def __init__(self,
balance,
tickers=None,
base_model=LinearRegression(),
n_prev=2,
wait=100,
steps_ahead=100,
k=5,
envelope='proportional',
log=False,
commission=.0002,
flat_rate=8):
if tickers is None:
tickers = s_and_p_names('2014-1-1', '2015-11-02')
super(TSEBuyAndHoldStrategy, self).__init__(balance,
tickers,
log=log,
commission=commission,
wait=wait,
flat_rate=flat_rate)
self.model = TimeSeriesRegressor(base_model, n_ahead=1, n_prev=n_prev)
self.steps_ahead = steps_ahead
self.k = k
self.envelope = envelope
def get_data_dependece(X,
data_sizes,
folds=20,
test_size=30,
n_prev=2,
log=True):
bests = np.empty((len(data_sizes), folds, X.shape[1]))
for i, data_size in enumerate(data_sizes):
pairs = cascade_cv(len(X),
folds,
data_size=data_size,
test_size=test_size,
number=True)
for j, pair in enumerate(pairs):
if log:
print('data size: {} trial {} '.format(data_size, j))
X_train, X_test = np.array(X.iloc[pair[0], :]), np.array(
X.iloc[pair[1], :])
tsr = TimeSeriesRegressor(LinearRegression(), n_prev=n_prev)
tsr.fit(X_train)
fc = tsr.forecast(X_train, len(X_test))
def changes(X, start=0, end=-1):
return np.array(
[X[end, i] - X[start, i] for i in range(X.shape[1])])
best_is = changes(fc).argsort()[::-1]
for k in range(X.shape[1]):
bests[i, j, k] = changes(X_test)[best_is[k]] - np.mean(
changes(X_test))
return bests
from TimeSeriesEstimator import TimeSeriesRegressor, time_series_split
def mse(X1, X2, multioutput='raw_values'):
if multioutput == 'raw_values':
return np.mean((X1 - X2)**2, axis=0)**.5
if multioutput == 'uniform_average':
return np.mean(np.mean((X1 - X2)**2, axis=0)**.5)
X = datasets('sp500')
names = list(X.columns.values)
X_train, X_test = time_series_split(X)
n_prev = 2
tsr = TimeSeriesRegressor(LinearRegression(), n_prev=n_prev)
tsr.fit(X_train)
fc = tsr.forecast(X_train, len(X_test), noise=.2, n_paths=200)
fc_mean = tsr.forecast(X_train,
len(X_test),
noise=.2,
n_paths=200,
combine='mean')
#or for speed
#fc_mean = np.mean(fc, axis=0)
plt.plot(np.transpose(fc[:, :, 1]), 'r', alpha=.05)
plt.plot(np.transpose(fc_mean[:, 1]), 'b', label='Mean Forecast')
plt.plot(X_test[:, 1], 'g', label='Actual Price')
plt.legend()
plt.xlabel('days')
