def chi2normal_transformation(df):
risk_factor = pd.DataFrame(chi2.cdf(df, pd.rolling_mean(df, 150)),
columns=df.columns,
index=df.index)
risk_factor = (risk_factor - pd.expanding_mean(risk_factor)) / \
pd.expanding_std(risk_factor)
return risk_factor
def json_series(request, pk):
screen = get_object_or_404(Screen,pk=pk)
what = request.GET.get('mode','comp') # choices: comp, hand
ref = request.GET.get('ref','nap') # choices: nap, bkb, mv, cm
# filters = [
# RangeRule(name = 'range', lower = -5, upper = 5),
# RollingRule(name = 'spike', count = 3, tolerance = 3, comp ='LT')
# ]
# determine resampling rule
rule = request.GET.get('rule', 'H')
# if rule is None:
# series = screen.find_series()
# rule = 'H' if series.aantal() < 10000 else 'D'
series = screen.get_series(ref,what,rule=rule)#,filters=filters)
if series is None or series.empty:
values = []
else:
values = zip(series.index, series.values)
data = {'screen%s'%screen.nr: values}
stats = request.GET.get('stats','0')
try:
stats = int(stats)
if stats:
mean = pd.expanding_mean(series)
std = pd.expanding_std(series)
a = (mean - std).dropna()
b = (mean + std).dropna()
ranges = zip(a.index.to_pydatetime(), a.values, b.values)
data.update({'stats%s'%screen.nr: ranges})
except:
pass
return HttpResponse(json.dumps(data,ignore_nan=True,default=to_millis),content_type='application/json')
def rolling_tstat(x):
emean = pd.expanding_mean(x)
estd = pd.expanding_std(x)
t = np.arange(1, len(x) + 1)
esqr = np.sqrt(t)
rtstat = (emean / estd) * esqr
return rtstat
def featurize(self, H):
X = pd.DataFrame({
'last_sh': H.shift(1).stack(),
'history_sh': pd.expanding_mean(H).shift(1).stack(),
'history_sh_vol': pd.expanding_std(H).shift(1).stack(),
'nr_days': H.notnull().cumsum().stack()
})
return X
def featurize(self, H):
X = pd.DataFrame({
'last_sh': H.shift(1).stack(),
'history_sh': pd.expanding_mean(H).shift(1).stack(),
'history_sh_vol': pd.expanding_std(H).shift(1).stack(),
'nr_days': H.notnull().cumsum().stack()
})
return X
def VaR_norm(data, alpha=0.99, n=252):
Z = stats.norm(0, 1).ppf(1 - alpha) #反概率密度函数
data['mean'] = pd.rolling_mean(data['return'], n)
data['std'] = pd.rolling_std(data['return'], n)
if math.isnan(data.tail(1).iat[0, 3]):
data['mean'] = pd.expanding_mean(data['return'])
data['std'] = pd.expanding_std(data['return'])
data['delta'] = data['mean'] + Z * data['std']
return data.tail(1).iat[0, 4]
def mavg(x,y, span=SPAN):
"compute moving average"
x, y = map(_plot_friendly, [x,y])
if _isdate(x[0]):
x = np.array([i.toordinal() for i in x])
std_err = pd.expanding_std(y, span)
y = pd.rolling_mean(y, span)
y1 = y - std_err
y2 = y + std_err
return (y, y1, y2)
def mavg(x, y, span=SPAN):
"compute moving average"
x, y = map(_plot_friendly, [x, y])
if _isdate(x[0]):
x = np.array([i.toordinal() for i in x])
std_err = pd.expanding_std(y, span)
y = pd.rolling_mean(y, span)
y1 = y - std_err
y2 = y + std_err
return (y, y1, y2)
def plotSecondMomentConvergence(self, x):
"""
Plots the convergence of the second moment E(X^2), or
more precisely, the standard deviation over MCMC iterations.
