calculate_pnl.py

import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from typing import Dict


def calculate_pnl(
    product: str,
    exchange: str,
    data: pd.DataFrame,
    max_pos: int,
    open_threshold: float,
    close_threshold: float
):
    """
    此函数用于计算一天的收益。
    :param product: 交易品种
    :param exchange: 交易所
    :param data: 交易数据， 必须包含 predict, bid, ask, bid_vol, ask_vol 这五列数据
    :param max_pos: 最大仓位
    :param open_threshold: 开仓阈值
    :param close_threshold: 平仓阈值
    :return: {"profit": 毛利润, "commission": 手续费, "trade_n": 交易手数}
    """
    # load other data
    ############################## change dir here ##############################
    fee_data = pd.read_csv("./other_data/commission-2023-12-18.csv")
    volume_data = pd.read_csv("./other_data/DailyMarketVolume_20231218.csv")
    inst_data = pd.read_csv("./other_data/instrument-2023-12-18.csv")
    #############################################################################
    lock_position_list = ["IC", "IF", "IH", "IM", "AP", "lh", "sn", "ni", ]
    lock_position = product in lock_position_list
    if exchange in ["SHFE", "DCE", "GFEX"]:
        product = product.lower()
    else:
        product = product.upper()
    tmp = volume_data[volume_data['ProductId'] == product]
    if len(tmp) != 1:
        raise ValueError("There is {} volume record for {} ".format(len(tmp), product))
    symbol = str(tmp['Symbol1'].values[0])
    tmp = fee_data[fee_data["InstrumentID"] == symbol]
    if len(tmp) < 1:
        raise ValueError("{} commission record less than 1".format(symbol))
    open_ratio = tmp["OpenRatioByMoney"].values[0]
    open_fix = tmp["OpenRatioByVolume"].values[0]
    if lock_position:
        close_ratio = tmp["CloseRatioByMoney"].values[0]
        close_fix = tmp["CloseRatioByVolume"].values[0]
    else:
        close_ratio = tmp["CloseTodayRatioByMoney"].values[0]
        close_fix = tmp["CloseTodayRatioByVolume"].values[0]
    inst_data = inst_data[inst_data["InstrumentID"] == symbol]
    assert len(inst_data) > 0, "{} inst record is 0".format(symbol)
    multiplier = int(inst_data["VolumeMultiple"].values[0])
    px_tick = inst_data["PriceTick"].values[0]
    val_multip = multiplier * px_tick
    # calculate trade information
    # predict = data["predict"].values
    predict = data["predict_0226"].values
    bid = data["bid"].values
    ask = data["ask"].values
    bid_vol = data["bid_vol"].values
    ask_vol = data["ask_vol"].values
    cur_pos = 0
    revenue = [0]
    average_profit = 0
    commission = [0]
    all_account = [0]
    trade_n = 0
    for i in range(predict.shape[0]):
        trade_signal = predict[i]
        ord_amt = 0
        dir = None
        if cur_pos == 0:
            if trade_signal >= open_threshold:
                dir = "BUY"
                ord_amt = min(max_pos, ask_vol[i])
            elif trade_signal <= -1 * open_threshold:
                dir = "SELL"
                ord_amt = min(max_pos, bid_vol[i])
        elif cur_pos > 0:
            if trade_signal >= open_threshold and cur_pos < max_pos:
                dir = "BUY"
                ord_amt = max(0, min(max_pos - cur_pos, ask_vol[i]))
            elif trade_signal <= -1 * close_threshold:
                dir = "SELL"
                if trade_signal <= -1 * open_threshold:
                    ord_amt = min(max_pos + cur_pos, bid_vol[0])
                else:
                    ord_amt = min(cur_pos, bid_vol[0])
        else:  # cur_pos < 0
            if trade_signal <= -1 * open_threshold and cur_pos > -max_pos:
                dir = "SELL"
                ord_amt = max(0, min(max_pos + cur_pos, bid_vol[i]))
            elif trade_signal >= close_threshold:
                dir = "BUY"
                if trade_signal >= open_threshold:
                    ord_amt = min(max_pos - cur_pos, ask_vol[i])
                else:
                    ord_amt = min(-cur_pos, ask_vol[i])
        if ord_amt != 0 and dir is not None:
            # calculate pnl & update cur_pos
            if dir == 'SELL':
                px = bid[i]
                if cur_pos >= ord_amt:
                    diff = px * ord_amt - average_profit * ord_amt
                    commission.append(ord_amt * (px * close_ratio * val_multip + close_fix))
                elif cur_pos <= 0:
                    diff = 0
                    average_profit = (average_profit * abs(cur_pos) + ord_amt * px) / (abs(cur_pos) + ord_amt)
                    commission.append(ord_amt * (px * open_ratio * val_multip + open_fix))
                else:
                    diff = px * abs(cur_pos) - average_profit * abs(cur_pos)
                    commission.append(abs(cur_pos) * (px * close_ratio * val_multip + close_fix) + \
                                      (ord_amt - abs(cur_pos)) * (px * open_ratio * val_multip + open_fix))
                    average_profit = px
                cur_pos = cur_pos - ord_amt
                all_account.append(cur_pos)
            elif dir == 'BUY':
                px = ask[i]
                if cur_pos <= ord_amt * -1:
                    diff = average_profit * ord_amt - px * ord_amt
                    commission.append(ord_amt * (px * close_ratio * val_multip + close_fix))
                elif cur_pos >= 0:
                    diff = 0
                    average_profit = (average_profit * abs(cur_pos) + ord_amt * px) / (abs(cur_pos) + ord_amt)
                    commission.append(ord_amt * (px * open_ratio * val_multip + open_fix))
                else:
                    diff = average_profit * abs(cur_pos) - px * abs(cur_pos)
                    commission.append(abs(cur_pos) * (px * close_ratio * val_multip + close_fix) + \
                                      (ord_amt - abs(cur_pos)) * (px * open_ratio * val_multip + open_fix))
                    average_profit = px
                cur_pos = cur_pos + ord_amt
                all_account.append(cur_pos)
            else:
                raise ValueError("Unknown dir: {}".format(dir))
            revenue.append(diff * val_multip)
            trade_n += ord_amt
    return {"revenue": revenue, "commission": commission, "trade_n": trade_n}  # "position": all_account,


