Upload 4 files

calculate_pnl.py

@@ -0,0 +1,252 @@
+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))
+    )

demo.py

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+"""
+运行此脚本可以得到回测结果。
+你需要先修改此脚本中的data_dir和save_dir、以及calculate_pnl.py中的fee_data、volume_data、inst_data五个地方的路径为你本地机器的路径，
+之后就可以运行成功。
+"""
+
+from calculate_pnl import calculate_pnl, portfolio
+import pandas as pd
+import os
+from datetime import date, timedelta
+
+
+product_para = {
+    # "FG": {"exchange": "CZCE", "open_threshold": 0.88, "close_threshold": 0.792},
+    # "sc": {"exchange": "DCE", "open_threshold": 0.46, "close_threshold": 0.322},
+    "FG": {"exchange": "CZCE", "open_threshold": 0.25, "close_threshold": 0.15},
+    "sc": {"exchange": "DCE", "open_threshold": 0.46, "close_threshold": 0.322},
+}
+############################## change dir here ##############################
+# data_dir = "./generate_data/"  # 保存数据的路径
+# save_dir = "./backtest/"     # 保存回测结果的路径
+
+data_dir = "./res/"  # 保存数据的路径
+save_dir = "./backtest/"     # 保存回测结果的路径
+#############################################################################
+
+if __name__ == "__main__":
+    # product parameters
+    product = "FG"  # 品种名称
+    exchange = product_para[product]["exchange"]  # 交易所
+    open_threshold = product_para[product]["open_threshold"]  # 开仓阈值
+    close_threshold = product_para[product]["close_threshold"]  # 平仓阈值
+    max_pos = 1  # 最大仓位
+
+    # backtest parameters
+    start_day = date(2023, 12, 18)  # 回测起始日
+    end_day = date(2023, 12, 18)   # 回测结束日
+    rebate_ratio = 0.4  # 手续费返还比例，0.4代表40%的手续费最后会被返还给给账户
+
+    # backtest
+    day = start_day
+    backtest_data = {}
+    while day <= end_day:
+        path = os.path.join(data_dir, product, "{}.csv".format(day))
+        print(path, os.path.exists(path))
+        if not os.path.exists(path):
+            day += timedelta(days = 1)
+            continue
+        tmp = pd.read_csv(path)
+        backtest_data[day] = calculate_pnl(
+            product = product,
+            exchange = exchange,
+            data = tmp,
+            max_pos = max_pos,
+            open_threshold = open_threshold,
+            close_threshold = close_threshold,
+        )
+        day += timedelta(days = 1)
+        continue
+
+    # generate pnl data
+    portfolio(
+        trade_data = backtest_data,
+        max_pos = max_pos,
+        threshold = (open_threshold, close_threshold),
+        rebate_ratio = rebate_ratio,
+        save_dir = os.path.join(save_dir, product),
+    )