:param x: MCMC samples from distribution
:return: E(X^2) convergence plot
"""
x_in = np.transpose(x)
cumstd = pd.expanding_std(x_in, min_periods=1)
plt.plot(cumstd, label = u'E(X^2) Convergence', color='k', linewidth=1.5)
return cumstd
def sharpe(returns, rfr=0, expanding=0):
"""
returns: periodic return string
rfr: risk free rate
expanding: bool
"""
if expanding:
excess = excess_returns(returns, rfr)
return pd.expanding_mean(excess) / pd.expanding_std(returns)
else:
return excess_returns(returns, rfr).mean() / returns.std()
def sharpe(returns, rfr=0, expanding=0):
"""
returns: periodic return string
rfr: risk free rate
expanding: bool
"""
if expanding:
excess = excess_returns(returns, rfr)
return pd.expanding_mean(excess) / pd.expanding_std(returns)
else:
return excess_returns(returns, rfr).mean() / returns.std()
def std(self, window=0, rebalanced=True, from_date=None, to_date=None):
ret = None
returns = self.returns(rebalanced, from_date, to_date)
if window == 0:
ret = np.asscalar(np.std(returns))
if window > 0:
ret = pd.rolling_std(returns, window)
if window == -1:
ret = pd.expanding_std(returns)
return ret
def hurst(channel):
x = np.array(channel)
x = x - x.mean()
z = np.cumsum(x)
r = np.array((np.maximum.accumulate(z) - np.minimum.accumulate(z))[1:])
s = pd.expanding_std(x)[1:]
s[np.where(s == 0)] = 1e-12
r += 1e-12
y_axis = np.log(r / s)
x_axis = np.log(np.arange(1, len(y_axis) + 1))
x_axis = np.vstack([x_axis, np.ones(len(x_axis))]).T
m, b = np.linalg.lstsq(x_axis, y_axis)[0]
return (m)
def apply_one(x):
x -= x.mean()
z = np.cumsum(x)
r = (np.maximum.accumulate(z) - np.minimum.accumulate(z))[1:]
s = pd.expanding_std(x)[1:]
# prevent division by 0
s[np.where(s == 0)] = 1e-12
r += 1e-12
y_axis = np.log(r / s)
x_axis = np.log(np.arange(1, len(y_axis) + 1))
x_axis = np.vstack([x_axis, np.ones(len(x_axis))]).T
m, b = np.linalg.lstsq(x_axis, y_axis)[0]
return m
def apply_one(x):
x -= x.mean()
z = np.cumsum(x)
r = (np.maximum.accumulate(z) - np.minimum.accumulate(z))[1:]
s = pd.expanding_std(x)[1:]
# prevent division by 0
s[np.where(s == 0)] = 1e-12
r += 1e-12
y_axis = np.log(r / s)
x_axis = np.log(np.arange(1, len(y_axis) + 1))
x_axis = np.vstack([x_axis, np.ones(len(x_axis))]).T
m, b = np.linalg.lstsq(x_axis, y_axis)[0]
return m
def expanding_smoother(self, data, stype='rolling_mean', min_periods=None, freq=None):
"""
Perform a expanding smooting on the data for a complete help refer to http://pandas.pydata.org/pandas-docs/dev/computation.html
:param data: pandas dataframe input data
:param stype: soothing type
:param min_periods: periods
:param freq: frequence
smoothing types:
expanding_count Number of non-null observations
expanding_sum Sum of values
expanding_mean Mean of values
expanding_median Arithmetic median of values
expanding_min Minimum
expanding_max Maximum
expandingg_std Unbiased standard deviation
expanding_var Unbiased variance
expanding_skew Unbiased skewness (3rd moment)
expanding_kurt Unbiased kurtosis (4th moment)
"""
if stype == 'count':
newy = pd.expanding_count(data, min_periods=min_periods, freq=freq)
if stype == 'sum':
newy = pd.expanding_sum(data, min_periods=min_periods, freq=freq)
if stype == 'mean':
newy = pd.expanding_mean(data, min_periods=min_periods, freq=freq)
if stype == 'median':
newy = pd.expanding_median(data, min_periods=min_periods, freq=freq)
if stype == 'min':
newy = pd.expanding_min(data, min_periods=min_periods, freq=freq)
if stype == 'max':
newy = pd.expanding_max(data, min_periods=min_periods, freq=freq)
if stype == 'std':
newy = pd.expanding_std(data, min_periods=min_periods, freq=freq)
if stype == 'var':
newy = pd.expanding_var(data, min_periods=min_periods, freq=freq)
if stype == 'skew':
newy = pd.expanding_skew(data, min_periods=min_periods, freq=freq)
if stype == 'kurt':
newy = pd.expanding_kurt(data, min_periods=min_periods, freq=freq)
return newy
def equity_mm_test(data_df):
test_data = data_df[['S&P 500 Return','Cash Return']].add(1).cumprod()
rolling_period = 3
rolling_change = pd.DataFrame.pct_change(test_data,periods=rolling_period)
column_one = test_data.columns.values[0]
