def test_check_single_cutoff_forecast_func_calls(self):
m = Prophet()
m.fit(self.__df)
mock_predict = pd.DataFrame({
'ds':
pd.date_range(start='2012-09-17', periods=3),
'yhat':
np.arange(16, 19),
'yhat_lower':
np.arange(15, 18),
'yhat_upper':
np.arange(17, 20),
'y':
np.arange(16.5, 19.5),
'cutoff': [datetime.date(2012, 9, 15)] * 3
})
# cross validation with 3 and 7 forecasts
for args, forecasts in ((['4 days', '10 days', '115 days'], 3),
(['4 days', '4 days', '115 days'], 7)):
with patch(
'prophet.diagnostics.single_cutoff_forecast') as mock_func:
mock_func.return_value = mock_predict
df_cv = diagnostics.cross_validation(m, *args)
# check single forecast function called expected number of times
self.assertEqual(diagnostics.single_cutoff_forecast.call_count,
forecasts)
def __init__(self, demand: float = 20, var_per_day: float = 0.1, var_per_season: float = 0.1) -> None:
self.__var_per_day = var_per_day
self.__var_per_season = var_per_season
self.__mean_demand = demand
self.__pt = Prophet(seasonality_mode="multiplicative")
self.__periods = 12 * 20
self.data_demand = None
def test_cross_validation_extra_regressors(self):
df = self.__df.copy()
df['extra'] = range(df.shape[0])
df['is_conditional_week'] = np.arange(df.shape[0]) // 7 % 2
m = Prophet()
m.add_seasonality(name='monthly', period=30.5, fourier_order=5)
m.add_seasonality(name='conditional_weekly',
period=7,
fourier_order=3,
prior_scale=2.,
condition_name='is_conditional_week')
m.add_regressor('extra')
m.fit(df)
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='4 days',
initial='135 days')
self.assertEqual(len(np.unique(df_cv['cutoff'])), 2)
period = pd.Timedelta('4 days')
dc = df_cv['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= period)
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y'])**2), 0.0)
def train(self, metric_data=None, prediction_duration=15):
"""Train the Prophet model and store the predictions in predicted_df."""
prediction_freq = "1MIN"
# convert incoming metric to Metric Object
if metric_data:
# because the rolling_data_window_size is set, this df should not bloat
self.metric += Metric(metric_data)
# Don't really need to store the model, as prophet models are not retrainable
# But storing it as an example for other models that can be retrained
self.model = Prophet(daily_seasonality=True,
weekly_seasonality=True,
yearly_seasonality=True)
_LOGGER.info("training data range: %s - %s", self.metric.start_time,
self.metric.end_time)
# _LOGGER.info("training data end time: %s", self.metric.end_time)
_LOGGER.debug("begin training")
self.model.fit(self.metric.metric_values)
future = self.model.make_future_dataframe(
periods=int(prediction_duration),
freq=prediction_freq,
include_history=False,
)
forecast = self.model.predict(future)
forecast["timestamp"] = forecast["ds"]
forecast = forecast[["timestamp", "yhat", "yhat_lower", "yhat_upper"]]
forecast = forecast.set_index("timestamp")
self.predicted_df = forecast
_LOGGER.debug(forecast)
def predict(ticker, start_date):
today = datetime.date.today()
end_date = today.strftime("%Y-%m-%d")
data = yf.download(ticker, start_date, end_date)
df_forecast = data.copy()
df_forecast.reset_index(inplace=True)
df_forecast["ds"] = df_forecast["Date"]
df_forecast["y"] = df_forecast["Adj Close"]
df_forecast = df_forecast[["ds", "y"]]
df_forecast
model = Prophet()
model.fit(df_forecast)
future = pd.to_datetime(end_date) + pd.DateOffset(days=7)
future_date = future.strftime("%Y-%m-%d")
dates = pd.date_range(start=end_date, end=future_date)
df_pred = pd.DataFrame({"ds": dates})
forecast = model.predict(df_pred)
prediction_list = forecast.tail(7).to_dict("records")
output = {}
for data in prediction_list:
date = data["ds"].strftime("%Y-%m-%d")
output[date] = data["trend"]
return output
def test_set_seasonality_mode(self):
# Setting attribute
m = Prophet()
self.assertEqual(m.seasonality_mode, 'additive')
m = Prophet(seasonality_mode='multiplicative')
self.assertEqual(m.seasonality_mode, 'multiplicative')
with self.assertRaises(ValueError):
Prophet(seasonality_mode='batman')
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Train the Prophet model on each time tick.
