strategy.entry(id, direction, qty, limit, stop, oca_name, oca_type, comment, alert_message, disable_alert)

import math
import glob
import pandas as pd
from pathlib import Path

if __name__ == '__main__':
    # ***************************************************
    # パラメータ設定
    # ***************************************************
    short_periods = 20
    Lot_unit = 10000

    # パスを指定
    path_charts_list = r'C:/'
    files_fullpath = glob.glob(path_charts_list + 'FX_*.csv')
    output_path = r'C:/'
    outfile = r'a.csv'
 
    # 日足ファイルを辞書型(DataFrame)に読み込み
    data_dic = {}  # 空の辞書型を作成

    # 日足ファイル中のデータを読み込み
    for fullpath in files_fullpath:
        fname = Path(fullpath).stem # 日足ファイル名を作成(keyのため)
        df = pd.read_csv(fullpath, encoding="shift-jis")
        df.drop(columns=['Volume MA'], inplace=True) # Volume MAを削除
        df['datetime'] = pd.to_datetime(df.time)  # 日付の列を追加
        df.drop(columns=['time'], inplace=True) # time列を削除

        df["ave_short"] = df["close"].rolling(short_periods, min_periods=short_periods).mean()
        df["stddev"] = df["close"].rolling(short_periods, min_periods=short_periods).std(ddof=0)

        # 辞書型へ追加
        data_dic[fname] = df
 
    # ***************************************************
    # トレード計算
    # ***************************************************
    order_dic = {}  # 空の辞書型を作成
    colums = ["order_type", "open_date", "close_date", "open_price", "close_price", "lots", "profit_loss"]

    for stock in data_dic.keys():
        order_dic[stock] = pd.DataFrame()
        df = data_dic[stock]  # 個別銘柄の価格情報 'open','high','low','close','Volume', 'datetime','ave_short', 'stddev'
        pos = 0
        open_price = 0.0
        close_price = 0.0
        buysell = 1
        profit_loss = 0
        lots = 0
        n = 0
        month = 0

        for row in df.itertuples():
            # 月が変わったことを調べる
            if row.datetime.month != month:
                month = row.datetime.month
                # もし注文があれば、最初にクローズする。orderclose
                if pos != 0:
                    close_price = row.open
                    close_date = row.datetime
                    profit_loss = buysell * math.log10(close_price/open_price)  # 損益を計算, 対数変化率
                    pos = pos - lots

                    s = pd.DataFrame([[buysell, open_date, close_date, open_price, close_price, lots, profit_loss]], columns=colums, index=[n])
                    order_dic[stock] = pd.concat([order_dic[stock], s])
                    n = n + 1
 
                # 注文がなければ、始値で購入する ordersend
                if pos == 0:
                    open_price = row.open #建玉価格
                    open_date = row.datetime #購入時刻
                    lots = Lot_unit #ロット
                    buysell = 1 # 1:buy, -1:sell
                    pos = pos + lots

    # ***************************************************
    # 後処理
    # ***************************************************
    # 毎年の平均利益を求める
    BaH = "BaH(対数変化率)"
    BaH_ratio = "BaH(変化率%)"
    num_trade = "num_trade"
    columns = [BaH, num_trade]
    yearly_profit_loss = pd.DataFrame(index=range(1970,2024), columns=columns)
    yearly_profit_loss.fillna(0.0, inplace=True)

    for stock in order_dic.keys():#銘柄ループ
        for row in order_dic[stock].itertuples():#行ごと(トレード毎)に見ていく
            year = int(row.close_date.year)
            yearly_profit_loss.at[year, BaH] = yearly_profit_loss.at[year, BaH] + row.profit_loss
            yearly_profit_loss.at[year, num_trade] = yearly_profit_loss.at[year, num_trade] + 1

    yearly_profit_loss[BaH] = yearly_profit_loss[BaH]/yearly_profit_loss[num_trade]
    yearly_profit_loss.fillna(0,inplace=True) #NAを0埋め
    yearly_profit_loss[BaH_ratio] = (10**yearly_profit_loss[BaH] - 1.0)*100

    # ***************************************************
    # 計算結果をcsvにして保存
    # ***************************************************
    yearly_profit_loss.to_csv(output_path+outfile)