column_two = test_data.columns.values[1]
data_diff = rolling_change[column_one] - rolling_change[column_two]
data_diff['rolling_z'] = (data_diff - pd.expanding_mean(data_diff, min_periods=24))/ pd.expanding_std(data_diff, min_periods=24)
weights = pd.DataFrame(index=data_diff.index)
weights['bond_wght'] = data_diff['rolling_z']
weights['treasury_wght'] = data_diff['rolling_z'] * -1
weights = weights / 0.5
weights.dropna(inplace=True)
weights = weights.clip(-1, 1)
#weights['bond_wght'] = np.where(data_diff > 0, 1.0, np.where(data_diff< 0,-1.0, np.nan))
#weights['treasury_wght'] = np.where(data_diff > 0, -1.0, np.where(data_diff < 0,1.0, np.nan))
bond_wght = weights['bond_wght'].to_frame()
bond_wght.columns = ['US HY Return']
treasury_wght = weights['treasury_wght'].to_frame()
treasury_wght.columns = ['US Int. Trsy Return']
combined_wghts = pd.concat([bond_wght,treasury_wght], axis=1)
combined_wghts = combined_wghts.shift(1)
combined_wghts.dropna(inplace=True)
weighted_returns = combined_wghts * data_df[['US HY Return','US Int. Trsy Return']]
portfolio_return = weighted_returns.sum(axis=1).to_frame()
portfolio_return = portfolio_return.add(1).cumprod()
eq_mm = long_only_ew(portfolio_return, name='Equity Momentum')
return eq_mm, combined_wghts
def std_annualized(returns, scale=None, expanding=0):
scale = _resolve_periods_in_year(scale, returns)
if expanding:
return np.sqrt(scale) * pd.expanding_std(returns)
else:
return np.sqrt(scale) * returns.std()
def equity_vol_test(data_frame):
rolling_period = 1
rolling_change = pd.DataFrame.pct_change(np.log(data_frame['Equity Volatility']),periods=rolling_period)
rolling_change['rolling_z'] = (rolling_change - pd.expanding_mean(rolling_change, min_periods=24))/ pd.expanding_std(rolling_change, min_periods=24)
rolling_change['rolling_z'] = rolling_change['rolling_z'].to_frame()
weights = pd.DataFrame(index=rolling_change['rolling_z'].index)
weights['bond_wght'] = rolling_change['rolling_z'] * -1
weights['treasury_wght'] = rolling_change['rolling_z']
weights = weights / 1.5
weights.dropna(inplace=True)
weights = weights.clip(-1, 1)
bond_wght = weights['bond_wght'].to_frame()
bond_wght.columns = ['US HY Return']
treasury_wght = weights['treasury_wght'].to_frame()
treasury_wght.columns = ['US Int. Trsy Return']
combined_wghts = pd.concat([bond_wght,treasury_wght], axis=1)
combined_wghts = combined_wghts.shift(1)
combined_wghts.dropna(inplace=True)
weighted_returns = combined_wghts * data_frame[['US HY Return','US Int. Trsy Return']]
portfolio_return = weighted_returns.sum(axis=1).to_frame()
portfolio_return = portfolio_return.add(1).cumprod()
eq_vol = long_only_ew(portfolio_return, name='Equity Volatility')
return eq_vol, combined_wghts
def cum_avg(data):
data = pandas.DataFrame({'data': data})
means = pandas.expanding_mean(data)
stds = pandas.expanding_std(data)
return numpy.array([i[0] for i in means.values
]), numpy.array([i[0] for i in stds.values])
degree=3,
gamma='auto',
kernel='rbf',
max_iter=1000,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False).fit(x_train, y_train)
y_predict = reg.predict(x[split:])
df = df.assign(p_trend=pd.Series(np.zeros(len(x))).values)
df['p_trend'][split:] = y_predict
accuracy = scorer.accuracy_score(df['Signal'][split:], df['p_trend'][split:])
df = df.assign(ret=pd.Series(np.zeros(len(x))).values)
df['ret'] = np.log(df['Open'].shift(-1) / df['Open'])
df = df.assign(ret1=pd.Series(np.zeros(len(x))).values)
df['ret1'] = df['p_trend'] * df['ret']
df = df.assign(cu_ret1=pd.Series(np.zeros(len(x))).values)
df['cu_ret1'] = np.cumsum(df['ret1'][split:])
df = df.assign(cu_ret=pd.Series(np.zeros(len(x))).values)
df['cu_ret'] = np.cumsum(df['ret'][split:])
std = pd.expanding_std(df['cu_ret1'])
sharpe = (df['cu_ret1'] - df['cu_ret']) / std
sharpe = sharpe[split:].mean()
print("\n\n ACCURACY :", accuracy)
plt.plot(df['cu_ret1'], color='b', label='Strategy Returns')
plt.plot(df['cu_ret'], color='g', label='Market Returns')
plt.figtext(0.14, 0.7, s='Sharpe ratio: %.2f' % sharpe)
plt.legend(loc='best')
plt.show()
def build_model():