df = dataframe[['time', 'close']] \
.rename(columns={'time': 'ds', 'close': 'y'})
self.model = Prophet(interval_width=0.95, daily_seasonality=True)
self.model.fit(df)
return dataframe
def test_subdaily_holidays(self):
holidays = pd.DataFrame({
'ds': pd.to_datetime(['2017-01-02']),
'holiday': ['special_day'],
})
m = Prophet(holidays=holidays)
m.fit(DATA2)
fcst = m.predict()
self.assertEqual(sum(fcst['special_day'] == 0), 575)
def __init__(self, params: dict, transformation: str = "none"):
super().__init__(params, name="FBProphet", transformation=transformation)
# Stuff needed to make Prophet shut up during training.
self.suppress_stdout_stderr = suppress_stdout_stderr
self.fbmodel = Prophet()
try:
self.fbprophet_parameters = params["model_parameters"]["fbprophet_parameters"]
except KeyError:
self.fbprophet_parameters = None
def test_fit_with_holidays(self):
holidays = pd.DataFrame({
'ds':
pd.to_datetime(['2012-06-06', '2013-06-06']),
'holiday': ['seans-bday'] * 2,
'lower_window': [0] * 2,
'upper_window': [1] * 2,
})
model = Prophet(holidays=holidays, uncertainty_samples=0)
model.fit(DATA).predict()
def test_logistic_floor(self):
m = Prophet(growth='logistic')
N = DATA.shape[0]
history = DATA.head(N // 2).copy()
history['floor'] = 10.
history['cap'] = 80.
future = DATA.tail(N // 2).copy()
future['cap'] = 80.
future['floor'] = 10.
m.fit(history, algorithm='Newton')
self.assertTrue(m.logistic_floor)
self.assertTrue('floor' in m.history)
self.assertAlmostEqual(m.history['y_scaled'][0], 1.)
self.assertEqual(m.fit_kwargs, {'algorithm': 'Newton'})
fcst1 = m.predict(future)
m2 = Prophet(growth='logistic')
history2 = history.copy()
history2['y'] += 10.
history2['floor'] += 10.
history2['cap'] += 10.
future['cap'] += 10.
future['floor'] += 10.
m2.fit(history2, algorithm='Newton')
self.assertAlmostEqual(m2.history['y_scaled'][0], 1.)
def test_cross_validation(self):
m = Prophet()
m.fit(self.__df)
# Calculate the number of cutoff points(k)
horizon = pd.Timedelta('4 days')
period = pd.Timedelta('10 days')
initial = pd.Timedelta('115 days')
methods = [None, 'processes', 'threads', CustomParallelBackend()]
try:
from dask.distributed import Client
client = Client(processes=False) # noqa
methods.append("dask")
except ImportError:
pass
for parallel in methods:
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='10 days',
initial='115 days',
parallel=parallel)
self.assertEqual(len(np.unique(df_cv['cutoff'])), 3)
self.assertEqual(max(df_cv['ds'] - df_cv['cutoff']), horizon)
self.assertTrue(
min(df_cv['cutoff']) >= min(self.__df['ds']) + initial)
dc = df_cv['cutoff'].diff()
dc = dc[dc > pd.Timedelta(0)].min()
self.assertTrue(dc >= period)
self.assertTrue((df_cv['cutoff'] < df_cv['ds']).all())
# Each y in df_cv and self.__df with same ds should be equal
df_merged = pd.merge(df_cv, self.__df, 'left', on='ds')
self.assertAlmostEqual(
np.sum((df_merged['y_x'] - df_merged['y_y'])**2), 0.0)
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='10 days',
initial='135 days')
self.assertEqual(len(np.unique(df_cv['cutoff'])), 1)
with self.assertRaises(ValueError):
diagnostics.cross_validation(m,
horizon='10 days',
period='10 days',
initial='140 days')
# invalid alias
with self.assertRaisesRegex(ValueError, "'parallel' should be one"):
diagnostics.cross_validation(m, horizon="4 days", parallel="bad")
# no map method
with self.assertRaisesRegex(ValueError, "'parallel' should be one"):
diagnostics.cross_validation(m,
horizon="4 days",
parallel=object())
def test_flat_trend(self):
model = Prophet()
t = np.arange(11)
m = 0.5
y = model.flat_trend(t, m)
y_true = np.array([0.5] * 11)
self.assertEqual((y - y_true).sum(), 0)
t = t[8:]
y_true = y_true[8:]
y = model.flat_trend(t, m)
self.assertEqual((y - y_true).sum(), 0)
def test_cross_validation_custom_cutoffs(self):
m = Prophet()
m.fit(self.__df)
# When specify a list of cutoffs
# the cutoff dates in df_cv are those specified
df_cv1 = diagnostics.cross_validation(
m,
horizon='32 days',
period='10 days',
cutoffs=[pd.Timestamp('2012-07-31'),
pd.Timestamp('2012-08-31')])
self.assertEqual(len(df_cv1['cutoff'].unique()), 2)
def fit_predict_model(dataframe, interval_width=0.99, changepoint_range=0.8):
m = Prophet(daily_seasonality=False,
yearly_seasonality=False,
weekly_seasonality=False,
seasonality_mode='multiplicative',
interval_width=interval_width,
changepoint_range=changepoint_range)
m = m.fit(dataframe)
forecast = m.predict(dataframe)
forecast['fact'] = dataframe['y'].reset_index(drop=True)
return forecast
def _build(self, **config):
"""
build the models and initialize.
