def w_r():
wr = ta.wr(high, low, close, lbp=14, fillna=False)
WR = wr[-1]
if 0 >= WR >= 20:
status7 = "Sell"
elif -80 <= WR:
status7 = "Buy"
else:
status7 = "Hold"
return status7
dd['target_3']=dd['Adj Close'].shift(-3)/dd['Adj Close']-1 dd['target_7']=dd['Adj Close'].shift(-7)/dd['Adj Close']-1 dd['target_14']=dd['Adj Close'].shift(-14)/dd['Adj Close']-1 dd['target_30']=dd['Adj Close'].shift(-30)/dd['Adj Close']-1 dd['s00_return_1']=dd.Close/dd.Close.shift(1)-1 dd['s00_return_3']=dd.Close/dd.Close.shift(3)-1 dd['s03_return_1']=dd.s00_return_1.shift(3) dd['s03_return_3']=dd.s00_return_3.shift(3) dd['s05_return_1']=dd.s00_return_1.shift(5) dd['s05_return_3']=dd.s00_return_3.shift(5) dd['s00_rsi_14']=ta.rsi(dd.Close, 14) dd['s00_rsi_7']=ta.rsi(dd.Close, 7) dd['s00_willR_14']=ta.wr(dd.High,dd.Low,dd.Close,14) dd['s00_willR_7']=ta.wr(dd.High,dd.Low,dd.Close,7) dd['s00_stoch_sig_14_3']=ta.stoch_signal(dd.High,dd.Low,dd.Close,14,3) dd['s00_stoch_sig_7_3']=ta.stoch_signal(dd.High,dd.Low,dd.Close,7,3) dd['s00_cci_20_0015']=ta.cci(dd.High,dd.Low,dd.Close,20,0.015) dd['s00_cci_20_005']=ta.cci(dd.High,dd.Low,dd.Close,20,0.05) dd['s00_macd_12_26_9']=ta.macd_diff(dd.Close,12,26,9) dd['s00_macd_7_14_9']=ta.macd_diff(dd.Close,7,14, 9) dd['s00_kst_9']=ta.kst(dd.Close)-ta.kst_sig(dd.Close) dd['s01_rsi_14']=dd.s00_rsi_14.shift(+1) dd['s01_rsi_7']=dd.s00_rsi_7.shift(+1) dd['s01_willR_14']=dd.s00_willR_14.shift(+1) dd['s01_willR_7']=dd.s00_willR_7.shift(+1) dd['s01_stoch_sig_14_3']=dd.s00_stoch_sig_14_3.shift(+1) dd['s01_stoch_sig_7_3']=dd.s00_stoch_sig_7_3.shift(+1)
import matplotlib.pyplot as plt
import ta
df = pd.read_csv('./data/coinbase_daily.csv')
df = df.dropna().reset_index().sort_values('Date')
ta_df = pd.DataFrame()
ta_df['RSI'] = ta.rsi(df["Close"])
ta_df['MFI'] = ta.money_flow_index(
df["High"], df["Low"], df["Close"], df["Volume BTC"])
ta_df['TSI'] = ta.tsi(df["Close"])
ta_df['UO'] = ta.uo(df["High"], df["Low"], df["Close"])
ta_df['Stoch'] = ta.stoch(df["High"], df["Low"], df["Close"])
ta_df['Stoch_Signal'] = ta.stoch_signal(df["High"], df["Low"], df["Close"])
ta_df['WR'] = ta.wr(df["High"], df["Low"], df["Close"])
ta_df['AO'] = ta.ao(df["High"], df["Low"])
ta_df['MACD'] = ta.macd(df["Close"])
ta_df['MACD_signal'] = ta.macd_signal(df["Close"])
ta_df['MACD_diff'] = ta.macd_diff(df["Close"])
ta_df['EMA_fast'] = ta.ema_indicator(df["Close"])
ta_df['EMA_slow'] = ta.ema_indicator(df["Close"])
ta_df['Vortex_pos'] = ta.vortex_indicator_pos(
df["High"], df["Low"], df["Close"])
ta_df['Vortex_neg'] = ta.vortex_indicator_neg(
df["High"], df["Low"], df["Close"])
ta_df['Vortex_diff'] = abs(
ta_df['Vortex_pos'] -
ta_df['Vortex_neg'])
ta_df['Trix'] = ta.trix(df["Close"])
def get_data(context, data_, window):