# Load SCDB CSV data.
scdb_case_data = pandas.DataFrame.from_csv('data/SCDB_2013_01_caseCentered_Citation.csv')
scdb_justice_data = pandas.DataFrame.from_csv('data/SCDB_2013_01_justiceCentered_Citation.csv')
# Apply date transforms to the data.
scdb_case_data['dateDecision'] = scdb_case_data['dateDecision'].apply(get_date)
scdb_justice_data['dateDecision'] = scdb_justice_data['dateDecision'].apply(get_date)
scdb_case_data['dateArgument'] = scdb_case_data['dateArgument'].apply(get_date)
scdb_justice_data['dateArgument'] = scdb_justice_data['dateArgument'].apply(get_date)
scdb_case_data['dateRearg'] = scdb_case_data['dateRearg'].apply(get_date)
scdb_justice_data['dateRearg'] = scdb_justice_data['dateRearg'].apply(get_date)
scdb_case_data['monthDecision'] = scdb_case_data['dateDecision'].apply(get_month)
scdb_justice_data['monthDecision'] = scdb_justice_data['dateDecision'].apply(get_month)
scdb_case_data['monthArgument'] = scdb_case_data['dateArgument'].apply(get_month)
scdb_justice_data['monthArgument'] = scdb_justice_data['dateArgument'].apply(get_month)
# Apply other basic transforms to the data.
# Set unspecified decision directions to the middle of the range, 1.5
scdb_case_data.loc[scdb_case_data['decisionDirection'] == 3, 'decisionDirection'] = 1.5
scdb_justice_data.loc[scdb_justice_data['decisionDirection'] == 3, 'decisionDirection'] = 1.5
# Map case origin and source to the Circuit within which it belongs.
scdb_case_data['caseOrigin_circuit'] = scdb_case_data['caseOrigin'].apply(map_circuit)
scdb_justice_data['caseOrigin_circuit'] = scdb_justice_data['caseOrigin'].apply(map_circuit)
scdb_case_data['caseSource_circuit'] = scdb_case_data['caseSource'].apply(map_circuit)
scdb_justice_data['caseSource_circuit'] = scdb_justice_data['caseSource'].apply(map_circuit)
# Map party type (e.g., petitioner or respondent) to our mapping table in constants.
scdb_case_data['petitioner_dk'] = scdb_case_data['petitioner'].apply(map_party)
scdb_case_data['respondent_dk'] = scdb_case_data['respondent'].apply(map_party)
scdb_justice_data['petitioner_dk'] = scdb_justice_data['petitioner'].apply(map_party)
scdb_justice_data['respondent_dk'] = scdb_justice_data['respondent'].apply(map_party)
# Generate the overturn variable by comparing Supreme Court direction with lower court direction.
scdb_case_data['decisionOverturn'] = numpy.abs(numpy.sign(scdb_case_data['decisionDirection'] - scdb_case_data['lcDispositionDirection']))
scdb_justice_data['decisionOverturn'] = numpy.abs(numpy.sign(scdb_justice_data['direction'] - scdb_justice_data['lcDispositionDirection']))