:param config: hyperparameters for the model
"""
self.set_params(**config)
self.model = Prophet(changepoint_prior_scale=self.changepoint_prior_scale,
seasonality_prior_scale=self.seasonality_prior_scale,
holidays_prior_scale=self.holidays_prior_scale,
changepoint_range=self.changepoint_range,
seasonality_mode=self.seasonality_mode)
self.model_init = True
def test_setup_dataframe(self):
m = Prophet()
N = DATA.shape[0]
history = DATA.head(N // 2).copy()
history = m.setup_dataframe(history, initialize_scales=True)
self.assertTrue('t' in history)
self.assertEqual(history['t'].min(), 0.0)
self.assertEqual(history['t'].max(), 1.0)
self.assertTrue('y_scaled' in history)
self.assertEqual(history['y_scaled'].max(), 1.0)
class MetricPredictor:
"""docstring for Predictor."""
model_name = "prophet"
model_description = "Forecasted value from Prophet model"
model = None
predicted_df = None
metric = None
def __init__(self, metric, rolling_data_window_size="10d"):
"""Initialize the Metric object."""
self.metric = Metric(metric, rolling_data_window_size)
def train(self, metric_data=None, prediction_duration=15):
"""Train the Prophet model and store the predictions in predicted_df."""
prediction_freq = "1MIN"
# convert incoming metric to Metric Object
if metric_data:
# because the rolling_data_window_size is set, this df should not bloat
self.metric += Metric(metric_data)
# Don't really need to store the model, as prophet models are not retrainable
# But storing it as an example for other models that can be retrained
self.model = Prophet(daily_seasonality=True,
weekly_seasonality=True,
yearly_seasonality=True)
_LOGGER.info("training data range: %s - %s", self.metric.start_time,
self.metric.end_time)
# _LOGGER.info("training data end time: %s", self.metric.end_time)
_LOGGER.debug("begin training")
self.model.fit(self.metric.metric_values)
future = self.model.make_future_dataframe(
periods=int(prediction_duration),
freq=prediction_freq,
include_history=False,
)
forecast = self.model.predict(future)
forecast["timestamp"] = forecast["ds"]
forecast = forecast[["timestamp", "yhat", "yhat_lower", "yhat_upper"]]
forecast = forecast.set_index("timestamp")
self.predicted_df = forecast
_LOGGER.debug(forecast)
def predict_value(self, prediction_datetime):
"""Return the predicted value of the metric for the prediction_datetime."""
