Kostenlose Historische EOD Daten

EOD data von ALV (Dax30)
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dieses Beispiel als scriplet erzeugt aus Datenreihen buy/sell Signale
mit grafischer Darstellung.
Lauffähiges file für Python IDLE oder Notebook jupyter im Anhang:
Charts ebenso
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Hat jemand im Forum py script für Optmierungsläufe der Parameter ?
feedback jeder Art wäre hilfreich 
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py_macd_buy_sell_signal.txt   -->  *.txt  in  *.py umbenennen
(file ist virenfrei)
 
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Besprechung zur Generierung von crossover Signalen 
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für Datenreihen aus: --High, Low, Close, Adj Close, Volume
für Indikator-Reihen z.B. : -- macd, sma, ema, etc.....
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scriplet:
erzeugen eines macd buy/sell signals :
###-------------------------------------------------------
sma12 = df['Adj Close'].rolling(12).mean()
sma26 = df['Adj Close'].rolling(26).mean()
MACD = sma12 -sma26
signal = MACD.ewm(span=9, adjust=False).mean()
###-------------------------------------------------------
# create new columns
df['MACD'] = MACD
df['Signal Line'] = signal
#-----------------------------
### generate buy sell signal from macd crossover
def buy_sell(signal):
    Buy=[]
    Sell=[]
    flag=-1
    for i in range(0,len(signal)):
        if signal['MACD'][i] > signal['Signal Line'][i]:
            Sell.append(np.nan)
            if flag != 1:
                Buy.append(signal['Close'][i])
                flag = 1
            else:
                Buy.append(np.nan)
        #------------------------------------------------
        elif signal['MACD'][i] < signal['Signal Line'][i]:
            Buy.append(np.nan)
            if flag != 0:
                Sell.append(signal['Close'][i])
                flag = 0
            else:
                Sell.append(np.nan)
        else:
            Buy.append(np.nan)
            Sell.append(np.nan)
    return(Buy, Sell)
#-------------------------------------------------
#  store macd buy and sell signal
a = buy_sell(df)
df['Buy_Signal']= a[0]
df['Sell_Signal']= a[1]
#-------------------------------------------------
# visually show stock buy sell data
plt.figure(figsize=(10,4.5))
plt.scatter(df.index,df['Buy_Signal'], color='green',label='macd Buy', marker='^',alpha=1)
plt.scatter(df.index,df['Sell_Signal'], color='red',label='macd Sell', marker='v',alpha=1)
plt.plot(df['MACD'], label='SMA26', alpha = 0.9)
plt.plot(df['Signal Line'], label='Close Price', alpha = 0.9)
plt.plot(df['Close'], label='Close Price', alpha = 0.9)
titel = symb1 + '  Price ' + '( Dax30 )' + '  \n' + 'macd buy/sell signal on close '
plt.title(titel)
plt.xlabel('Date as index -> 2.1.20 - 28.8.20')
plt.ylabel('Close Price')
plt.legend(loc='upper left')
plt.show()
#-------------------------------------------------