def tasa_implicita(df_futuro, df_spot, fecha_vto):
"""
Determinación de la tasa implícita al día del vencimiento
del futuro, considerando el precio del spot
inputs:
Cotización del Futuro (pandas series)
Precio del Spot (pandas series)
Fecha del día (datetime)
Fecha del Vto (datetime)
"""
ohlc_stock = getOHLC(df_futuro)
ohlc_spot = getOHLC(df_spot)
ohlc_stock.rename(columns={'close': 'close_LA'}, inplace=True)
ohlc_spot.rename(columns={'close': 'close_IV'}, inplace=True)
ohlc_stock.drop(['open', 'high', 'low'], axis=1, inplace=True)
ohlc_spot.drop(['open', 'high', 'low'], axis=1, inplace=True)
df = pd.concat([ohlc_stock, ohlc_spot], axis=1, join='inner')
# df.dropna(inplace=True, axis=0)
df['base'] = df.close_LA - df.close_IV
df['dias_vto'] = (fecha_vto - df.index).days + 1
df['tasa_implicita'] = df.base / df.close_IV * 365 / df.dias_vto
df.dropna(inplace=True)
# print(df)
df = df[['tasa_implicita', 'close_LA']]
df.rename(columns={'close_LA': 'close'}, inplace=True)
return df
def mean_reversion(ticker, period='1min', window=5, start_date=''):
"""
Mean reversion Strategy.
Basically follows the moving average, and the standard deviatino of the sotck price, and look if is moment of buy or sell.
More info in https://medium.com/auquan/mean-reversion-simple-trading-strategies-part-1-a18a87c1196a
"""
df = utils.DBtools.read_ticker(ticker, start_date=start_date)
df = df.drop_duplicates('LA_date')
ohlc = getOHLC(df, 'LA_price', 'LA_size', period)
ohlc.dropna(inplace=True)
ohlc['mean'] = ohlc.close.rolling(window=window).mean()
ohlc['std'] = ohlc.close.rolling(window=window).std()
ohlc['zscore'] = (ohlc['close'] - ohlc['mean']) / ohlc['std']
# Sell short if the z-score is > 1
ohlc.loc[(ohlc.zscore > 1), 'estrategia'] = -1
# Buy long if the z-score is < -1
ohlc.loc[(ohlc.zscore < -1), 'estrategia'] = 1
# Clear positions if the z-score between -.5 and .5
ohlc.loc[(ohlc.zscore < 0.5) & (ohlc.zscore > -0.5), 'estrategia'] = 0
ohlc['estrategia'].fillna(method='ffill', inplace=True)
ohlc['returns'] = ohlc.close.diff() * ohlc.estrategia
ohlc['Cumulative'] = ohlc.returns.cumsum()
return ohlc
def cruce_medias(ticker, period='1min', long_window=10 * 3, short_window=5):
"""
Estrategia utilizando el cruce de dos medias. Para poder calcular las medias,
primero debo generar el OHLC en el periodo especificado. A su vez, se caluclan
las dos medias moviles simples para la ventana long y short. En funcion del cruce
se determina si comprar o vender
"""
start = datetime.now()
df = utils.DBtools.read_ticker(ticker)
print('Just read the database takes:',
(datetime.now() - start).total_seconds())
## df.index = pd.to_datetime(df.index)
df = df[df['Ticker'] == ticker].drop_duplicates('LA_date')
print('Read the info from database and make a DataFrame takes:,' (
datetime.now() - start).total_seconds())
FS = 1.0 #Factor de seguridad = 2%?
start = datetime.now()
ohlc = getOHLC(df, period=period)
ohlc.fillna(method='ffill', inplace=True)
ohlc = ohlc.between_time('10:00', '17:00')
print('get the OHLC takes:,' (datetime.now() - start).total_seconds())
start = datetime.now()
estrategia = ohlc.copy()
estrategia['SMA_short'] = ohlc.close.rolling(window=short_window).mean()
estrategia['SMA_long'] = ohlc.close.rolling(window=long_window).mean()
estrategia.loc[estrategia['SMA_short'] > estrategia['SMA_long'] * FS,
'estrategia'] = 1
estrategia.loc[estrategia['SMA_short'] < estrategia['SMA_long'] / FS,
'estrategia'] = -1
estrategia.estrategia.fillna(method='ffill', inplace=True)
estrategia = estrategia.loc[
estrategia.index > datetime.strptime('19-02-2019', '%d-%m-%Y')]
position = estrategia.estrategia.iloc[-1]
print('Calculate the strategy takes:,' (datetime.now() -
start).total_seconds())
if position == 1:
price = df['OF_price'].iloc[-1]
side = 'BUY'
elif position == -1:
price = df['BI_price'].iloc[-1]
side = 'SELL'
else:
price = 0
side = ''
return position, price, side, estrategia
def PP(df, ticker):
"""
Determinación de Soportes y resistencais en función de precio de apertura y cierre. Es lo denominado, Puntos de Pivot en la literatura.