nearest_index = self.predicted_df.index.get_loc(prediction_datetime,
method="nearest")
return self.predicted_df.iloc[[nearest_index]]
def fit_prophet(dtf_train, dtf_test, lst_exog=None, model=None, freq="D", conf=0.95, figsize=(15,10)):
## setup prophet
if model is None:
model = Prophet(growth="linear", changepoints=None, n_changepoints=25, seasonality_mode="multiplicative",
yearly_seasonality="auto", weekly_seasonality="auto", daily_seasonality="auto",
holidays=None, interval_width=conf)
if lst_exog != None:
for regressor in lst_exog:
model.add_regressor(regressor)
## train
model.fit(dtf_train)
## test
dtf_prophet = model.make_future_dataframe(periods=len(dtf_test)+10, freq=freq, include_history=True)
if model.growth == "logistic":
dtf_prophet["cap"] = dtf_train["cap"].unique()[0]
if lst_exog != None:
dtf_prophet = dtf_prophet.merge(dtf_train[["ds"]+lst_exog], how="left")
dtf_prophet.iloc[-len(dtf_test):][lst_exog] = dtf_test[lst_exog].values
dtf_prophet = model.predict(dtf_prophet)
dtf_train = dtf_train.merge(dtf_prophet[["ds","yhat"]], how="left").rename(
columns={'yhat':'model', 'y':'ts'}).set_index("ds")
dtf_test = dtf_test.merge(dtf_prophet[["ds","yhat","yhat_lower","yhat_upper"]], how="left").rename(
columns={'yhat':'forecast', 'y':'ts', 'yhat_lower':'lower', 'yhat_upper':'upper'}).set_index("ds")
## evaluate
dtf = dtf_train.append(dtf_test)
dtf = utils_evaluate_ts_model(dtf, conf=conf, figsize=figsize, title="Prophet")
return dtf, model
def test_cross_validation_uncertainty_disabled(self):
df = self.__df.copy()
for uncertainty in [0, False]:
m = Prophet(uncertainty_samples=uncertainty)
m.fit(df, algorithm='Newton')
df_cv = diagnostics.cross_validation(m,
horizon='4 days',
period='4 days',
initial='115 days')
expected_cols = ['ds', 'yhat', 'y', 'cutoff']
self.assertTrue(
all(col in expected_cols for col in df_cv.columns.tolist()))
df_p = diagnostics.performance_metrics(df_cv)
self.assertTrue('coverage' not in df_p.columns)
def test_cross_validation_default_value_check(self):
m = Prophet()
m.fit(self.__df)
# Default value of initial should be equal to 3 * horizon
df_cv1 = diagnostics.cross_validation(m,
horizon='32 days',
period='10 days')
df_cv2 = diagnostics.cross_validation(m,
horizon='32 days',
period='10 days',
initial='96 days')
self.assertAlmostEqual(((df_cv1['y'] - df_cv2['y'])**2).sum(), 0.0)
self.assertAlmostEqual(((df_cv1['yhat'] - df_cv2['yhat'])**2).sum(),
0.0)
def main():
path_example = "../examples"
"""example 1"""
df = read_dataframe(path_examples=path_example, data_idx=4)
model, future, forecasted = forecast(
df, periods=1096,
showflag=True) # uncertainty intervals seem way too wide
df.loc[(df['ds'] > '2010-01-01') & (df['ds'] < '2011-01-01'), 'y'] = None
model_removed = Prophet().fit(df)
fig = model_removed.plot(
model_removed.predict(future)
) # model with missing data. prediction of whole data with future.
fig.set_figheight(18)
fig.set_figwidth(9)
plt.title('prediction (model with missing data)')
plt.show()
"""example 2"""
df2 = read_dataframe(path_examples=path_example, data_idx=5)
model2, future2, forecasted2 = forecast(
df2, periods=1096,
showflag=True) # extreme outlieres in June 2015 mess up estimate.
df2.loc[(df2['ds'] > '2015-06-01') & (df2['ds'] < '2015-06-30'),
'y'] = None
model2_removed = Prophet().fit(df2) # Same approach as previous example
fig = model2_removed.plot(model2_removed.predict(future2))
fig.set_figheight(18)
fig.set_figwidth(9)
plt.title('prediction2 (model with missing data)')
plt.show()
def test_fit_predict_uncertainty_disabled(self):
N = DATA.shape[0]
train = DATA.head(N // 2)
future = DATA.tail(N // 2)
for uncertainty in [0, False]:
m = Prophet(uncertainty_samples=uncertainty)
m.fit(train)
fcst = m.predict(future)
expected_cols = [
'ds', 'trend', 'additive_terms', 'multiplicative_terms',
'weekly', 'yhat'
]
self.assertTrue(
all(col in expected_cols for col in fcst.columns.tolist()))
def test_fit_predict_constant_history(self):
N = DATA.shape[0]
train = DATA.head(N // 2).copy()
train['y'] = 20
future = pd.DataFrame({'ds': DATA['ds'].tail(N // 2)})
m = Prophet()
m.fit(train)
fcst = m.predict(future)
self.assertEqual(fcst['yhat'].values[-1], 20)
train['y'] = 0
future = pd.DataFrame({'ds': DATA['ds'].tail(N // 2)})
m = Prophet()
m.fit(train)
fcst = m.predict(future)
self.assertEqual(fcst['yhat'].values[-1], 0)
def test_fourier_series_yearly(self):
mat = Prophet.fourier_series(DATA['ds'], 365.25, 3)
