def ATR(self, df, period = 14, multiplier = 1):
""" ATR bands input ATR(time = 14, multiplier = 1)."""
df.columns = map(str.lower, df.columns)
df.sort_index(inplace = True)
df = df.reset_index()
if type(period) == int:
z = ta.average_true_range(df['high'], df['low'], df['close'], period)
z = z.iloc[-1]
return float(z)*multiplier
else:
for i in period:
df[f'ATR{i}'] = ta.average_true_range(df['high'], df['low'], df['close'], i)
return df.iloc[-1]
def multiplied_keltner_channel_lband(high,
low,
close,
n=14,
m=2,
fillna=False):
"""Multiplied Keltner channel (MKC)
Showing a simple moving average line (low) of typical price.
https://en.wikipedia.org/wiki/Keltner_channel
Args:
high(pandas.Series): dataset 'High' column.
low(pandas.Series): dataset 'Low' column.
close(pandas.Series): dataset 'Close' column.
n(int): n period.
m(int): multiplier
Returns:
pandas.Series: New feature generated.
"""
lower = ta.ema_fast(close, n, fillna) - (
m * ta.average_true_range(high, low, close, n, fillna))
if fillna:
lower = lower.fillna(method='backfill')
return pd.Series(lower, name='mkc_lband')
def __init__(self,
prices,
balance,
atr_period=48,
entry_period=48,
exit_period=24,
max_entry=10,
**kwargs):
self.name = 'Turtle Trading'
self.prices = prices
self.balance = balance
self.signals = pd.DataFrame({
'ATR':
ta.average_true_range(self.prices['High'],
self.prices['Low'],
self.prices['Close'],
n=atr_period)
})
self.init_period = atr_period
self.max_entry = max_entry
self.entry_period = entry_period
self.exit_period = exit_period
self.entrySig = [0]
self.exitSig = [0]
self.stopSig = [0]
self.unit = np.zeros(len(self.prices))
self.entryFlag = False
self.entryPrice = None
self.entryCounter = 0
def enrich_sampleset(df):
df['OBV'] = ta.on_balance_volume(df['close'], df['volume'])
df['ADX'] = ta.adx(df['high'], df['low'], df['close'], n=14)
df['VPT'] = ta.volume_price_trend(df['close'], df['volume'])
df['ATR'] = ta.average_true_range(df['high'], df['low'], df['close'], n=14)
df['MFI'] = ta.money_flow_index(df['high'], df['low'], df['close'],
df['volume'])
df['KST'] = ta.kst(df['close'])
return ta.utils.dropna(df)
def at():
atr = ta.average_true_range(high, low, close, n=14, fillna=False)
if atr[-1] >= 1.5 + mean(atr[-10:]):
vot_status_atr = "Buy"
elif atr[-1] <= mean(atr[-10:] - 1.5):
vot_status_atr = "Sell"
else:
vot_status_atr = "Hold"
return vot_status_atr
def ATR(self, df, period=14, multiplier=1):
""" ATR bands input ATR(time = 14, multiplier = 1)."""
df.columns = map(str.lower, df.columns) #UPGRADE
df.sort_index(inplace=True)
df = df.reset_index() #UPGRADE
z = ta.average_true_range(df['high'], df['low'], df['close'], period)
z = z.iloc[-1]
return float(z) * multiplier
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 __init__(self,
prices,
balance,
atr_period=48,
fast_period=12,
slow_period=26,
signal_period=9,
max_entry=1,
**kwargs):
self.name = 'SMA strategy'
self.prices = prices
self.balance = balance
self.signals = pd.DataFrame()
self.signals['ATR'] = ta.average_true_range(self.prices['High'],
self.prices['Low'],
self.prices['Close'],
n=atr_period)
self.signals['MACD'] = ta.macd(self.prices['Close'],
n_fast=fast_period,
n_slow=slow_period)
self.signals['MACD_SIG'] = ta.macd_signal(self.prices['Close'],
n_fast=fast_period,
n_slow=slow_period,
n_sign=signal_period)
self.signals['MACD_HIST'] = ta.macd_diff(self.prices['Close'],
n_fast=fast_period,
n_slow=slow_period,
n_sign=signal_period)
self.signals['MACD'].fillna(0)
self.init_period = slow_period
self.stop_period = 10
self.max_entry = max_entry
self.gamma_z = 0.1
self.entrySig = [0]
self.exitSig = [0]
self.stopSig = [0]
self.unit = np.zeros(len(self.prices))
self.entryFlag = False
self.entryPrice = None
self.entryCounter = 0
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"])
ta_df['BBHI'] = ta.bollinger_hband_indicator(
df["Close"])
ta_df['BBLI'] = ta.bollinger_lband_indicator(
df["Close"])
ta_df['KCC'] = ta.keltner_channel_central(
df["High"],
df["Low"],
df["Close"])
ta_df['KCH'] = ta.keltner_channel_hband(
df["High"],
df["Low"],
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['ease_of_movement'] = ta.ease_of_movement(price['High'],
price['Low'],
price['Adj. Close'],
price['Volume'],
n=20,
fillna=True)
X['volume_price_trend'] = ta.volume_price_trend(price['Adj. Close'],
price['Volume'],
fillna=True)
X['negative_volume_index'] = ta.negative_volume_index(price['Adj. Close'],
price['Volume'],
fillna=True)
X['average_true_range'] = ta.average_true_range(price['High'],
price['Low'],
price['Adj. Close'],
n=14,
fillna=True)
# X['KCU'] = ta.keltner_channel_hband_indicator(price['High'], price['Low'], price['Adj. Close'], n=10, fillna=True)
X['keltner_channel_lband_indicator'] = ta.keltner_channel_lband_indicator(
price['High'], price['Low'], price['Adj. Close'], n=10, fillna=True)
X['donchian_channel_hband_indicator'] = ta.donchian_channel_hband_indicator(
price['Adj. Close'], n=20, fillna=True)
X['donchian_channel_lband_indicator'] = ta.donchian_channel_lband_indicator(
price['Adj. Close'], n=20, fillna=True)
X['macd_signal'] = ta.macd_signal(price['Adj. Close'],
n_fast=12,
n_slow=26,
n_sign=9,
fillna=True)
X['adx_pos'] = ta.adx_pos(price['High'],
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)