Example #1
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def RSI(ticker,date, periods):
    ''' calculate RSI
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data=dt.get_stock_data(ticker,date,periods+1)
    close_data=stock_data['Close'].values

    close_data_new=close_data[1:periods+1]
    close_data_old=close_data[0:periods]

    difference=close_data_new-close_data_old

    gain=difference[difference>0].sum()/periods
    loss=-difference[difference<0].sum()/periods

    relative_strength=gain/loss

    retVal=100-(100/(1+relative_strength))

    return retVal
Example #2
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def RSI(ticker, date, periods):
    ''' calculate RSI
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data = dt.get_stock_data(ticker, date, periods + 1)
    close_data = stock_data['Close'].values

    close_data_new = close_data[1:periods + 1]
    close_data_old = close_data[0:periods]

    difference = close_data_new - close_data_old

    gain = difference[difference > 0].sum() / periods
    loss = -difference[difference < 0].sum() / periods

    relative_strength = gain / loss

    retVal = 100 - (100 / (1 + relative_strength))

    return retVal
Example #3
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def ATR(ticker, date, periods):
    ''' calculate ATR
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data = dt.get_stock_data(ticker, date, periods + 1)

    H = stock_data['High'].values[1:periods + 1]
    L = stock_data['Low'].values[1:periods + 1]
    PC = stock_data['Close'].values[0:periods]

    matr = pd.DataFrame()

    matr['HL'] = H - L
    matr['H_PC'] = abs(H - PC)
    matr['L_PC'] = abs(L - PC)

    matr['max'] = matr[['HL', 'H_PC', 'L_PC']].max(axis=1)

    retVal = matr['max'].mean()

    return retVal
Example #4
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def ATR(ticker,date,periods):
    ''' calculate ATR
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data=dt.get_stock_data(ticker,date,periods+1)

    H=stock_data['High'].values[1:periods+1]
    L=stock_data['Low'].values[1:periods+1]
    PC=stock_data['Close'].values[0:periods]

    matr=pd.DataFrame()

    matr['HL']=H-L
    matr['H_PC']=abs(H-PC)
    matr['L_PC']=abs(L-PC)

    matr['max']=matr[['HL','H_PC','L_PC']].max(axis=1)

    retVal=matr['max'].mean()

    return retVal
Example #5
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def get_price(ticker, date, price_type):
    ''' get close price of a date
    string: ticker
    Datetime: date
    string: price_type {"Buy","Sell","High","Low","Volume"}
    
    return double
    '''
    stock_data = dt.get_stock_data(ticker, date, 1)

    return stock_data[price_type].values[0]
Example #6
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def get_price(ticker,date,price_type):
    ''' get close price of a date
    string: ticker
    Datetime: date
    string: price_type {"Buy","Sell","High","Low","Volume"}
    
    return double
    '''
    stock_data=dt.get_stock_data(ticker,date,1)

    return stock_data[price_type].values[0]
Example #7
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def typical_price(ticker,date):
    ''' return typical price on a date
    string: ticker
    Datetime: date

    return double
    '''
    stock_data=dt.get_stock_data(ticker,date,1)

    retVal=(stock_data["High"].values[0]+stock_data["Low"].values[0]+stock_data["Close"].values[0])/3

    return retVal
Example #8
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def typical_price(ticker, date):
    ''' return typical price on a date
    string: ticker
    Datetime: date

    return double
    '''
    stock_data = dt.get_stock_data(ticker, date, 1)

    retVal = (stock_data["High"].values[0] + stock_data["Low"].values[0] +
              stock_data["Close"].values[0]) / 3

    return retVal
Example #9
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def SMA(ticker, date, periods):
    ''' calculate simple moving average in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data = dt.get_stock_data(ticker, date, periods)

    retVal = stock_data['Close'].mean()

    return retVal
Example #10
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def standard_deviation(ticker,date,periods):
    ''' calculate standard deviation in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data=dt.get_stock_data(ticker,date,periods)

    retVal=stock_data['Close'].std()

    return retVal
Example #11
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def standard_deviation(ticker, date, periods):
    ''' calculate standard deviation in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data = dt.get_stock_data(ticker, date, periods)