# These are from the R forecast package directly.
true_values = np.array([
0.7006152, -0.7135393, -0.9998330, 0.01827656, 0.7262249, 0.6874572
])
self.assertAlmostEqual(np.sum((mat[0] - true_values)**2), 0.0)
def test_fourier_series_weekly(self):
mat = Prophet.fourier_series(DATA['ds'], 7, 3)
# These are from the R forecast package directly.
true_values = np.array([
0.7818315, 0.6234898, 0.9749279, -0.2225209, 0.4338837, -0.9009689
])
self.assertAlmostEqual(np.sum((mat[0] - true_values)**2), 0.0)
def _run_prophet(self, data: ProphetDataEntry, params: dict) -> np.ndarray:
"""
Construct and run a :class:`Prophet` model on the given
:class:`ProphetDataEntry` and return the resulting array of samples.
"""
prophet = self.init_model(Prophet(**params))
# Register dynamic features as regressors to the model
for i in range(len(data.feat_dynamic_real)):
prophet.add_regressor(feat_name(i))
prophet.fit(data.prophet_training_data)
future_df = prophet.make_future_dataframe(
periods=self.prediction_length,
freq=self.freq,
include_history=False,
)
# Add dynamic features in the prediction range
for i, feature in enumerate(data.feat_dynamic_real):
future_df[feat_name(i)] = feature[data.train_length:]
prophet_result = prophet.predictive_samples(future_df)
return prophet_result["yhat"].T
def test_fit_changepoint_not_in_history(self):
train = DATA[(DATA['ds'] < '2013-01-01') | (DATA['ds'] > '2014-01-01')]
future = pd.DataFrame({'ds': DATA['ds']})
prophet = Prophet(changepoints=['2013-06-06'])
forecaster = prophet
forecaster.fit(train)
forecaster.predict(future)
def _build(self, **config):
"""
build the model and initialize.
:param config: hyperparameters for the model
"""
changepoint_prior_scale = config.get('changepoint_prior_scale', 0.05)
seasonality_prior_scale = config.get('seasonality_prior_scale', 10.0)
holidays_prior_scale = config.get('holidays_prior_scale', 10.0)
seasonality_mode = config.get('seasonality_mode', 'additive')
changepoint_range = config.get('changepoint_range', 0.8)
self.metric = config.get('metric', self.metric)
self.model = Prophet(changepoint_prior_scale=changepoint_prior_scale,
seasonality_prior_scale=seasonality_prior_scale,
holidays_prior_scale=holidays_prior_scale,
changepoint_range=changepoint_range,
seasonality_mode=seasonality_mode)
def result_gam(pred_period=24):
# Use monthly data
df = pd.read_csv('../Data/sfopax_month.csv')
# Reformat data frame
df['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')
df['year'] = df['date'].dt.year
df['pax_count'] = df['pax_count'] / 1000000
X_train = df[df['year'] <= 2015][['date', 'pax_count']]
X_test = df[df['year'] > 2015][['date', 'pax_count']]
# Save the dataframe for return object
X_train_org = X_train
X_test_org = X_test
# Prepare date list for data frame if pred_period != 24
if pred_period > 24:
X_test_startdate = X_train['date'].max() + pd.tseries.offsets.MonthEnd(
1)
X_test_enddate = X_train['date'].max() + pd.tseries.offsets.MonthEnd(
pred_period)
X_test_date = pd.date_range(X_test_startdate, X_test_enddate, freq='m')
X_test['test'] = X_test_date
# Rename the column names for Prophet
X_train = X_train.rename(columns={'date': 'ds', 'pax_count': 'y'})
X_test = X_test.rename(columns={'date': 'ds', 'pax_count': 'y'})
# Train the model and predict
model = Prophet()
model.fit(X_train)
yhat = model.predict(X_test)
# Get RMSE
fb_rmse = mse(X_test['y'], yhat['yhat'].tolist()[:24])**0.5
# Reformat the data frame for result
yhat = yhat[['ds', 'yhat']]
yhat = yhat.rename(columns={'ds': 'date', 'yhat': 'pred'})
# Save result and return
result = {}
result['X_train'] = X_train_org
result['X_test'] = X_test_org