R1 = (P x 2) – L
S1 = (P x 2) – H
R2 = P + (H - L) = P + (R1 - S1)
S2 = P - (H - L) = P - (R1 - S1)
donde:
P: Nivel de Pivot
L: Anterior Low
H: Anterior High
R1: Nivel de resistencia 1
S1: Nivel de soporte 1
R2: Nivel de resistencia 2
S2: Nivel de soporte 2
"""
df = df[df['Ticker'] == ticker].drop_duplicates('LA_date')
ohlc = getOHLC(df, 'LA_price', 'LA_size')
ohlc.dropna(inplace=True)
pp = indicadores.puntos_pivot(ohlc)
r1 = pp * 2 - ohlc.low.shift(1)
s1 = pp * 2 - ohlc.high.shift(1)
r2 = pp + r1 - s1
s2 = pp - r1 + s1
indicador = pd.concat([
pp.rename('pp'),
r1.rename('r1'),
s1.rename('s1'),
r2.rename('r2'),
s2.rename('s2')
],
sort=False,
axis=1)
end = datetime.now()
# print(df.tail())
# print(indicador.tail())
# print('Perform the strategy takes ', end-start, 'seconds')
return indicador
def stochastic(ticker, period='2min'):
"""
Compra y venta en funcion del valor del estocastico. Si cruza hacia abajo de 80, es venta, si cruza hacia arriba de 20, es compra.
"""
df = utils.DBtools.read_ticker(ticker)
df = df[df['Ticker'] == ticker].drop_duplicates('LA_date')
ohlc = getOHLC(df, 'LA_price', 'LA_size', period)
# ohlc.dropna(inplace=True)
ohlc.drop(['open', 'high', 'low', 'volumen'], axis=1, inplace=True)
ohlc.fillna(method='ffill', inplace=True)
stoch = indicadores.stochastic(ohlc, 'close')
ohlc = pd.concat([ohlc, stoch], axis=1, join_axes=[ohlc.index])
ohlc.loc[(stoch.d < 20) & (stoch.d.shift(-1) > 20), 'estrategia'] = 1
ohlc.loc[(stoch.d > 80) & (stoch.d.shift(-1) < 80), 'estrategia'] = -1
ohlc.fillna(method='ffill', inplace=True)
ohlc.insert(len(ohlc.columns), 'cum returns',
ohlc.close.diff() * ohlc.estrategia)
return ohlc
def FollowTheVolume(ticker,
start_date=datetime.today().strftime('%Y-%m-%d'),
period='1min',
no_operation_roc=0.005):
start = datetime.now()
df = utils.DBtools.read_ticker(ticker, start_date=start_date)
df = df.drop_duplicates('LA_date')
# print("get the df from the DB takes ", (datetime.now()-start).total_seconds(), "seconds")
start = datetime.now()
try:
ohlc = getOHLC(df, 'LA_price', 'LA_size', period)
ohlc.dropna(inplace=True)
# print("get the OHLC takes ", (datetime.now()-start).total_seconds(), "seconds")
start = datetime.now()
ohlc = indicadores.vwap(ohlc,
column_price_name='close',
column_volume_name='volume')
# print("get the VWAP takes ", (datetime.now()-start).total_seconds(), "seconds")
start = datetime.now()
ohlc = indicadores.ROC(ohlc, column_price_name='VWAP')
# print("get the ROC takes ", (datetime.now()-start).total_seconds(), "seconds")
start = datetime.now()
ohlc.loc[(ohlc.ROC < 0), 'estrategia'] = -1
ohlc.loc[(ohlc.ROC > 0), 'estrategia'] = 1
# ohlc.loc[(ohlc.ROC > -no_operation_roc) & (ohlc.ROC < no_operation_roc), 'estrategia'] = 0
ohlc['estrategia'].fillna(method='ffill', inplace=True)
# print("get the Strategy takes ", (datetime.now()-start).total_seconds(), "seconds")
start = datetime.now()
ohlc['returns'] = ohlc.close.diff() * ohlc.estrategia
ohlc['Cumulative'] = ohlc.returns.cumsum()
except:
ohlc = pd.DataFrame()
return ohlc
ohlc.dropna(inplace=True)
pp = indicadores.puntos_pivot(ohlc)
r1 = pp * 2 - ohlc.low.shift(1)
s1 = pp * 2 - ohlc.high.shift(1)
r2 = pp + r1 - s1
s2 = pp - r1 + s1
indicador = pd.concat([
pp.rename('pp'),
r1.rename('r1'),
s1.rename('s1'),
r2.rename('r2'),
s2.rename('s2')
],
sort=False,
axis=1)
end = datetime.now()
# print(df.tail())
# print(indicador.tail())
# print('Perform the strategy takes ', end-start, 'seconds')
return indicador
if __name__ == "__main__":
from finance_tools import getOHLC
from CSVtools import read_csvData
df = pd.DataFrame()
df = read_csvData('TestData.csv')
ohlc = getOHLC(df)
stocastico(ohlc)