notebook/data_eda.ipynb

@@ -0,0 +1,341 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os, sys, json, collections\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### FG（玻璃）数据"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(80808, 127)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>MidPxChange_1</th>\n",
+       "      <th>MidPxChange_5</th>\n",
+       "      <th>MidPxChange_10</th>\n",
+       "      <th>MidPxChange_20</th>\n",
+       "      <th>MidPxChange_50</th>\n",
+       "      <th>MidPxChange_100</th>\n",
+       "      <th>MidPxChange_200</th>\n",
+       "      <th>MidPxVolRatio_1</th>\n",
+       "      <th>MidPxVolRatio_5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>label1</th>\n",
+       "      <th>label2</th>\n",
+       "      <th>last</th>\n",
+       "      <th>bid</th>\n",
+       "      <th>ask</th>\n",
+       "      <th>bid_vol</th>\n",
+       "      <th>ask_vol</th>\n",
+       "      <th>volume</th>\n",
+       "      <th>turnover</th>\n",
+       "      <th>predict</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-05-31 21:00:00.500</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-0.00303</td>\n",
+       "      <td>-0.003030</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-7.0</td>\n",
+       "      <td>-2.285714</td>\n",
+       "      <td>1406.0</td>\n",
+       "      <td>1405.0</td>\n",
+       "      <td>1406.0</td>\n",
+       "      <td>594.0</td>\n",
+       "      <td>96.0</td>\n",
+       "      <td>2502.0</td>\n",
+       "      <td>3524194.0</td>\n",
+       "      <td>0.245262</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-05-31 21:00:00.750</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>-1.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>-0.001105</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-7.0</td>\n",
+       "      <td>-2.285714</td>\n",
+       "      <td>1406.0</td>\n",
+       "      <td>1405.0</td>\n",
+       "      <td>1406.0</td>\n",
+       "      <td>460.0</td>\n",
+       "      <td>1102.0</td>\n",
+       "      <td>3077.0</td>\n",
+       "      <td>4332491.0</td>\n",
+       "      <td>-0.709275</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 127 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Unnamed: 0  MidPxChange_1  MidPxChange_5  MidPxChange_10  \\\n",
+       "0  2023-05-31 21:00:00.500           -1.0           -1.0            -1.0   \n",
+       "1  2023-05-31 21:00:00.750            0.0           -1.0            -1.0   \n",
+       "\n",
+       "   MidPxChange_20  MidPxChange_50  MidPxChange_100  MidPxChange_200  \\\n",
+       "0            -1.0            -1.0             -1.0             -1.0   \n",
+       "1            -1.0            -1.0             -1.0             -1.0   \n",
+       "\n",
+       "   MidPxVolRatio_1  MidPxVolRatio_5  ...  label1    label2    last     bid  \\\n",
+       "0         -0.00303        -0.003030  ...    -7.0 -2.285714  1406.0  1405.0   \n",
+       "1          0.00000        -0.001105  ...    -7.0 -2.285714  1406.0  1405.0   \n",
+       "\n",
+       "      ask  bid_vol  ask_vol  volume   turnover   predict  \n",
+       "0  1406.0    594.0     96.0  2502.0  3524194.0  0.245262  \n",
+       "1  1406.0    460.0   1102.0  3077.0  4332491.0 -0.709275  \n",
+       "\n",
+       "[2 rows x 127 columns]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filepath = '/Users/saidcalculationboy/Coding/GenWealth/data/generate_data/FG/2023-06-01.csv'\n",
+    "FG_1_df = pd.read_csv(filepath)\n",
+    "print(FG_1_df.shape)\n",
+    "FG_1_df.head(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(84453, 127)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>MidPxChange_1</th>\n",
+       "      <th>MidPxChange_5</th>\n",
+       "      <th>MidPxChange_10</th>\n",
+       "      <th>MidPxChange_20</th>\n",
+       "      <th>MidPxChange_50</th>\n",
+       "      <th>MidPxChange_100</th>\n",
+       "      <th>MidPxChange_200</th>\n",
+       "      <th>MidPxVolRatio_1</th>\n",
+       "      <th>MidPxVolRatio_5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>label1</th>\n",
+       "      <th>label2</th>\n",
+       "      <th>last</th>\n",
+       "      <th>bid</th>\n",
+       "      <th>ask</th>\n",
+       "      <th>bid_vol</th>\n",
+       "      <th>ask_vol</th>\n",
+       "      <th>volume</th>\n",
+       "      <th>turnover</th>\n",
+       "      <th>predict</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-05-31 21:00:01.000</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.019231</td>\n",
+       "      <td>0.019231</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5.142857</td>\n",
+       "      <td>5.142857</td>\n",
+       "      <td>4905.0</td>\n",
+       "      <td>4901.0</td>\n",
+       "      <td>4905.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>559.0</td>\n",
+       "      <td>2744938.0</td>\n",
+       "      <td>-0.014487</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-05-31 21:00:01.250</td>\n",
+       "      <td>-0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.006173</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>6.035714</td>\n",
+       "      <td>6.035714</td>\n",
+       "      <td>4904.0</td>\n",
+       "      <td>4902.0</td>\n",
+       "      <td>4903.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>640.0</td>\n",
+       "      <td>3141997.0</td>\n",
+       "      <td>-0.093558</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 127 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Unnamed: 0  MidPxChange_1  MidPxChange_5  MidPxChange_10  \\\n",
+       "0  2023-05-31 21:00:01.000            0.5            0.5             0.5   \n",
+       "1  2023-05-31 21:00:01.250           -0.5            0.0             0.0   \n",
+       "\n",
+       "   MidPxChange_20  MidPxChange_50  MidPxChange_100  MidPxChange_200  \\\n",
+       "0             0.5             0.5              0.5              0.5   \n",
+       "1             0.0             0.0              0.0              0.0   \n",
+       "\n",
+       "   MidPxVolRatio_1  MidPxVolRatio_5  ...    label1    label2    last     bid  \\\n",
+       "0         0.019231         0.019231  ...  5.142857  5.142857  4905.0  4901.0   \n",
+       "1        -0.006173         0.000000  ...  6.035714  6.035714  4904.0  4902.0   \n",
+       "\n",
+       "      ask  bid_vol  ask_vol  volume   turnover   predict  \n",
+       "0  4905.0      4.0      5.0   559.0  2744938.0 -0.014487  \n",
+       "1  4903.0      1.0      3.0   640.0  3141997.0 -0.093558  \n",
+       "\n",
+       "[2 rows x 127 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filepath = '/Users/saidcalculationboy/Coding/GenWealth/data/generate_data/sc/2023-06-01.csv'\n",
+    "sc_1_df = pd.read_csv(filepath)\n",
+    "print(sc_1_df.shape)\n",
+    "sc_1_df.head(2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pytorch",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