def sharpe_ratio(
    pl: np.ndarray,
    base_rate: float = 0,
):
    """
    此代码用于计算夏普率
    """
    if len(pl) == 0:
        return np.NaN
    pl2 = pl - base_rate
    pl2_std = np.std(pl2)
    if pl2_std == 0:
        return np.NaN
    return np.mean(pl2) / pl2_std


def sharpe_ratio_day(
    net_pl: np.ndarray
):
    """
    此代码用于计算夏普率（按照天数计算）
    """
    n_days = len(net_pl)
    if n_days == 0:
        return np.NaN
    mult = float(len(net_pl)) * 250 / n_days
    return sharpe_ratio(net_pl) * np.sqrt(mult)


def plot_pnl(revenue: np.ndarray, commission: np.ndarray, rebate_ratio: float, save_dir: str, title: str):
    """
    此代码用于绘制收益曲线
    """
    plt.figure(figsize = (10, 5))
    plt.step(range(len(revenue)), np.cumsum(revenue), color = 'g', label = 'Revenue')
    plt.step(range(len(commission)), np.cumsum(commission), color = 'r', label = 'Commission')
    plt.step(range(len(revenue)), np.cumsum(revenue) - np.cumsum(commission), color = 'b', linestyle = '--', label = 'Profit')
    plt.step(range(len(revenue)), np.cumsum(revenue) - np.cumsum(commission * (1 - rebate_ratio)), color = 'b', label = 'Net P&L')
    plt.axhline(y = 0, color = 'g', linestyle = '--')
    plt.title(title)
    plt.legend()
    plt.savefig(save_dir)


def portfolio(
    trade_data: Dict,
    max_pos: int,
    threshold: tuple,
    rebate_ratio: float,
    save_dir: str,
):
    """
    此代码用于生成回测报告（csv文件）和绘制收益曲线
    :param trade_data: 交易数据
    :param max_pos: 最大仓位
    :param threshold: 交易阈值
    :param rebate_ratio: 手续费返还比例
    :param save_dir: 报告保存路径
    :return: None
    """
    start_day = min(list(trade_data.keys()))
    end_day = max(list(trade_data.keys()))
    days_n = len(list(trade_data.keys()))
    revenue_list = []
    commission_list = []
    trade_n = 0
    for x in trade_data.values():
        revenue_list.append(sum(x["revenue"]))
        commission_list.append(sum(x["commission"]))
        trade_n += x["trade_n"]
    net_pnl_list = np.array(revenue_list) - (1 - rebate_ratio) * np.array(commission_list)
    sr = sharpe_ratio_day(net_pnl_list)
    report_path = os.path.join(save_dir, "backtest.csv")
    figure_path = os.path.join(save_dir, "figures")
    os.makedirs(figure_path, exist_ok = True)
    if os.path.exists(report_path):
        report = pd.read_csv(report_path)
    else:
        report = pd.DataFrame(
            columns = [
                    "StartDay", "EndDay", "Days", "Threshold", "MaxPos",  "Revenue", "Fee", "Profit", "PNL",
                    "AvgPnl", "Sharpe", "Trades", "TO",
                ]
        )
    new_row = pd.DataFrame(
        {
            "StartDay": [start_day],  # 第一个交易日
            "EndDay": [end_day],  # 最后一个交易日
            "Days": [days_n],   # 交易日数量
            "Threshold": [threshold],  # 交易阈值
            "MaxPos": [max_pos],   # 最大仓位
            "Revenue": [sum(revenue_list)],   # 费前收益
            "Fee": [sum(commission_list)],  # 手续费
            "Profit": [sum(revenue_list) - sum(commission_list)],  # 费后收益
            "PNL": [sum(net_pnl_list)],  # 净收益（加上手续费返还：PNL = Profit + rebate_ratio * Fee）
            "AvgPnl": [sum(net_pnl_list) / trade_n if trade_n > 0 else 0],  # 每笔交易的净利润
            "Sharpe": [sr],  # 夏普率
            "Trades": [trade_n],  # 交易手数
            "TO": [trade_n / days_n / max_pos if trade_n > 0 else 0],  # 换手率
        }
    )
    report = pd.concat([report, new_row], ignore_index = True)
    report.to_csv(report_path, index = False)
    plot_pnl(
        revenue = np.concatenate([x["revenue"] for x in trade_data.values()]),
        commission = np.concatenate([x["commission"] for x in trade_data.values()]),
        rebate_ratio = rebate_ratio,
        save_dir = os.path.join(figure_path, "backtest_{:.03f}_{:.03f}.png".format(threshold[0], threshold[1])),
        title = "Backtest {} - {}".format(str(start_day), str(end_day))
    )