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
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
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
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
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]
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]
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
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
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
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
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
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
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
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
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
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
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
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
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
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