# Handle the agreement field, i.e., does this particular Justice's vote match the Court's direction.
scdb_justice_data['agree'] = (scdb_justice_data['direction'] == scdb_justice_data['decisionDirection'])
# Map Justice data, some of which comes from the justice_list.csv file in data/
scdb_justice_data['gender'] = scdb_justice_data['justice'].apply(get_gender)
scdb_justice_data['year_of_birth'] = scdb_justice_data['justice'].apply(get_year_of_birth)
scdb_justice_data['party_president'] = scdb_justice_data['justice'].apply(get_party_president)
scdb_justice_data['segal_cover'] = scdb_justice_data['justice'].apply(get_segal_cover)
scdb_justice_data['is_chief'] = [int(x.endswith(y)) for x, y, in zip(scdb_justice_data['justiceName'].tolist(), scdb_justice_data['chief'].tolist())]
# Sort cases by decision date and set into case list
docket_list = scdb_case_data.sort('dateDecision')['docketId'].tolist()
# Clean up unspecifiable/direction=3 values by setting them to the middle of the range."
scdb_case_data.loc[scdb_case_data['lcDispositionDirection'] == 3, 'lcDispositionDirection'] = 1.5
scdb_justice_data.loc[scdb_justice_data['lcDispositionDirection'] == 3, 'lcDispositionDirection'] = 1.5
scdb_case_data.loc[scdb_case_data['decisionDirection'] == 3, 'decisionDirection'] = 1.5
scdb_justice_data.loc[scdb_justice_data['decisionDirection'] == 3, 'decisionDirection'] = 1.5
# Set minimum record count prior to training and max records to predict.
min_record_count = 100
max_record_count = 99999
# Setup total feature and target data
feature_data = pandas.DataFrame()
target_data = pandas.DataFrame()
# Setup the model
model = None
bad_feature_labels = ['docket', 'outcome', 'docket_outcome', 'case_outcome', 'disposition_outcome', 'direction']
feature_labels = []
feature_weights = []
# Outcome data
outcome_data = pandas.DataFrame()
case_outcome_data = pandas.DataFrame()
# Track the less likely label
min_label = 1.0
# Iterate over all dockets
num_dockets = 0
for docket_id in docket_list:
# Increment dockets seen
num_dockets += 1
if max_record_count != None and num_dockets > max_record_count:
break
# Get rows of feature and target data
feature_rows, target_rows = get_ml_row(docket_id, scdb_case_data, scdb_justice_data)
# Now append to the feature and target lists
feature_data = feature_data.append(feature_rows.copy())
target_data = target_data.append(target_rows.copy())
# Now re-calculate all the z-scaled values
feature_data['justice_direction_mean_z'] = (feature_data['justice_direction_mean'] - pandas.expanding_mean(
feature_data['justice_direction_mean'])) / pandas.expanding_std(feature_data['justice_direction_mean'])
feature_data['diff_justice_lc_direction_abs_z'] = (feature_data[
'diff_justice_lc_direction_abs'] - pandas.expanding_mean(
feature_data['diff_justice_lc_direction_abs'])) / pandas.expanding_std(
feature_data['diff_justice_lc_direction_abs'])
feature_data['diff_justice_lc_direction_z'] = (feature_data['diff_justice_lc_direction'] - pandas.expanding_mean(
feature_data['diff_justice_lc_direction'])) / pandas.expanding_std(feature_data['diff_justice_lc_direction'])
feature_data['diff_court_lc_direction_abs_z'] = (
feature_data[
'diff_court_lc_direction_abs'] - pandas.expanding_mean(
feature_data[
'diff_court_lc_direction_abs'])) / pandas.expanding_std(
feature_data['diff_court_lc_direction_abs'])
feature_data['justice_direction_issue_mean_z'] = (feature_data[
'justice_direction_issue_mean'] - pandas.expanding_mean(
feature_data['justice_direction_issue_mean'])) / pandas.expanding_std(
feature_data['justice_direction_issue_mean'])
feature_data['current_court_direction_issue_mean_z'] = (feature_data[
'current_court_direction_issue_mean'] - pandas.expanding_mean(
feature_data['current_court_direction_issue_mean'])) / pandas.expanding_std(
feature_data['current_court_direction_issue_mean'])
feature_data = feature_data.replace(-numpy.inf, -98)
feature_data = feature_data.replace(numpy.inf, -98)
feature_data = feature_data.fillna(-99)
# Update any missing columns in E-block
feature_rows = feature_data.ix[feature_data['docket'] == docket_id].sort('justice').copy()
target_rows = feature_rows['outcome']
# Check to see if we've trained a model yet.
if model != None:
# If so, let's test it.
docket_outcome_data = feature_rows.copy()
docket_outcome_data['prediction'] = model.predict(feature_rows[feature_labels])
docket_outcome_data['target'] = target_rows.copy()
# Get the vote of the court aggregated
vote_mean_outcome = docket_outcome_data['prediction'].value_counts().idxmax()
docket_outcome_data['docket_vote_mean'] = vote_mean_outcome
docket_outcome_data['docket_vote_sum'] = docket_outcome_data['prediction'].sum()
# Append data to the case outcome data frame
case_record = scdb_case_data.ix[scdb_case_data['docketId'] == docket_id]
case_outcome_record = docket_outcome_data.ix[0][
['docket', 'docket_outcome', 'docket_vote_mean', 'docket_vote_sum']]
case_outcome_record['docket_outcome'] = int(
(case_record['lcDispositionDirection'] == case_record['decisionDirection']).tolist().pop())
case_outcome_data = case_outcome_data.append(case_outcome_record)
# Append feature weights
feature_weights.append(copy.deepcopy(model.best_estimator_.steps[-1][1].feature_importances_.tolist()))
# Aggregate all data
outcome_data = outcome_data.append(copy.deepcopy(docket_outcome_data))
if num_dockets % 100 == 0:
# Output the rolling confusion matrix every few ticks
print(sklearn.metrics.classification_report(outcome_data['target'].tolist(),
outcome_data['prediction'].tolist()))
print(sklearn.metrics.accuracy_score(outcome_data['target'].tolist(),
outcome_data['prediction'].tolist()))
# Relabel indices for feature and target data
record_count = int(feature_data.shape[0])
# Ensure that we have enough records
if record_count < min_record_count:
continue
# If we have at least that many records, let's actually train a model.
feature_data.index = range(record_count)
target_data.index = range(record_count)
# Subset feature labels to exclude our indices
if num_dockets > min_record_count and model == None:
# Set the excluded feature labels
feature_labels = [label for label in feature_data.columns.tolist() if label not in bad_feature_labels]
# Train the model on the data
model = train_model(feature_data[feature_labels], target_data[0].apply(int).tolist(),
search_parameters)
elif num_dockets > min_record_count and num_dockets % 100 == 0:
print((docket_id, num_dockets))
# Train the model on the data
model = train_model(feature_data[feature_labels], target_data[0].apply(int).tolist(), search_parameters)
# Output the feature weight data
feature_weight_df = pandas.DataFrame(feature_weights, columns=feature_labels)
# Track the case assessment
case_assessment = []
# Try to calculate case outcomes accurately.
for case_id, case_data in outcome_data.groupby('docket'):
# Get the vote data
vote_data = (case_data[['docket', 'justice', 'is_chief', 'justice_direction_mean', 'prediction', 'target']].sort('justice_direction_mean'))
overturn_predicted = vote_data['prediction'].mean()
overturn_actual = vote_data['target'].mean()
row = [
case_id,
get_year_from_docket(case_id),
case_data['issue'].tail(1).tolist().pop(),
case_data['issue_area'].tail(1).tolist().pop(),
case_data['case_source_circuit'].tail(1).tolist().pop(),
case_data['case_origin_circuit'].tail(1).tolist().pop(),
case_data['lc_direction'].tail(1).tolist().pop(),
case_data['lc_disposition'].tail(1).tolist().pop(),
overturn_predicted,
overturn_actual,
overturn_predicted > 0.5,
overturn_actual > 0.5
]
# Get the votes aligned
[row.append(value) for value in vote_data['prediction']]
[row.append(value) for value in vote_data['justice_direction_mean']]
# Pad if fewer than nine justices voting
if vote_data['prediction'].shape[0] < 9:
for i in range((9 - vote_data['prediction'].shape[0])):
row.append(numpy.nan)
row.append(vote_data.ix[vote_data['is_chief'] == 1]['prediction'].tolist().pop())