# Crear ventana de datos.
h1 = data_.history(
context.symbols,
context.row_features,
bar_count=window,
frequency=str(context.bar_period) + "T",
)
h1 = h1.swapaxes(2, 0)
norm_data = []
close_prices = []
for i, asset in enumerate(context.assets):
data = h1.iloc[i]
close = h1.iloc[i].close
if context.include_ha:
ha = heikenashi(data)
data = pd.concat((data, ha), axis=1)
for period in [3, 6, 8, 10, 15, 20]:
data["rsi" + str(period)] = ta.rsi(data.close,
n=period,
fillna=True)
data["stoch" + str(period)] = ta.stoch(data.high,
data.low,
data.close,
n=period,
fillna=True)
data["stoch_signal" + str(period)] = ta.stoch_signal(
high=data.high,
low=data.low,
close=data.close,
n=period,
d_n=3,
fillna=True)
data["dpo" + str(period)] = ta.dpo(close=data.close,
n=period,
fillna=True)
data["atr" + str(period)] = ta.average_true_range(high=data.high,
low=data.low,
close=data.close,
n=period,
fillna=True)
for period in [6, 7, 8, 9, 10]:
data["williams" + str(period)] = ta.wr(high=data.high,
low=data.low,
close=data.close,
lbp=period,
fillna=True)
for period in [12, 13, 14, 15]:
data["proc" + str(period)] = ta.trix(close=data.close,
n=period,
fillna=True)
data["macd_diff"] = ta.macd_diff(close=data.close,
n_fast=15,
n_slow=30,
n_sign=9,
fillna=True)
data["macd_signal"] = ta.macd_signal(close=data.close,
n_fast=15,
n_slow=30,
n_sign=9,
fillna=True)
data["bb_high_indicator"] = ta.bollinger_hband_indicator(
close=data.close, n=15, ndev=2, fillna=True)
data["bb_low_indicator"] = ta.bollinger_lband_indicator(
close=data.close, n=15, ndev=2, fillna=True)
data["dc_high_indicator"] = ta.donchian_channel_hband_indicator(
close=data.close, n=20, fillna=True)
data["dc_low_indicator"] = ta.donchian_channel_lband_indicator(
close=data.close, n=20, fillna=True)
data["ichimoku_a"] = ta.ichimoku_a(high=data.high,
low=data.low,
n1=9,
n2=26,
fillna=True)
data.fillna(method="bfill")
# Normalizar los valores
for feature in data.columns:
norm_feature = preprocessing.normalize(
data[feature].values.reshape(-1, 1), axis=0)
data[feature] = pd.DataFrame(data=norm_feature,
index=data.index,
columns=[feature])
norm_data.append(data.values)
close_prices.append(close)
context.features = data.columns
return np.array(norm_data), np.array(close_prices)
s=7,
m=14,
l=28,
ws=4,
wm=2,
wl=1,
fillna=True)
X['stoch_signal'] = ta.stoch_signal(price['High'],
price['Low'],
price['Adj. Close'],
n=14,
d_n=3,
fillna=True)
X['wr'] = ta.wr(price['High'],
price['Low'],
price['Adj. Close'],
lbp=14,
fillna=True)
X['ao'] = ta.ao(price['Low'], price['Adj. Close'], s=5, l=34, fillna=True)
X['daily_return'] = ta.daily_return(price['Adj. Close'], fillna=True)
# </editor-fold>
print('creating TA test set')
# <editor-fold desc="Create the test set">
X_test = pd.DataFrame(index=price_test.index,
data={
'bollinger_hband_indicator':
np.array([np.nan] * price_test.index.shape[0])
})
X_test['bollinger_hband_indicator'] = ta.bollinger_hband_indicator(
price_test['Adj. Close'], n=20, ndev=2, fillna=True)
