def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end, use_adj=True)
# Add technical indicator: 200 sma regime filter
self.ts['regime'] = \
pf.CROSSOVER(self.ts, timeperiod_fast=1, timeperiod_slow=200)
# Add technical indicator: instrument risk, i.e. annual std
self.ts['vola'] = \
pf.VOLATILITY(self.ts, lookback=20, time_frame='yearly')
# Add technical indicator: X day sma
sma = SMA(self.ts, timeperiod=self.sma)
self.ts['sma'] = sma
# Add technical indicator: X day high, and X day low
period_high = pd.Series(self.ts.close).rolling(self.period).max()
period_low = pd.Series(self.ts.close).rolling(self.period).min()
self.ts['period_high'] = period_high
self.ts['period_low'] = period_low
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def volatility_graph(rets, labels, points_to_plot=None):
def _boxplot(volas, labels):
fig = plt.figure(figsize=(12, 8))
axes = fig.add_subplot(111, ylabel='Volatility')
plt.ylim(0, 1)
plt.boxplot(volas, labels=labels)
def _volas_plot(volas, labels):
fig = plt.figure(figsize=(14,10))
axes = fig.add_subplot(111, ylabel='Volatility')
for i, vola in enumerate(volas):
axes.plot(vola, label=labels[i])
plt.legend(loc='best')
# default is to plot all points (days)
if points_to_plot is None:
points_to_plot = 0;
# get volatility for each return set
volas = []
for ret in rets:
volas.append(pf.VOLATILITY(ret[-points_to_plot:]).dropna())
# build metrics dataframe
index = []
columns = labels
data = []
# add metrics
metrics = ['avg', 'median', 'min', 'max', 'std', 'last']
for metric in metrics:
index.append(metric)
if metric == 'avg': data.append(vola.mean() for vola in volas)
elif metric == 'median': data.append(vola.median() for vola in volas)
elif metric == 'min': data.append(vola.min() for vola in volas)
elif metric == 'max': data.append(vola.max() for vola in volas)
elif metric == 'std': data.append(vola.std() for vola in volas)
elif metric == 'last': data.append(vola[-1] for vola in volas)
df = pd.DataFrame(data, columns=columns, index=index)
_boxplot(volas, labels)
_volas_plot(volas, labels)
return df
def run(self):
self.ts = pf.fetch_timeseries(self.symbol)
self.ts = pf.select_tradeperiod(self.ts, self.start, self.end)
# add regime filter
self.ts['regime'] = \
pf.CROSSOVER(self.ts,
timeperiod_fast=self.timeperiod_fast,
timeperiod_slow=self.timeperiod_slow,
band=self.percent_band)
# Add technical indicator: volatility
self.ts['vola'] = pf.VOLATILITY(self.ts)
self.ts, self.start = pf.finalize_timeseries(self.ts, self.start)
self.tlog = pf.TradeLog(self.symbol)
self.dbal = pf.DailyBal()
self._algo()
def volatility_graphs(dbals, labels, points_to_plot=None):
"""
Plot volatility graphs.
The first graph is a boxplot showing the differences between
2 or more returns. The second graph shows the volatility plotted
for 2 or more returns.
Parameters
----------
dbals : list of pd.DataFrame
A list of daily closing balances (or daily instrument closing
prices) indexed by date.
labels : list of str
A list of labels.
points_to_plot : int, optional
Define how many points (trading days) we intend to plot
(default is None, which implies plot all points or days).
Returns
-------
pf.DataFrame
Statistics comparing the `dbals`.
Examples
--------
>>> df = pf.volatility_graph([ts, dbal], ['SPY', 'Strategy'],
points_to_plot=5000)
>>> df
"""
def _boxplot(volas, labels):
"""
Plot a volatility boxplot.
"""
fig = plt.figure(figsize=(12, 8))
axes = fig.add_subplot(111, ylabel='Volatility')
plt.ylim(0, 1)
plt.boxplot(volas, labels=labels)
def _volas_plot(volas, labels):
"""
Plot volatility.
"""
fig = plt.figure(figsize=(14, 10))
axes = fig.add_subplot(111, ylabel='Volatility')
for i, vola in enumerate(volas):
axes.plot(vola, label=labels[i])
plt.legend(loc='best')
if points_to_plot is None:
points_to_plot = 0
# Get volatility for each dbal set.
volas = []
for dbal in dbals:
volas.append(pf.VOLATILITY(dbal[-points_to_plot:]).dropna())
# Build metrics dataframe.
index = []
columns = labels
data = []
# Add metrics.
metrics = ['avg', 'median', 'min', 'max', 'std', 'last']
for metric in metrics:
index.append(metric)
if metric == 'avg': data.append(vola.mean() for vola in volas)
elif metric == 'median': data.append(vola.median() for vola in volas)
elif metric == 'min': data.append(vola.min() for vola in volas)
elif metric == 'max': data.append(vola.max() for vola in volas)
elif metric == 'std': data.append(vola.std() for vola in volas)
elif metric == 'last': data.append(vola[-1] for vola in volas)
df = pd.DataFrame(data, columns=columns, index=index)
_boxplot(volas, labels)
_volas_plot(volas, labels)
return df
def _volatility(ts, ta_param, input_column):
return pf.VOLATILITY(ts, price=input_column)