result['pred'] = yhat
result['rmse'] = fb_rmse
return result
def test_quickstart():
prophet = Prophet()
prophet.set_universe(['AAPL', 'XOM'])
prophet.register_data_generators(YahooCloseData(cache_path=CACHE_PATH))
prophet.set_order_generator(OrderGenerator())
backtest = prophet.run_backtest(start=datetime(2010, 1, 1),
end=datetime(2014, 11, 21))
prophet.register_portfolio_analyzers(default_analyzers)
analysis = prophet.analyze_backtest(backtest)
assert round(analysis['sharpe'], 10) == 1.0970973495
assert round(analysis['average_return'], 10) == 0.0010547843
assert round(analysis['cumulative_return'], 10) == 2.1688171559
assert round(analysis['volatility'], 10) == 0.0152622562
today = datetime(2014, 11, 10)
expected_orders = Orders(Order(symbol='AAPL', shares=100))
assert prophet.generate_orders(today) == expected_orders
def test_quickstart():
prophet = Prophet()
prophet.set_universe(['AAPL', 'XOM'])
price_generator = YahooData('Adj Close', 'prices', cache_path=CACHE_PATH)
prophet.register_data_generators(price_generator)
prophet.set_order_generator(OrderGenerator())
backtest = prophet.run_backtest(start=datetime(2010, 1, 1),
end=datetime(2014, 11, 21))
prophet.register_portfolio_analyzers(default_analyzers)
analysis = prophet.analyze_backtest(backtest)
assert round(analysis['sharpe'], 10) == 1.1083876014
assert round(analysis['average_return'], 10) == 0.0010655311
assert round(analysis['cumulative_return'], 10) == 2.2140809296
assert round(analysis['volatility'], 10) == 0.0152607097
today = datetime(2014, 11, 10)
expected_orders = Orders(Order(symbol='AAPL', shares=100))
assert prophet.generate_orders(today) == expected_orders
from prophet.analyze import default_analyzers
from bollinger import BollingerData
from eventstudy import BollingerEventStudy
from eventstudy import OrderGenerator
# Based on Homework #7 for Computational Investing
# http://wiki.quantsoftware.org/index.php?title=CompInvesti_Homework_7
# Here we use 2 symbols and a benchmark to reduce data pulled
# but you can use the full sp5002012.txt file from QSTK
# You will have to adjust the portfolio analyzers
# The homework solution's analyzers start the analysis
# when the first trade is conducted instead of the entire
# duration of the backtest.
prophet = Prophet()
symbols = ["AAPL", "XOM", "SPX"]
prophet.set_universe(symbols)
prophet.register_data_generators(YahooCloseData(),
BollingerData(),
BollingerEventStudy())
prophet.set_order_generator(OrderGenerator())
backtest = prophet.run_backtest(start=dt.datetime(2008, 1, 1),
end=dt.datetime(2009, 12, 31), lookback=20)
prophet.register_portfolio_analyzers(default_analyzers)
analysis = prophet.analyze_backtest(backtest)
print analysis
# +----------------------------------------+
# | sharpe | -0.851247401074 |
class OrderGenerator(object):
def __init__(self):
super(OrderGenerator, self).__init__()
self._data = dict()
def run(self, prices, timestamp, cash, **kwargs):
symbol = "AAPL"
orders = Orders()
if (prices.loc[timestamp, symbol] * 100) < cash:
orders.add_order(symbol, 100)
return orders
prophet = Prophet()
prophet.set_universe(['AAPL', 'XOM'])
prophet.register_data_generators(YahooCloseData())
prophet.set_order_generator(OrderGenerator())
backtest = prophet.run_backtest(start=datetime(2010, 1, 1))
prophet.register_portfolio_analyzers(default_analyzers)
analysis = prophet.analyze_backtest(backtest)
print analysis
# +--------------------------------------+
# | sharpe | 1.09754359611 |
# | average_return | 0.00105478425027 |
# | cumulative_return | 2.168833 |
# | volatility | 0.0152560508189 |
# +--------------------------------------+