def test_datatypes(self):
dataset = obtain_ohlc_data('MSFT')
period: int = 12
ma_ohlc, sig_cols = MovingAverage(period,
MAType.ema).get_signal(dataset)
self.assertEqual(
type(ma_ohlc.get(sig_cols)['ma_period_' + str(period)][0]),
np.float64)
def test_ma_visualization(self):
dataset = obtain_ohlc_data('MSFT')[:300]
period: int = 7
ma_ohlc, sig_cols = MovingAverage(dataset, period,
MAType.ema).get_signal()
plt.plot(ma_ohlc.index, ma_ohlc["Adj Close"], label="Adj Close")
plt.plot(ma_ohlc.index, ma_ohlc["ma_period_7"], label="MA")
plt.show()
def test_macd_visualization(self):
dataset = obtain_ohlc_data('MSFT')
macd_included_df, sig_cols = Macd(12, 26, 9).get_signal(dataset)
# macd_included_df = (macd_included_df - macd_included_df.min()) / (macd_included_df.max() - macd_included_df.min())
macd_included_df[['MACD', 'Signal']][:100].plot()
plt.show()
# macd_included_df[['Adj Close']][:100].plot()
# plt.show()
print(macd_included_df)
def __init__(self, commodity: str, strategies: Dict[Strategy, float],
risk: Dict[str, Callable[[float], float]]):
self.ohlc_data = obtain_ohlc_data(commodity)
self.current_trade = BHSSignal.hold
self.current_trade_executed = True
self.strategies = strategies
self.strategy_signal_columns = []
self.analytics = AnalyticsManager(commodity)
self.risk = risk
def test_data_driven_slope(self):
dataset = obtain_ohlc_data('MSFT')
df, col = Slope(dataset).get_signal()
df = df[:20]
plt.scatter(df.index, df[col[0]], c='r')
plt.scatter(df.index, df[col[1]], c='g')
plt.plot(df.index, df['Adj Close'])
df.plot()
plt.show()
def test_basic_sig_merge(self):
ohlc_data: pd.DataFrame = obtain_ohlc_data('AAPL')
strategies: Dict[Strategy, float] = {
MADoubleCrossOver(1, 5, 13): 0.5,
MADoubleCrossOver(2, 50, 200): 0.5,
}
ohlc_data = SignalManager(ohlc_data, strategies).get_master_signal()
self.assertEqual(len(ohlc_data.get('master_sig')),
len(ohlc_data.get('open')))
def test_scatter(self):
df = obtain_ohlc_data('MSFT')
df, cols = Slope(df).get_signal()
df = df[:10]
# Plot results
plt.scatter(df.index, df['local_minima'], c='r')
# plt.scatter(df.index, df['local_maxima'], c='g')
# plt.scatter(df.index, df[cols[0]], c='b')
df['Adj Close'].plot()
plt.show()
import talib
from market_data.ohlc_data import obtain_ohlc_data
import pandas as pd
from scipy.signal import lfilter
def smooth_algo(data, cnt: int = 200, smooth_intensity: int = 3):
data = data[:cnt]
n = smooth_intensity # the larger n is, the smoother curve will be
b = [1.0 / n] * n
a = 1
yy = lfilter(b, a, data)[n - 1:]
return yy
symbol = "EURUSD=X"
ohlc_data = obtain_ohlc_data(symbol, include_all_indicators=True)
smoothened_data = smooth_algo(ohlc_data["Adj Close"], 4500, 2)
p_3 = smoothened_data[3:]
p_7 = smoothened_data[7:]
p_21 = smoothened_data[21:]
p_50 = smoothened_data[50:]
diff_p_3 = (p_3 - smoothened_data[:-3])[:-47]
diff_p_7 = (p_7 - smoothened_data[:-7])[:-43]
diff_p_21 = (p_21 - smoothened_data[:-21])[:-29]
diff_p_50 = (p_50 - smoothened_data[:-50])
df = pd.DataFrame()
df['P3'] = diff_p_3
df['P7'] = diff_p_7
def get_data(self):
return obtain_ohlc_data('EURUSD=X')["Adj Close"].to_numpy()
from market_data.ohlc_data import obtain_ohlc_data
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
dataset = obtain_ohlc_data('MSFT', include_all_indicators=True)[:]
b = dataset["Open"]
a = 9
for c in dataset.columns:
if str(c).__contains__("CDL"):
x = np.array(dataset["Close"])
p = np.where(np.array(dataset[c]) == 100)[0]
t = np.where(np.array(dataset[c]) == -100)[0]
plt.plot(x)
plt.plot(p, x[p], "v")
plt.plot(t, x[t], "^")
plt.show()
def test_ma_columns(self):
dataset = obtain_ohlc_data('MSFT')
period: int = 12
ma_ohlc, sig_cols = MovingAverage(period,
MAType.ema).get_signal(dataset)
self.assertIn(sig_cols[0], ma_ohlc.count(axis=0).index.tolist())
def test_ma_count(self):
dataset = obtain_ohlc_data('MSFT')
ma_ohlc, sig_cols = MovingAverage(12, MAType.ema).get_signal(dataset)
self.assertEqual(len(sig_cols), 1)
def test_datatypes(self):
dataset = obtain_ohlc_data('MSFT')
macd_included_df, sig_cols = Macd(12, 26, 9).get_signal(dataset)
self.assertEqual(type(macd_included_df['Signal'][0]), numpy.float64)
self.assertEqual(type(macd_included_df['MACD'][0]), numpy.float64)
def test_macd_columns(self):
dataset = obtain_ohlc_data('MSFT')
macd_included_df, sig_cols = Macd(12, 26, 9).get_signal(dataset)
self.assertEqual(
macd_included_df.get(sig_cols).count(axis=0).index.tolist(),
['MACD', 'Signal'])
def test_macd_count(self):
dataset = obtain_ohlc_data('MSFT')
macd_included_df, sig_cols = Macd(12, 26, 9).get_signal(dataset)
self.assertEqual(len(macd_included_df.get(sig_cols).count(axis=0)), 2)
def __init__(self, symbol: str):
self.ohlc_data = obtain_ohlc_data(symbol, include_all_indicators=True)
