STREAMLIT_gridTrading_App.py

# streamlit run app.py
# http://localhost:8501/

# STREAMLIT套件
import streamlit as st
import pandas as pd
# 可以互動的PLOT套件
import plotly.graph_objects as go
# 設置日期格式的套件
import datetime
from datetime import datetime as dt
from datetime import timedelta 
import tejapi

# 登入TEJ API
tejapi.ApiConfig.api_key = 'your_key'
#把時間取消保留日期 (無視)
tejapi.ApiConfig.ignoretz = True

st.set_page_config(page_title='量化投資', page_icon=':bar_chart:', layout='wide')

with st.sidebar:
    
    st.title('TEJAPI股票學習')
    col1, col2 = st.columns(2)
    with col1:
        # 將股票起使日期設置為變數d1
        d1 = st.date_input(
        "股票起始日期",
        # 並將預設資料設定為2022年的1/1
        datetime.date(2022, 1, 1))
        
        

    with col2:
        # 將股票起使日期設置為變數d2
        d2= st.date_input(
        "股票結束日期",
        datetime.date(2023, 2, 3))
        
    
    #輸入股價
    # 使用date套件的date獲取今天的日期資料
    current_date = dt.now().date()
    # 使用date套件的timedelta獲取昨天的日期資料
    previous_date = current_date - timedelta(days=1)
    data = tejapi.get('TWN/APIPRCD',
                  mdate=previous_date,
                  opts={'columns':['coid']},
                  paginate=True)
    
    coids = data['coid'].tolist()
    stock_code = st.selectbox('選擇股票代碼', data)
    st.write('你選擇股票是: ', stock_code)


    upper_bound = st.number_input('上界值:',value=150)
    lower_bound = st.number_input('下界值:',value=100)
    interval = st.number_input('網格區間:',value=10)

    stock_id = {stock_code}
    gte, lte = {d1}, {d2}
    tejdata= tejapi.get('TWN/APIPRCD',
                        paginate = True,
                        coid = stock_id,
                        mdate = {'gte':gte, 'lte':lte},
                        chinese_column_name=True
                        )
    df = tejdata
    df.reset_index(drop=True, inplace=True)
    
    # 創建 Buy_Signal 和 Sell_Signal 列，預設為 False
    df['Buy_Signal'] = False
    df['Sell_Signal'] = False

    # 在適當的條件下，將 Buy_Signal 和 Sell_Signal 設置為 True
    grid = list(range(lower_bound, upper_bound + interval, interval))
    for i in range(1, len(df)):
        for price in grid:
            if df['收盤價'][i-1] > price >= df['收盤價'][i]:
                df.at[i, 'Buy_Signal'] = True
            if df['收盤價'][i-1] < price <= df['收盤價'][i]:
                df.at[i, 'Sell_Signal'] = True
    

    
   
st.title('🌐STREAMLIT股票資料網格交易應用')
st.write("")

tab1, tab2, tab3 = st.tabs(["交易訊號", "資料集", "投資績效表"])

with tab1:
    st.title(stock_code)
    fig = go.Figure()
    fig.add_trace(go.Scatter(x=df['資料日'], y=df['收盤價'], name=stock_code))

    # 創建網格，將線條顏色設置為灰色
    for price in grid:
        fig.add_shape(type="line", x0=df['資料日'].iloc[0], x1=df['資料日'].iloc[-1], y0=price, y1=price, line=dict(color='gray'))

    # 加入買進訊號的散點圖
    buy_signals = df[df['Buy_Signal']]
    fig.add_trace(go.Scatter(x=buy_signals['資料日'], y=buy_signals['收盤價'], mode='markers', name='Buy Signal', marker=dict(color='green', size=10, symbol='triangle-up')))

    # 加入賣出訊號的散點圖
    sell_signals = df[df['Sell_Signal']]
    fig.add_trace(go.Scatter(x=sell_signals['資料日'], y=sell_signals['收盤價'], mode='markers', name='Sell Signal', marker=dict(color='red', size=10, symbol='triangle-down')))

    fig.update_layout()

    st.plotly_chart(fig)

with tab2:
    st.dataframe(df, height=500)  
    @st.cache_data
    def convert_df(df):
        # IMPORTANT: Cache the conversion to prevent computation on every rerun
        return df.to_csv().encode("utf-8")
    csv = convert_df(df)

    st.download_button(
        label="點此下載資料範例",
        data=csv,
        file_name=f"{stock_code}股價資料.csv",
        mime="text/csv",
    )

with tab3:
    st.title('交易模擬')
    
