def _load(self):
self._times = self._feeder.get_times()
# Default Params
ma_fast_period = 23 # MACD Fast Length
ma_slow_period = 50 # MACD Slow Length
cycle_length = 10 # Cycle Length
d1_length = 3 # 1st %D Length
d2_length = 3 # 2nd %D Length
# close
prices_close = self._feeder.get_prices(200, AppliedPrice.CLOSE)
# macd
ema_fast = talib.EMA(prices_close, ma_fast_period)
ema_slow = talib.EMA(prices_close, ma_slow_period)
macd = ema_fast - ema_slow
# stocastic from the macd
k1 = stochastic.percent_k(macd, cycle_length)
d1 = talib.EMA(k1, d1_length)
# stocastic from the macd
k2 = stochastic.percent_k(d1, cycle_length)
d2 = talib.EMA(k2, d2_length)
self._prices = d2[-self._feeder.bar_count:].tolist()
def next_calculation(self, candle):
if self.get_datawindow() is not None:
self.value['stochastic_percent_k'] = indicator.percent_k(
self.get_close(), self.params['period'])[-1]
if self.get_datawindow() is not None:
self.value['stochastic_percent_d'] = indicator.percent_k(
self.get_close(), self.params['period'])[-1]
def prepare_data(symbol):
coin = symbol
coin_1 = crypto_data(coin)
df = coin_1
df['SMA_20'] = sma(df['Close'], 20)
df['SMA_50'] = sma(df['Close'], 50)
df['EMA_20'] = ema(df['Close'], 20)
df['EMA_50'] = ema(df['Close'], 50)
df['MACD'] = macd(df['Close'], 26, 12)
df['per_k_stoch_10'] = percent_k(df['Close'], 10)
df['per_d_stoch_10'] = percent_d(df['Close'], 10)
df['OBV'] = obv(df['Close'], df['Volume'])
fp = []
for price in df['Close']:
fp.append(price)
fp.pop(0)
fp.append(df['Close'].mean())
df['FP'] = fp
df_predict = df.tail(1)
df.drop(df.tail(1).index, inplace=True)
label = []
for i in range(len(df)):
if df['FP'][i] > df['Close'][i]:
label.append(1)
else:
label.append(0)
df['goes_up'] = label
df = df.drop(['FP'], axis=1)
df = df.fillna(df.mean())
df_predict = df_predict.drop(['FP'], axis=1)
return df, df_predict
import matplotlib.pyplot as plt
# %matplotlib inline
from matplotlib import style
style.use('ggplot')
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)
socket = fxcmpy.fxcmpy(config_file='fxcm.cfg')
data = socket.get_candles(instrument='GBP/USD',
period='D1',
start=dt.datetime(2015, 1, 1),
end=dt.datetime(2018, 6, 10))
data['percent_k'] = percent_k(data['askclose'], 20)
data['percent_d'] = percent_d(data['askclose'], 20)
fig = plt.figure(figsize=(12, 8))
ax1 = fig.add_subplot(111, xlabel='Date', ylabel='Close')
data['askclose'].plot(ax=ax1, color='r', lw=1)
ss_plot = plt.figure(figsize=(12, 8))
ax3 = ss_plot.add_subplot(111, ylabel='Percent')
data['percent_k'].plot(ax=ax3, color='r')
data['percent_d'].plot(ax=ax3, color='g')
data['ovr'] = .80
data['ovr'].plot(
ax=ax3,
color='b',
)
def _get_ta_features(high, low, close, volume, desc):
"""
Returns a dict containing the technical analysis indicators calculated on the given
high, low, close and volumes.
