feat: predict the top 5 stocks

predict_top_5.py

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+import json
+import sqlite3
+from datetime import datetime, timedelta
+import pickle
+import pandas as pd
+from sklearn.linear_model import LinearRegression
+import requests
+
+
+def rolling_gptprod_profit(df_test, price):
+
+    # 轉換日期格式
+    df_test["report_date"] = pd.to_datetime(df_test["report_date"])
+    df_test["end_date"] = pd.to_datetime(df_test["end_date"])
+    try:
+        price["date"] = pd.to_datetime(price["date"])
+    except KeyError:
+        price.rename(columns={"Date": "date"}, inplace=True)
+        price["date"] = pd.to_datetime(price["date"])
+
+    # 按月分組
+    df_test["month"] = df_test["report_date"].dt.to_period("M")
+    print(df_test.month.max())
+    # 初始化選中股票列表和累計收益
+    selected_stocks = pd.DataFrame()
+    total_return = 1
+    cumulative_return = [1]
+    stop_trading = False
+    consecutive_profit_months = 0
+    months = list(df_test.sort_values("month").month.unique())
+    stock_list = pd.DataFrame()
+    # 遍歷每個月
+
+    for month in df_test.sort_values("month").month.unique():
+        # 獲取當月的預測
+        current_month_predictions = df_test[(df_test["month"] == month)]
+
+        # 結合上個月選出的股票，同時檢查是否過期
+        combined = pd.concat([selected_stocks, current_month_predictions])
+        combined = combined[combined["report_date"] >= (month - 12).start_time]
+
+        # 選擇預測值最高的五檔股票
+        top5 = combined.nlargest(5, "pred_reg_12m")
+        selected_stocks = top5.drop_duplicates("symbol")
+        tmp = selected_stocks[["symbol", "pred_reg_12m"]]
+        tmp["month"] = month
+
+        stock_prices = price[price["symbol"].isin(selected_stocks.symbol)]
+        buy_price = (
+            stock_prices[stock_prices["date"].dt.to_period("M") == (month + 1)]
+            .groupby("symbol")
+            .agg({"Open": "first"})
+            .reset_index()
+        )
+        sell_price = (
+            stock_prices[stock_prices["date"].dt.to_period("M") == (month + 1)]
+            .groupby("symbol")
+            .agg({"Close": "last"})
+            .reset_index()
+        )
+        monthly_return = ((sell_price.Close - buy_price.Open) / buy_price.Open).mean()
+
+        # 檢查是否停止買賣
+        if stop_trading:
+            if monthly_return > 0:
+                consecutive_profit_months += 1
+                if consecutive_profit_months >= 1:
+                    stop_trading = False
+                    consecutive_profit_months = 0
+        else:
+            total_return = cumulative_return[-1] * (1 + monthly_return)
+            if monthly_return < -0.1:
+                stop_trading = True
+                consecutive_profit_months = 0
+
+        tmp["stop_trading"] = stop_trading
+        stock_list = stock_list.append(tmp, ignore_index=True)
+        cumulative_return.append(total_return)
+
+    # 打印累計收益
+    months.append((month + 1))
+    cumulative_return = pd.DataFrame(
+        dict(month=months, cumulative_return=cumulative_return)
+    )
+    cumulative_return["month"] = cumulative_return.month.astype(str)
+    # 打印累計收益
+    print("Total Return:", total_return)
+    return cumulative_return, stock_list
+
+
+def rolling_fit_predict(data, year, feature_cols):
+    df = data.copy()
+    df = df[~df.target_ml.isna()]
+    df_origin = data.copy()
+    for i in year:
+        print(i)
+        reg_12m = LinearRegression(positive=True).fit(
+            df.loc[df.year <= i, feature_cols], df.loc[df.year <= i, "target_ml"]
+        )
+        df_origin.loc[df_origin.year == i + 1, "pred_reg_12m"] = reg_12m.predict(
+            df_origin.loc[df_origin.year == i + 1, feature_cols]
+        )
+    return df_origin
+
+
+def line_broadcast_flex(data):
+    path = "line_config.json"
+    with open(path, "r", encoding="utf8") as f:
+        config = json.load(f)
+
+    url = "https://api.line.me/v2/bot/message/broadcast"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": "Bearer {}".format(config["token"]),
+    }
+    contents = []
+    if data[0]["stop_trading"]:
+
+        contents.append(
+            {
+                "type": "box",
+                "layout": "horizontal",
+                "contents": [
+                    {
+                        "type": "text",
+                        "text": "Stop Trading",
+                        "size": "sm",
+                        "color": "#555555",
+                        "flex": 0,
+                    }
+                ],
+            }
+        )
+    else:
+        for item in data:
+            contents.append(
+                {
+                    "type": "box",
+                    "layout": "horizontal",
+                    "contents": [
+                        {
+                            "type": "text",
+                            "text": item["symbol"],
+                            "size": "sm",
+                            "color": "#555555",
+                            "flex": 0,
+                        },
+                        {
+                            "type": "text",
+                            "text": "{:.4f}".format(item["pred_reg_12m"]),
+                            "size": "sm",
+                            "color": "#111111",
+                            "align": "end",
+                        },
+                    ],
+                }
+            )
+
+    flex_message = {
+        "type": "flex",
+        "altText": "Stock Information",
+        "contents": {
+            "type": "bubble",
+            "body": {
+                "type": "box",
+                "layout": "vertical",
+                "contents": [
+                    {
+                        "type": "text",
+                        "text": "STOCK INFO",
+                        "weight": "bold",
+                        "color": "#1DB446",
+                        "size": "sm",
+                    },
+                    {"type": "separator", "margin": "xxl"},
+                    {
+                        "type": "box",
+                        "layout": "vertical",
+                        "margin": "xxl",
+                        "spacing": "sm",
+                        "contents": contents,
+                    },
+                    {"type": "separator", "margin": "xxl"},
+                ],
+            },
+            "styles": {"footer": {"separator": True}},
+        },
+    }
+
+    payload = json.dumps({"messages": [flex_message]})
+    response = requests.request("POST", url, headers=headers, data=payload, timeout=10)
+    print(response.text)
+
+
+def main():
+    connect = sqlite3.connect("feature.sqlite")
+    feature = pd.read_sql("SELECT * FROM feature", con=connect)
+    connect.close()
+
+    connect = sqlite3.connect("target.sqlite")
+    target = pd.read_sql("SELECT * FROM target", con=connect)
+    connect.close()
+
+    connect = sqlite3.connect("price.sqlite")
+    price = pd.read_sql("SELECT * FROM price_table", con=connect)
+    connect.close()
+
+    year = int(datetime.now().strftime("%Y"))
+    df = feature.merge(
+        target[["symbol", "report_date", "target_ml", "end_date"]],
+        on=["symbol", "report_date"],
+        how="left",
+    )
+    model = pickle.load(open("model.pkl", "rb"))
+    df["report_date"] = pd.to_datetime(df.report_date)
+    df.loc[df.end_date.isna(), "end_date"] = df.loc[
+        df.end_date.isna(), "report_date"
+    ] + timedelta(days=365)
+    years = [str(year - 1), str(year)]
+    output = rolling_fit_predict(df, years, feature_cols=model["feature"])
+    _, stock_list = rolling_gptprod_profit(output[output.year.isin(years)], price)
+    line_broadcast_flex(stock_list.tail(5).to_dict("records"))
+
+
+if __name__ == "__main__":
+    main()