# Append to the case assessment dataframe.
case_assessment.append(row)
# Setup the column list and final case assessment DF
column_list = [
'docket', 'year', 'issue', 'issue_area', 'case_source_circuit',
'case_origin_circuit', 'lc_direction', 'lc_disposition',
'overturn_count_predict', 'overturn_count_actual', 'overturn_predict',
'overturn_actual', 'justice_1', 'justice_2', 'justice_3', 'justice_4',
'justice_5', 'justice_6', 'justice_7', 'justice_8', 'justice_9',
'justice_1_dir', 'justice_2_dir', 'justice_3_dir', 'justice_4_dir',
'justice_5_dir', 'justice_6_dir', 'justice_7_dir', 'justice_8_dir',
'justice_9_dir', 'justice_chief'
]
case_assessment_df = pandas.DataFrame(case_assessment, columns=column_list)
case_assessment_df['correct'] = (case_assessment_df['overturn_predict'] == case_assessment_df['overturn_actual'])
outcome_data['correct'] = (outcome_data['prediction'] == outcome_data['target'])
# Get the annual accuracy figures
outcome_data['year'] = outcome_data['docket'].apply(get_year_from_docket)
case_assessment_df['year'] = case_assessment_df['docket'].apply(get_year_from_docket)
x_case_assessment_df = case_assessment_df.ix[case_assessment_df['year'] >= 1946]
print "Case Assessment"
print pandas.DataFrame(sklearn.metrics.confusion_matrix(x_case_assessment_df['overturn_actual'].tolist(), x_case_assessment_df['overturn_predict'].tolist()))
print sklearn.metrics.classification_report(x_case_assessment_df['overturn_actual'].tolist(), x_case_assessment_df['overturn_predict'].tolist())
print sklearn.metrics.accuracy_score(x_case_assessment_df['overturn_actual'].tolist(), x_case_assessment_df['overturn_predict'].tolist())
print "Justice Assessment"
x_outcome_data = outcome_data.loc[outcome_data['year'] >= 1946]
print pandas.DataFrame(sklearn.metrics.confusion_matrix(x_outcome_data['target'].tolist(), x_outcome_data['prediction'].tolist()))
print sklearn.metrics.classification_report(x_outcome_data['target'].tolist(), x_outcome_data['prediction'].tolist())
print sklearn.metrics.accuracy_score(x_outcome_data['target'].tolist(), x_outcome_data['prediction'].tolist())
# Setup vars
output_folder = 'model_output'
timestamp_suffix = time.strftime("%Y%m%d%H%M%S")
# Create path
run_output_folder = os.path.join(output_folder, timestamp_suffix)
os.makedirs(run_output_folder)
# Output data
outcome_data.to_csv(os.path.join(run_output_folder, 'justice_outcome_data.csv'))
case_assessment_df.to_csv(os.path.join(run_output_folder, 'case_outcome_data.csv'))
feature_weight_df.to_csv(os.path.join(run_output_folder, 'feature_weights.csv'))
# Make a ZIP
os.system('zip -9 {0}.zip {1}'.format(os.path.join(output_folder, timestamp_suffix), os.path.join(run_output_folder, '*.csv')))
def comput_idicators(df,
trading_days,
required,
save_file,
save_address,
whole=1):
# TODO:net_value has some problem.
# columns needed
col = ['index_price', 'Interest_rate', 'nav', 'rebalancing', 'stoploss']
df_valid = df.ix[:, col]
start_balance = df.index[df['rebalancing'] == 1][0]
df_valid = df_valid[df_valid.index >= start_balance]
# daily return
df_valid['return'] = np.log(df['nav']) - np.log(df['nav'].shift(1))
# benchmark_net_value
df_valid[
'benchmark'] = df_valid['index_price'] / df_valid['index_price'].ix[0]
# benchmark_return
df_valid['benchmark_return'] = (df_valid['benchmark']-
df_valid['benchmark'].shift(1))/\
df_valid['benchmark'].shift(1)
# Annualized return
df_valid['Annu_return'] = pd.expanding_mean(
df_valid['return']) * trading_days
# Volatility
df_valid.loc[:, 'algo_volatility'] = pd.expanding_std(
df_valid['return']) * np.sqrt(trading_days)
df_valid.loc[:, 'xret'] = df_valid[