"""
ta = {}
# Set numpy to ignore division error and invalid values (since not all features are complete)
old_settings = np.seterr(divide='ignore', invalid='ignore')
record_count = len(close)
# Determine relative moving averages
for _short, _long in desc['rsma']:
if record_count < _short or record_count < _long:
logging.error(
"get_ta_features: not enough records for rsma (short={}, long={}, records={})"
.format(_short, _long, record_count))
continue
ta['rsma_{}_{}'.format(_short,
_long)] = relative_sma(close, _short, _long)
for _short, _long in desc['rema']:
if record_count < _short or record_count < _long:
logging.error(
"get_ta_features: not enough records for rema (short={}, long={}, records={})"
.format(_short, _long, record_count))
continue
ta['rema_{}_{}'.format(_short,
_long)] = relative_ema(close, _short, _long)
# MACD Indicator
if 'macd' in desc:
for _short, _long in desc['macd']:
if record_count < _short or record_count < _long:
logging.error(
"get_ta_features: not enough records for rema (short={}, long={}, records={})"
.format(_short, _long, record_count))
continue
ta['macd_{}_{}'.format(
_short, _long)] = moving_average_convergence_divergence(
close, _short, _long)
# Aroon Indicator
if 'ao' in desc:
for _period in desc['ao']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for ao (period={}, records={})"
.format(_period, record_count))
continue
ta['ao_{}'.format(_period)] = aroon_oscillator(close, _period)
# Average Directional Movement Index (ADX)
if 'adx' in desc:
for _period in desc['adx']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for adx (period={}, records={})"
.format(_period, record_count))
continue
ta['adx_{}'.format(_period)] = average_directional_index(
close, high, low, _period)
# Difference between Positive Directional Index(DI+) and Negative Directional Index(DI-)
if 'wd' in desc:
for _period in desc['wd']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for wd (period={}, records={})"
.format(_period, record_count))
continue
ta['wd_{}'.format(_period)] = \
positive_directional_index(close, high, low, _period) \
- negative_directional_index(close, high, low, _period)
# Percentage Price Oscillator
if 'ppo' in desc:
for _short, _long in desc['ppo']:
if record_count < _short or record_count < _long:
logging.error(
"get_ta_features: not enough records for ppo (short={}, long={}, records={})"
.format(_short, _long, record_count))
continue
ta['ppo_{}_{}'.format(_short, _long)] = price_oscillator(
close, _short, _long)
# Relative Strength Index
if 'rsi' in desc:
for _period in desc['rsi']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for rsi (period={}, records={})"
.format(_period, record_count))
continue
ta['rsi_{}'.format(_period)] = relative_strength_index(
close, _period)
# Money Flow Index
if 'mfi' in desc:
for _period in desc['mfi']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for mfi (period={}, records={})"
.format(_period, record_count))
continue
ta['mfi_{}'.format(_period)] = money_flow_index(
close, high, low, volume, _period)
# True Strength Index
if 'tsi' in desc and len(close) >= 40:
if record_count < 40:
logging.error(
"get_ta_features: not enough records for tsi (period={}, records={})"
.format(40, record_count))
else:
ta['tsi'] = true_strength_index(close)
if 'boll' in desc:
for _period in desc['stoch']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for boll (period={}, records={})"
.format(_period, record_count))
continue
ta['boll_{}'.format(_period)] = percent_b(close, _period)
# Stochastic Oscillator
if 'stoch' in desc:
for _period in desc['stoch']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for stoch (period={}, records={})"
.format(_period, record_count))
continue
ta['stoch_{}'.format(_period)] = percent_k(close, _period)
# ta.py['stoch'] = percent_k(high, low, close, 14)
# Chande Momentum Oscillator
## Not available in ta.py
if 'cmo' in desc:
for _period in desc['cmo']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for cmo (period={}, records={})"
.format(_period, record_count))
continue
ta['cmo_{}'.format(_period)] = chande_momentum_oscillator(
close, _period)
# Average True Range Percentage
if 'atrp' in desc:
for _period in desc['atrp']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for atrp (period={}, records={})"
.format(_period, record_count))
continue
ta['atrp_{}'.format(_period)] = average_true_range_percent(
close, _period)
# Percentage Volume Oscillator
if 'pvo' in desc:
for _short, _long in desc['pvo']:
if record_count < _short or record_count < _long:
logging.error(
"get_ta_features: not enough records for pvo (short={}, long={}, records={})"
.format(_short, _long, record_count))
continue
ta['pvo_{}_{}'.format(_short, _long)] = volume_oscillator(
volume, _short, _long)
# Force Index
if 'fi' in desc:
fi = force_index(close, volume)
for _period in desc['fi']:
if record_count < _period:
logging.error(
"get_ta_features: not enough records for atrp (period={}, records={})"
.format(_period, record_count))
continue
ta['fi_{}'.format(_period)] = exponential_moving_average(
fi, _period)
# Accumulation Distribution Line
if 'adi' in desc:
ta['adi'] = accumulation_distribution(close, high, low, volume)
# On Balance Volume
if 'obv' in desc:
ta['obv'] = on_balance_volume(close, volume)
# Restore numpy error settings
np.seterr(**old_settings)
return ta
def test_percent_k_invalid_period(self):
period = 128
with self.assertRaises(Exception):
stochastic.percent_k(self.high_data, self.low_data, self.close_data, period)
def test_percent_k_period_10(self):
period = 10
percent_k = stochastic.percent_k(self.high_data, self.low_data, self.close_data, period)
np.testing.assert_array_equal(percent_k, self.percent_k_period_10_expected)
def test_percent_k_period_8(self):
period = 8
percent_k = stochastic.percent_k(self.data, period)
np.testing.assert_array_equal(percent_k, self.percent_k_period_8_expected)
