def ich():
ica = ta.ichimoku_a(high, low, n1=9, n2=26, visual=False, fillna=False)
icb = ta.ichimoku_b(high,
low,
n2=26,
n3=52,
visual=False,
fillna=False)
if ica[-1] > icb[-1]:
trn_ich_status = "ICH Signal is: Buy"
elif ica[-1] < icb[-1]:
trn_ich_status = "ICH Signal is: Sell"
else:
trn_ich_status = "ICH Signal is: Hold"
return trn_ich_status
def process_data(data):
data['BB_5'] = ta.bollinger_mavg(
data['CLOSE'], 5) #bollinger_moving average 5 trading periods
data['BB_10'] = ta.bollinger_mavg(
data['CLOSE'], 10) #bollinger_moving average 10 trading periods
data['BB_20'] = ta.bollinger_mavg(
data['CLOSE'], 20) # bollinger_moving average 20 periods
data['ADX'] = ta.adx(data['HIGH'], data['LOW'], data['CLOSE'],
14) #Average Directional Index
data['ATR'] = ta.average_true_range(data['HIGH'], data['LOW'],
data['CLOSE'], 14) #Average True Range
data['CCI'] = ta.cci(data['HIGH'], data['LOW'], data['CLOSE'],
14) #Commodity Channel Index
data['DCH'] = ta.donchian_channel_hband(
data['CLOSE']) #Donchian Channel High Band
data['DCL'] = ta.donchian_channel_lband(
data['CLOSE']) #Donchian Channel Low Band
data['DPO'] = ta.dpo(data['CLOSE']) #Detrend Price Oscilator
data['EMAf'] = ta.ema_fast(
data['CLOSE']) #Expornential Moving Average fast
data['EMAs'] = ta.ema_slow(
data['CLOSE']) #Expornential Moving Average slow
data['FI'] = ta.force_index(
data['CLOSE'],
data['VOLUME']) # Force Index(reveals the value of a trend)
data['ICHa'] = ta.ichimoku_a(data['HIGH'], data['LOW']) #Ichimoku A
data['ICHb'] = ta.ichimoku_b(data['HIGH'], data['LOW']) #Ichimoku B
data['KC'] = ta.keltner_channel_central(
data['HIGH'], data['LOW'], data['CLOSE']) #Keltner channel(KC) Central
data['KST'] = ta.kst(
data['CLOSE']
) #KST Oscillator (KST) identify major stock market cycle junctures
data['MACD'] = ta.macd(
data['CLOSE']) # Moving Average convergence divergence
data['OBV'] = ta.on_balance_volume_mean(
data['CLOSE'], data['VOLUME']) # on_balance_volume_mean
data['RSI'] = ta.rsi(data['CLOSE']) # Relative Strength Index (RSI)
data['TRIX'] = ta.trix(
data['CLOSE']
) #Shows the percent rate of change of a triple exponentially smoothed moving average
data['TSI'] = ta.tsi(data['CLOSE']) #True strength index (TSI)
data['ROC1'] = (data['CLOSE'] - data['OPEN']) / data['OPEN']
data['RET'] = data['CLOSE'].pct_change()
data['y'] = np.where(data['OPEN'] <= data['CLOSE'], 1, -1)
data = data.dropna()
return data
def plot(symbol, grouping_range, ts_start=0, ts_end=0, skip_orders=False):
if not ts_start:
d = datetime.date(2018, 1, 11)
ts_start = mktime(d.timetuple())
if not ts_end:
d2 = datetime.date(2018, 12, 18)
ts_end = mktime(d2.timetuple())
df = get_dataframe_from_timerange(db,
symbol,
grouping_range,
ts_start=ts_start,
ts_end=ts_end)
df['ema_fast'] = ta.ema_fast(df['close'], 32)
df['ema_slow'] = ta.ema_slow(df['close'], 9)
df['bollinger_hband'] = ta.bollinger_hband(df['close'], 26, 1.9)
df['bollinger_lband'] = ta.bollinger_lband(df['close'], 26, 1.9)
df['ichimoku_a'] = ta.ichimoku_a(df['high'], df['low'])
df['ichimoku_b'] = ta.ichimoku_b(df['high'], df['low'])
# df['keltner_low'] = ta.keltner_channel_lband(df['high'], df['low'], df['close'])
# df['keltner_high'] = ta.keltner_channel_hband(df['high'], df['low'], df['close'])
# df['mkl'] = multiplied_keltner_channel_lband(df['high'], df['low'], df['close'], n=14, m=2)
# df['atr'] = ta.average_true_range(df['high'], df['low'], df['close'], n=14)
# df['ema'] = ta.ema_slow(df['close'], 14)
# df['mkh'] = multiplied_keltner_channel_hband(df['high'], df['low'], df['close'], n=14, m=2)
# df['mkh'] = multiplied_keltner_channel_hband(df['high'], df['low'], df['close'], n=14, m=2)
# plotly_candles(df, 'test_plot', ['ichimoku_a', 'ichimoku_b'])
if not skip_orders:
orders = get_orders(symbol, ts_start=ts_start, ts_end=ts_end)
else:
orders = None
plotly_candles(df,
'test_plot',
orders=orders,
indicators=['bollinger_hband', 'bollinger_lband'])
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"])
ta_df['Mass_index'] = ta.mass_index(df["High"], df["Low"])
ta_df['CCI'] = ta.cci(df["High"], df["Low"], df["Close"])
ta_df['DPO'] = ta.dpo(df["Close"])
ta_df['KST'] = ta.kst(df["Close"])
ta_df['KST_sig'] = ta.kst_sig(df["Close"])
ta_df['KST_diff'] = (
ta_df['KST'] -
ta_df['KST_sig'])
ta_df['Ichimoku_a'] = ta.ichimoku_a(df["High"], df["Low"], visual=True)
ta_df['Ichimoku_b'] = ta.ichimoku_b(df["High"], df["Low"], visual=True)
ta_df['Aroon_up'] = ta.aroon_up(df["Close"])
ta_df['Aroon_down'] = ta.aroon_down(df["Close"])
ta_df['Aroon_ind'] = (
ta_df['Aroon_up'] -
ta_df['Aroon_down']
)
ta_df['ATR'] = ta.average_true_range(
df["High"],
df["Low"],
df["Close"])
ta_df['BBH'] = ta.bollinger_hband(df["Close"])
ta_df['BBL'] = ta.bollinger_lband(df["Close"])
ta_df['BBM'] = ta.bollinger_mavg(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)
fillna=True)
X['dpo'] = ta.dpo(price['Adj. Close'], n=20, fillna=True)
X['kst_sig'] = ta.kst_sig(price['Adj. Close'],
r1=10,
r2=15,
r3=20,
r4=30,
n1=10,
n2=10,
n3=10,
n4=15,
nsig=9,
fillna=True)
X['ichimoku_a'] = ta.ichimoku_a(price['High'],
price['Low'],
n1=9,
n2=26,
fillna=True)
X['ichimoku_b'] = ta.ichimoku_b(price['High'],
price['Low'],
n2=26,
n3=52,
fillna=True)
X['money_flow_index'] = ta.money_flow_index(price['High'],
price['Low'],
price['Adj. Close'],
price['Volume'],
n=14,
fillna=True)
X['rsi'] = ta.rsi(price['Adj. Close'], n=14, fillna=True)
X['tsi'] = ta.tsi(price['Adj. Close'], r=25, s=13, 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)