notebook/nn_baseline.ipynb

@@ -0,0 +1,259 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "from tensorflow.keras.models import Sequential\n",
+    "from tensorflow.keras.layers import Dense"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/10\n",
+      "2526/2526 [==============================] - 2s 757us/step - loss: 0.6998 - val_loss: 0.9270\n",
+      "Epoch 2/10\n",
+      "2526/2526 [==============================] - 2s 745us/step - loss: 0.7014 - val_loss: 0.9254\n",
+      "Epoch 3/10\n",
+      "2526/2526 [==============================] - 2s 647us/step - loss: 0.6917 - val_loss: 0.9253\n",
+      "Epoch 4/10\n",
+      "2526/2526 [==============================] - 2s 657us/step - loss: 0.6858 - val_loss: 0.9270\n",
+      "Epoch 5/10\n",
+      "2526/2526 [==============================] - 2s 718us/step - loss: 0.6889 - val_loss: 0.9264\n",
+      "Epoch 6/10\n",
+      "2526/2526 [==============================] - 2s 730us/step - loss: 0.6945 - val_loss: 0.9289\n",
+      "Epoch 7/10\n",
+      "2526/2526 [==============================] - 2s 651us/step - loss: 0.6867 - val_loss: 0.9274\n",
+      "Epoch 8/10\n",
+      "2526/2526 [==============================] - 2s 672us/step - loss: 0.6895 - val_loss: 0.9267\n",
+      "Epoch 9/10\n",
+      "2526/2526 [==============================] - 2s 656us/step - loss: 0.6938 - val_loss: 0.9278\n",
+      "Epoch 10/10\n",
+      "2526/2526 [==============================] - 2s 634us/step - loss: 0.6906 - val_loss: 0.9264\n",
+      "2539/2539 [==============================] - 1s 334us/step - loss: 0.9264\n",
+      "Test Loss: 0.9263737201690674\n"
+     ]
+    }
+   ],
+   "source": [
+    "# 读取数据\n",
+    "train_data = pd.read_csv('../generate_data/FG/2023-06-01.csv')\n",
+    "test_data = pd.read_csv('../generate_data/FG/2023-12-18.csv')\n",
+    "\n",
+    "# 选择因子\n",
+    "selected_factors = ['MidPxChange_1', 'MidPxVolRatio_1']\n",
+    "X_train = train_data[selected_factors]  # 选择你认为合适的因子列\n",
+    "y_train = train_data['label1']\n",
+    "X_test = test_data[selected_factors]\n",
+    "y_test = test_data['label1']\n",
+    "\n",
+    "\n",
+    "# 数据标准化\n",
+    "scaler = StandardScaler()\n",
+    "X_train_scaled = scaler.fit_transform(X_train)\n",
+    "X_test_scaled = scaler.transform(X_test)\n",
+    "\n",
+    "# 构建神经网络模型\n",
+    "model = Sequential([\n",
+    "    Dense(64, activation='relu', input_shape=(X_train_scaled.shape[1],)),\n",
+    "    Dense(32, activation='relu'),\n",
+    "    Dense(1)  # 输出层，因为是回归问题，所以没有激活函数\n",
+    "])\n",
+    "\n",
+    "# 编译模型\n",
+    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
+    "\n",
+    "# 训练模型\n",
+    "model.fit(X_train_scaled, y_train, epochs=10, batch_size=32, validation_data=(X_test_scaled, y_test))\n",
+    "\n",
+    "# 评估模型\n",
+    "loss = model.evaluate(X_test_scaled, y_test)\n",
+    "print('Test Loss:', loss)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "predictions = model.predict(X_test)\n",
+    "test_data['predict_0226'] = predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test_data.to_csv('/Users/saidcalculationboy/Coding/GenWealth/data/res/FG/2023-12-18.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Unnamed: 0</th>\n",