    retVal = stock_data['Close'].std()

    return retVal
Example #12
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def SMA(ticker, date, periods):
    ''' calculate simple moving average in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data=dt.get_stock_data(ticker,date,periods)

    retVal=stock_data['Close'].mean()

    return retVal
Example #13
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def willamsR(ticker, date, periods):
    ''' calculate williamsR
    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:williams_r
    string: ticker
    Datetime: date
    int: periods

    return double
    '''
    stock_data=dt.get_stock_data(ticker,date,periods)

    highest_high=stock_data['High'].max()
    lowest_low=stock_data['Low'].min()
    today_close=stock_data['Close'][-1]

    R=(highest_high-today_close)/(highest_high-lowest_low)*(-100)
    
    return R
Example #14
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def willamsR(ticker, date, periods):
    ''' calculate williamsR
    http://stockcharts.com/school/doku.php?id=chart_school:technical_indicators:williams_r
    string: ticker
    Datetime: date
    int: periods

    return double
    '''
    stock_data = dt.get_stock_data(ticker, date, periods)

    highest_high = stock_data['High'].max()
    lowest_low = stock_data['Low'].min()
    today_close = stock_data['Close'][-1]

    R = (highest_high - today_close) / (highest_high - lowest_low) * (-100)

    return R
Example #15
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def volatility(ticker, date, periods):
    ''' calculate volatility in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data=dt.get_stock_data(ticker,date,periods+1)
    close_data=stock_data['Close'].values

    close_data_new=close_data[1:periods+1]
    close_data_old=close_data[0:periods]

    stock_returns=np.divide(close_data_new,close_data_old)-1.0

    retVal=np.std(stock_returns)* periods **(0.5)

    return retVal
Example #16
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def true_range(ticker,date):
    ''' return true range on a date
    string: ticker
    Datetime: date

    return double
    '''
    stock_data=dt.get_stock_data(ticker,date,2)

    H=stock_data["High"].values[1]
    L=stock_data["Low"].values[1]
    PC=stock_data["Close"].values[0]

    HL=H-L
    HPC=abs(H-PC)
    LPC=abs(L-PC)

    retVal=max(HL,HPC,LPC)

    return retVal
Example #17
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def volatility(ticker, date, periods):
    ''' calculate volatility in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    stock_data = dt.get_stock_data(ticker, date, periods + 1)
    close_data = stock_data['Close'].values

    close_data_new = close_data[1:periods + 1]
    close_data_old = close_data[0:periods]

    stock_returns = np.divide(close_data_new, close_data_old) - 1.0

    retVal = np.std(stock_returns) * periods**(0.5)

    return retVal
Example #18
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def true_range(ticker, date):
    ''' return true range on a date
    string: ticker
    Datetime: date

    return double
    '''
    stock_data = dt.get_stock_data(ticker, date, 2)

    H = stock_data["High"].values[1]
    L = stock_data["Low"].values[1]
    PC = stock_data["Close"].values[0]

    HL = H - L
    HPC = abs(H - PC)
    LPC = abs(L - PC)

    retVal = max(HL, HPC, LPC)

    return retVal
Example #19
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def EMA(ticker, date, periods):
    ''' calculate exponential moving average in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    new_periods=periods*5
    stock_data=dt.get_stock_data(ticker,date,new_periods)

    close_array=stock_data['Close'].values
    alpha=2.0/(periods+1)

    weight=np.logspace(new_periods-1,0,new_periods,base=(1-alpha))*alpha

    weight[0]=weight[0]/alpha

    retVal=np.multiply(close_array,weight).sum()

    return retVal
Example #20
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def EMA(ticker, date, periods):
    ''' calculate exponential moving average in close price
    string: ticker
    Datetime: date
    int: periods

    return double
    '''

    new_periods = periods * 5
    stock_data = dt.get_stock_data(ticker, date, new_periods)

    close_array = stock_data['Close'].values
    alpha = 2.0 / (periods + 1)

    weight = np.logspace(new_periods - 1, 0, new_periods,
                         base=(1 - alpha)) * alpha

    weight[0] = weight[0] / alpha

    retVal = np.multiply(close_array, weight).sum()

    return retVal