    # 初始化本金和交易紀錄表
    principal = 500000
    cash = principal
    position = 0
    order_unit = 1000  # 每次交易的股數
    trade_book = pd.DataFrame(columns=['Coid', 'BuyOrSell', 'BuyTime', 'SellTime', 'CashFlow', 'TradeUnit', 'HoldingPosition', 'CashValue'])

    st.write("本金：", principal)

    # 進行網格交易並更新交易紀錄表
    for i in range(1, len(df)):
        cu_time = df['資料日'][i]
        cu_close = df['收盤價'][i]
        n_time = df['資料日'][i - 1]
        n_open = df['開盤價'][i]

        if position == 0 and df['Buy_Signal'][i]:
            # 判斷是否進行買進
            position += 1
            order_time = n_time
            order_price = n_open
            friction_cost = (20 if order_price * order_unit * 0.001425 < 20 else order_price * order_unit * 0.001425)
            total_cost = -1 * order_price * order_unit - friction_cost
            cash += total_cost
            trade_book = trade_book.append({'Coid': stock_id, 'BuyOrSell': 'Buy', 'BuyTime': order_time, 'SellTime': None, 'CashFlow': total_cost, 'TradeUnit': order_unit, 'HoldingPosition': position, 'CashValue': cash}, ignore_index=True)

        elif position > 0 and df['Sell_Signal'][i]:
            # 判斷是否進行賣出
            order_price = n_open
            cover_time = n_time
            friction_cost = (20 if order_price * order_unit * 0.001425 < 20 else order_price * order_unit * 0.001425) + order_price * order_unit * 0.003
            total_cost = order_price * order_unit - friction_cost
            cash += total_cost
            trade_book = trade_book.append({'Coid': stock_id, 'BuyOrSell': 'Sell', 'BuyTime': None, 'SellTime': cover_time, 'CashFlow': total_cost, 'TradeUnit': -1 * order_unit, 'HoldingPosition': position, 'CashValue': cash}, ignore_index=True)
            position = 0

    st.write("交易紀錄表：")
    st.dataframe(trade_book)

    # 計算交易相關數據
    overallreturn = ((cash - principal) / principal) * 100
    num_trade = len(trade_book)
    num_buy = len(trade_book[trade_book['BuyOrSell'] == 'Buy'])
    num_sell = len(trade_book[trade_book['BuyOrSell'] == 'Sell'])

    # 計算平均交易報酬
    avg_return_ = (trade_book['CashFlow'] / (trade_book['TradeUnit'] * trade_book['CashValue'])).mean() * 100

    # 計算平均持有期間
    trade_book['BuyTime'] = pd.to_datetime(trade_book['BuyTime'])  # 將BuyTime轉換為datetime格式
    trade_book['SellTime'] = pd.to_datetime(trade_book['SellTime'])  # 將SellTime轉換為datetime格式
    # 計算持有期間並排除掉NaN和負值
    trade_book['HoldPeriod'] = (trade_book['SellTime'] - trade_book['BuyTime']).dt.days
    trade_book['HoldPeriod'] = trade_book['HoldPeriod'].apply(lambda x: max(x, 0))  # 將持有期間中的負值改為0
    avg_hold_period_ = trade_book['HoldPeriod'].mean()

    # 計算勝率
    winning_rate = (len(trade_book[trade_book['CashFlow'] > 0]) / num_trade) * 100

    # 計算最大獲利交易報酬和最大損失交易報酬
    trade_book['ReturnPercentage'] = (trade_book['CashFlow'] / (trade_book['TradeUnit'] * trade_book['CashValue'])) * 100
    max_win = trade_book['ReturnPercentage'].max()
    max_loss = trade_book['ReturnPercentage'].min()

    # 計算最低現金持有量
    min_cash = trade_book['CashValue'].min()

    # 呈現交易相關數據
    st.write('總績效:', overallreturn, '%')
    st.write('交易次數:', num_trade, '次')
    st.write('買入次數:', num_buy, '次')
    st.write('賣出次數:', num_sell, '次')
    st.write('平均交易報酬:', avg_return_, '%')
    st.write('勝率:', winning_rate, '%')
    st.write('最大獲利交易報酬:', max_win, '%')
    st.write('最大損失交易報酬:', max_loss, '%')
    st.write('最低現金持有量:', min_cash)