+       "      <th>MidPxChange_1</th>\n",
+       "      <th>MidPxChange_5</th>\n",
+       "      <th>MidPxChange_10</th>\n",
+       "      <th>MidPxChange_20</th>\n",
+       "      <th>MidPxChange_50</th>\n",
+       "      <th>MidPxChange_100</th>\n",
+       "      <th>MidPxChange_200</th>\n",
+       "      <th>MidPxVolRatio_1</th>\n",
+       "      <th>MidPxVolRatio_5</th>\n",
+       "      <th>...</th>\n",
+       "      <th>label2</th>\n",
+       "      <th>last</th>\n",
+       "      <th>bid</th>\n",
+       "      <th>ask</th>\n",
+       "      <th>bid_vol</th>\n",
+       "      <th>ask_vol</th>\n",
+       "      <th>volume</th>\n",
+       "      <th>turnover</th>\n",
+       "      <th>predict</th>\n",
+       "      <th>predict_0226</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-12-17 21:00:00.250</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-0.001403</td>\n",
+       "      <td>-0.001403</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>1807.0</td>\n",
+       "      <td>1807.0</td>\n",
+       "      <td>1808.0</td>\n",
+       "      <td>75.0</td>\n",
+       "      <td>47.0</td>\n",
+       "      <td>4007.0</td>\n",
+       "      <td>7245815.0</td>\n",
+       "      <td>0.269611</td>\n",
+       "      <td>0.190654</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-17 21:00:00.500</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>-2.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.001218</td>\n",
+       "      <td>...</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>1807.0</td>\n",
+       "      <td>1807.0</td>\n",
+       "      <td>1808.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>12.0</td>\n",
+       "      <td>4223.0</td>\n",
+       "      <td>7636347.0</td>\n",
+       "      <td>0.889756</td>\n",
+       "      <td>0.027161</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2 rows × 128 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Unnamed: 0  MidPxChange_1  MidPxChange_5  MidPxChange_10  \\\n",
+       "0  2023-12-17 21:00:00.250           -2.0           -2.0            -2.0   \n",
+       "1  2023-12-17 21:00:00.500            0.0           -2.0            -2.0   \n",
+       "\n",
+       "   MidPxChange_20  MidPxChange_50  MidPxChange_100  MidPxChange_200  \\\n",
+       "0            -2.0            -2.0             -2.0             -2.0   \n",
+       "1            -2.0            -2.0             -2.0             -2.0   \n",
+       "\n",
+       "   MidPxVolRatio_1  MidPxVolRatio_5  ...  label2    last     bid     ask  \\\n",
+       "0        -0.001403        -0.001403  ...     8.1  1807.0  1807.0  1808.0   \n",
+       "1         0.000000        -0.001218  ...     8.1  1807.0  1807.0  1808.0   \n",
+       "\n",
+       "   bid_vol  ask_vol  volume   turnover   predict  predict_0226  \n",
+       "0     75.0     47.0  4007.0  7245815.0  0.269611      0.190654  \n",
+       "1     25.0     12.0  4223.0  7636347.0  0.889756      0.027161  \n",
+       "\n",
+       "[2 rows x 128 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_data.head(2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "pytorch",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}