table=api.USData.get('美股產業對照表')

#從每個粗產業中 ,挑出一檔市值最大的"非etf"
ls =[]
for group in table.粗產業名稱.unique():
    if group=='':
        continue
    symbol = table.loc[table[(table["粗產業名稱"] == group) & (table["是否為ETF"] == "0")]["市值(美元)"].idxmax()].股票代號
    ls.append(symbol)
    
dic ={}
for symbol in ls:
    table = api.USData.get('日K價量資料(個股、ETF)-單檔股票多個區間',symbol)
    df = table.set_index('日期')[['開盤價','收盤價','成交量','最低價']]
    df.columns=['Open','Close','Volume','Low']

    #量創10日均量的兩倍
    con1 = df['Volume'] > df['Volume'].shift(1).rolling(10).mean()*2
    #收盤價 > 最低價*1.03
    con2 = df['Close'] > df['Low']*1.03

    pos = con1 &con2
    df.loc[pos[pos==True].index,'sig']=True
    #每檔5000美元
    df['qty'] = np.around(5000/df['Open'])

    #存入dic
    dic[symbol] = df.reset_index()

#sig_value 用來計算止盈止損的對標欄位
#open_value 進場價格欄位
#close_value 出場價格欄位
benchmark =api.USData.get('日K價量資料(個股、ETF)-單檔股票多個區間','SPY').set_index('日期')['收盤價']
rp = api.backtest(  data=dic ,
                    date='日期',
                    sig_value='Close',
                    open_value='Open',  open_shift=True,   
                    close_value='Open', close_shift=True,  
                    longsig='sig',qty='qty',
                    moveloss=20,
                    fee=0.003/2 ,tax=0.0000051,
                    benchmark=benchmark)

table=api.USData.get('美股產業對照表')

#市值前30大的股票
ls =table[(table['是否為ETF']=='0')&(table['市值(美元)'].notnull())&(table.流通股數.notnull())].sort_values('市值(美元)',ascending=False)[:30].股票代號.tolist()

#計算macd
def ind_MACD(data, short_window=12 ,long_window = 26 ,macd_window = 9):
    short_ema = data.ewm(span=short_window, adjust=False).mean()
    short_ema[:short_window-1] = np.nan  # 前 11 筆設為 NaN
    long_ema = data.ewm(span=long_window, adjust=False).mean()
    long_ema[:long_window-1] = np.nan    # 前 25 筆設為 NaN
    macd = short_ema -long_ema
    macd_ema = macd.ewm(span=macd_window, adjust=False).mean()
    macdhist = macd - macd_ema
    return macd ,macd_ema  ,macdhist

dic ={}
for symbol in ls:
    table = api.USData.get('日K價量資料(個股、ETF)-單檔股票多個區間',symbol)
    df = table.set_index('日期')[['開盤價','收盤價','成交量','最低價']]
    df.columns=['Open','Close','Volume','Low']

    macd ,macd_ema  ,macdhist = ind_MACD(df['Close'], short_window=12 ,long_window = 26 ,macd_window = 9)

    #低點3bar内的最低macdhist
    low_point = macdhist.rolling(3).min()
    #低點是60内的最低點
    con1 = low_point ==low_point.rolling(60).min()
    #連續兩期上升
    con2 = (macdhist>macdhist.shift(1)) &(macdhist.shift(1)>macdhist.shift(2)) 
    #hist<0
    con3 = macdhist<0
    #進場
    bk = con1&con2&con3
    
    con4 = (macdhist<macdhist.shift(1))& (macdhist.shift(1)<macdhist.shift(2))
    #hist>0
    con5 = macdhist>1.5
    #出場
    bp = con4&con5 

    #套用到sig
    df.loc[bk[bk==True].index,'sig']=True
    df.loc[bp[bp==True].index,'sig']=False
    
    #每檔5000美元
    df['qty'] = np.around(5000/df['Open'])
    
    #存入dic
    dic[symbol] = df.reset_index()
    
benchmark =api.USData.get('日K價量資料(個股、ETF)-單檔股票多個區間','SPY').set_index('日期')['收盤價']
rp = api.backtest(  data=dic ,
                    date='日期',
                    sig_value='Close',
                    open_value='Open',  open_shift=True,   
                    close_value='Open', close_shift=True,  
                    longsig='sig',qty='qty',
                    fee=0.003/2 ,tax=0.0000051,
                    benchmark=benchmark)