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
# %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',
)
data['blw'] = .20
def test_percent_d_invalid_period(self):
period = 128
with self.assertRaises(Exception) as cm:
stochastic.percent_d(self.high_data, self.low_data, self.close_data, period)
expected = "Error: data_len < period"
self.assertEqual(str(cm.exception), expected)
def test_percent_d_period_10(self):
period = 10
percent_d = stochastic.percent_d(self.high_data, self.low_data, self.close_data, period)
np.testing.assert_array_equal(percent_d, self.percent_d_period_10_expected)
def test_percent_d_period_6(self):
period = 6
percent_d = stochastic.percent_d(self.high_data, self.low_data, self.close_data, period)
print (percent_d.tolist())
np.testing.assert_array_equal(percent_d, self.percent_d_period_6_expected)
def test_percent_d_period_8(self):
period = 8
percent_d = stochastic.percent_d(self.data, period)
np.testing.assert_array_equal(percent_d, self.percent_d_period_8_expected)