_COLOR), # 坐标轴标题使用黑色
|
||
tickfont=dict(color=TEXT_COLOR) # 刻度标签使用黑色
|
||
),
|
||
yaxis=dict(
|
||
gridcolor="rgba(128, 128, 128, 0.2)",
|
||
range=[min_price, max_price], # 设置价格范围
|
||
title_font=dict(color=TEXT_COLOR), # 坐标轴标题使用黑色
|
||
tickfont=dict(color=TEXT_COLOR) # 刻度标签使用黑色
|
||
),
|
||
hovermode="x unified",
|
||
hoverlabel=dict(
|
||
namelength=-1,
|
||
bgcolor="rgba(255, 255, 255, 0.9)",
|
||
font_size=FONT_SIZE,
|
||
font_color=TEXT_COLOR # 悬停标签文字使用黑色
|
||
),
|
||
margin=dict(l=50, r=50, t=80, b=50),
|
||
plot_bgcolor="rgba(240, 240, 240, 1)",
|
||
width=CHART_WIDTH, # 使用配置的宽度
|
||
height=CHART_HEIGHT, # 使用配置的高度
|
||
font=dict(size=FONT_SIZE, color=TEXT_COLOR), # 全局字体大小和颜色
|
||
|
||
# 增强交互性配置
|
||
dragmode="pan", # 默认拖拽模式为平移
|
||
clickmode="event+select", # 点击模式
|
||
selectdirection="h", # 水平选择方向
|
||
modebar=dict(
|
||
orientation="h", # 水平方向工具栏
|
||
bgcolor="rgba(255, 255, 255, 0.7)", # 半透明背景
|
||
color="rgba(0, 0, 0, 0.7)", # 图标颜色
|
||
activecolor="rgba(0, 0, 0, 0.9)" # 激活图标颜色
|
||
)
|
||
)
|
||
|
||
# 添加模式栏按钮
|
||
fig.update_layout(
|
||
modebar_add=[
|
||
"zoom2d",
|
||
"pan2d",
|
||
"select2d",
|
||
"lasso2d",
|
||
"zoomIn2d",
|
||
"zoomOut2d",
|
||
"autoScale2d",
|
||
"resetScale2d",
|
||
"toImage"
|
||
]
|
||
)
|
||
|
||
# 配置缩放行为 - 确保滚轮缩放正常工作
|
||
fig.update_xaxes(
|
||
autorange=False,
|
||
fixedrange=False, # 允许缩放
|
||
constrain="domain", # 约束在域内
|
||
rangeslider=dict(visible=False) # 禁用范围滑块
|
||
)
|
||
|
||
fig.update_yaxes(
|
||
autorange=False,
|
||
fixedrange=False, # 允许缩放
|
||
scaleanchor="x", # 保持纵横比
|
||
scaleratio=1, # 缩放比例
|
||
constrain="domain" # 约束在域内
|
||
)
|
||
|
||
# 保存并打开结果 - 启用滚轮缩放
|
||
fig.write_html(
|
||
OUTPUT_HTML,
|
||
include_plotlyjs="cdn",
|
||
auto_open=True,
|
||
config={
|
||
'scrollZoom': True, # 启用滚轮缩放
|
||
'displayModeBar': True, # 显示工具栏
|
||
'displaylogo': False, # 隐藏Plotly标志
|
||
'responsive': True # 响应式布局
|
||
}
|
||
)
|
||
print(f"图表已生成: {OUTPUT_HTML}")
|
||
|
||
# 返回盈亏详情
|
||
if not pnl_df.empty:
|
||
pnl_df.to_csv("pnl_details.csv", index=False)
|
||
print(f"盈亏详情已保存: pnl_details.csv")
|
||
|
||
if not connections_df.empty:
|
||
connections_df.to_csv("trade_connections.csv", index=False)
|
||
print(f"订单连接关系已保存: trade_connections.csv")
|
||
|
||
return fig
|
||
|
||
|
||
# ========== 主执行流程 ==========
|
||
def main():
|
||
print("开始处理数据...")
|
||
|
||
# 加载数据
|
||
kdf, median_step = load_kline_data()
|
||
odf = load_order_data()
|
||
|
||
print(f"加载K线数据: {len(kdf)}条")
|
||
print(f"加载订单数据: {len(odf)}条")
|
||
|
||
# 对齐交易时间
|
||
odf = align_trades_to_candles(kdf, odf, median_step)
|
||
|
||
# 检查时间范围
|
||
kline_min_time = kdf["time"].min()
|
||
kline_max_time = kdf["time"].max()
|
||
order_min_time = odf["时间"].min()
|
||
order_max_time = odf["时间"].max()
|
||
|
||
print(f"K线时间范围: {kline_min_time} 至 {kline_max_time}")
|
||
print(f"订单时间范围: {order_min_time} 至 {order_max_time}")
|
||
|
||
# 检查是否有订单在K线时间范围外
|
||
outside_orders = odf[(odf["时间"] < kline_min_time) | (odf["时间"] > kline_max_time)]
|
||
if not outside_orders.empty:
|
||
print(f"警告: 有 {len(outside_orders)} 个订单在K线时间范围外")
|
||
print(outside_orders[["时间", "方向", "价格"]])
|
||
|
||
# 计算盈亏和订单连接关系
|
||
pnl_df, cum_realized, connections_df = calculate_pnl(odf)
|
||
print(f"累计已实现盈亏: {cum_realized:.2f} USDT")
|
||
print(f"订单连接关系: {len(connections_df)}条")
|
||
|
||
# 生成图表
|
||
generate_chart(kdf, odf, pnl_df, cum_realized, connections_df)
|
||
print("处理完成")
|
||
|
||
|
||
if __name__ == "__main__":
|
||
with warnings.catch_warnings():
|
||
warnings.simplefilter("ignore")
|
||
main() |