def get_trayectory(self, t_intervals):
"""
:param t_intervals: número de intervalos en cada trayectoria
:return: Datos con características de la trayectoria sintética y precios de cierre en bruto de al misma
"""
trayectories = []
closes = []
p = True
for i, asset in enumerate(self.context.assets):
synthetic_return = np.exp(
self.drift[i] + self.stdev[i] * norm.ppf(np.random.rand((t_intervals * self.frequency) + self.frequency, 1)))
initial_close = self.close[i, -1]
synthetic_close = np.zeros_like(synthetic_return)
synthetic_close[0] = initial_close
for t in range(1, synthetic_return.shape[0]):
synthetic_close[t] = synthetic_close[t - 1] * synthetic_return[t]
OHLC = []
for t in range(synthetic_return.shape[0]):
if t % self.frequency == 0 and t > 0:
open = synthetic_close[t - self.frequency]
high = np.max(synthetic_close[t - self.frequency: t])
low = np.min(synthetic_close[t - self.frequency: t])
close = synthetic_close[t]
OHLC.append([open, high, close, low])
data = pd.DataFrame(data=OHLC, columns=["open", "high", "low", "close"])
close = data.close
if self.context.include_ha:
ha = heikenashi(data)
data = pd.concat((data, ha), axis=1)
for period in [3, 6, 8, 10, 15, 20]:
data["rsi" + str(period)] = ta.rsi(data.close, n=period, fillna=True)
data["stoch" + str(period)] = ta.stoch(data.high, data.low, data.close, n=period, fillna=True)
data["stoch_signal" + str(period)] = ta.stoch_signal(high=data.high,
low=data.low,
close=data.close,
n=period,
d_n=3,
fillna=True)
data["dpo" + str(period)] = ta.dpo(close=data.close,
n=period,
fillna=True)
data["atr" + str(period)] = ta.average_true_range(high=data.high,
low=data.low,
close=data.close,
n=period,
fillna=True)
for period in [6, 7, 8, 9, 10]:
data["williams" + str(period)] = ta.wr(high=data.high,
low=data.low,
close=data.close,
lbp=period,
fillna=True)
for period in [12, 13, 14, 15]:
data["proc" + str(period)] = ta.trix(close=data.close,
n=period,
fillna=True)
data["macd_diff"] = ta.macd_diff(close=data.close,
n_fast=15,
n_slow=30,
n_sign=9,
fillna=True)
data["macd_signal"] = ta.macd_signal(close=data.close,
n_fast=15,
n_slow=30,
n_sign=9,
fillna=True)
data["bb_high_indicator"] = ta.bollinger_hband_indicator(close=data.close,
n=15,
ndev=2,
fillna=True)
data["bb_low_indicator"] = ta.bollinger_lband_indicator(close=data.close,
n=15,
ndev=2,
fillna=True)
data["dc_high_indicator"] = ta.donchian_channel_hband_indicator(close=data.close,
n=20,
fillna=True)
data["dc_low_indicator"] = ta.donchian_channel_lband_indicator(close=data.close,
n=20,
fillna=True)
data["ichimoku_a"] = ta.ichimoku_a(high=data.high,
low=data.low,
n1=9,
n2=26,
fillna=True)
data.fillna(method="bfill")
# Normalizar los valores
for feature in data.columns:
norm_feature = preprocessing.normalize(data[feature].values.reshape(-1, 1), axis=0)
data[feature] = pd.DataFrame(data=norm_feature, index=data.index, columns=[feature])
self.assets = data.columns
trayectories.append(data.values)
closes.append(close)
return np.array(trayectories), np.array(closes)