'return'] - df_valid['Interest_rate'] / trading_days / 100
df_valid.loc[:, 'ex_return'] = df_valid['return'] - df_valid[
'benchmark_return']
def ratio(x):
return np.nanmean(x) / np.nanstd(x)
# sharpe ratio
df_valid.loc[:, 'sharpe'] = pd.expanding_apply(df_valid['xret'], ratio)\
* np.sqrt(trading_days)
# information ratio
df_valid.loc[:, 'IR'] = pd.expanding_apply(df_valid['ex_return'], ratio)\
* np.sqrt(trading_days)
# Sortino ratio
def modify_ratio(x, re):
re /= trading_days
ret = np.nanmean(x) - re
st_d = np.nansum(np.square(x[x < re] - re)) / x[x < re].size
return ret / np.sqrt(st_d)
df_valid.loc[:, 'sortino'] = pd.expanding_apply(
df_valid['return'], modify_ratio,
args=(required, )) * np.sqrt(trading_days)
# Transfer infs to NA
df_valid.loc[np.isinf(df_valid.loc[:, 'sharpe']), 'sharpe'] = np.nan
df_valid.loc[np.isinf(df_valid.loc[:, 'IR']), 'IR'] = np.nan
# hit_rate
wins = np.where(df_valid['return'] >= df_valid['benchmark_return'], 1.0,
0.0)
df_valid.loc[:, 'hit_rate'] = wins.cumsum() / pd.expanding_apply(wins, len)
# 95% VaR
df_valid['VaR'] = -pd.expanding_quantile(df_valid['return'], 0.05)*\
np.sqrt(trading_days)
# 95% CVaR
df_valid['CVaR'] = -pd.expanding_apply(df_valid['return'],
lambda x: x[x < np.nanpercentile(x, 5)].mean())\
* np.sqrt(trading_days)
if whole == 1:
# max_drawdown
def exp_diff(x, type):
if type == 'dollar':
xret = pd.expanding_apply(x, lambda xx: (xx[-1] - xx.max()))
else:
xret = pd.expanding_apply(
x, lambda xx: (xx[-1] - xx.max()) / xx.max())
return xret
# dollar
# xret = exp_diff(df_valid['cum_profit'],'dollar')
# df_valid['max_drawdown_profit'] = abs(pd.expanding_min(xret))
# percentage
xret = exp_diff(df_valid['nav'], 'percentage')
df_valid['max_drawdown_ret'] = abs(pd.expanding_min(xret))
# max_drawdown_duration:
# drawdown_enddate is the first time for restoring the max
def drawdown_end(x, type):
xret = exp_diff(x, type)
minloc = xret[xret == xret.min()].index[0]
x_sub = xret[xret.index > minloc]
# if never recovering,then return nan
try:
return x_sub[x_sub == 0].index[0]
except:
return np.nan
def drawdown_start(x, type):
xret = exp_diff(x, type)
minloc = xret[xret == xret.min()].index[0]
x_sub = xret[xret.index < minloc]
try:
return x_sub[x_sub == 0].index[-1]
except:
return np.nan
df_valid['max_drawdown_start'] = pd.Series()
df_valid['max_drawdown_end'] = pd.Series()
df_valid['max_drawdown_start'].ix[-1] = drawdown_start(
df_valid['nav'], 'percentage')
df_valid['max_drawdown_end'].ix[-1] = drawdown_end(
df_valid['nav'], 'percentage')
df_valid.to_csv(save_address)
# =====result visualization=====
plt.figure(1)
if whole == 1:
plt.subplot(224)
plt.plot(df_valid['nav'], label='strategy')
plt.plot(df_valid['benchmark'], label='S&P500')
plt.xlabel('Date')
plt.legend(loc=0, shadow=True)
plt.ylabel('Nav')
plt.title('Nav of ' + save_file + ' & SP500')
# plt.subplot(223)
# plt.plot(df_valid['cum_profit'],label = 'strategy')
# plt.xlabel('Date')
# plt.ylabel('Cum_profit')
# plt.title('Cum_profit of ' + save_file)
plt.subplot(221)
plt.plot(df_valid['return'], label='strategy')
plt.xlabel('Date')
plt.ylabel('Daily_return')
plt.title('Daily Return of ' + save_file)
plt.subplot(222)
x_return = df_valid[df_valid['return'].notna()].loc[:, 'return']
y_return = df_valid[
df_valid['benchmark_return'].notna()].loc[:, 'benchmark_return']
mu = x_return.mean()
sigma = x_return.std()
mybins = np.linspace(mu - 3 * sigma, mu + 3 * sigma, 100)
count_x, _, _ = plt.hist(x_return,
mybins,
normed=1,
alpha=0.5,
label='strategy')
count_y, _, _ = plt.hist(y_return,
mybins,
normed=1,
alpha=0.5,
label='S&P500')
plt.ylabel('density')
plt.xlabel('daily_return')
plt.title('Histogram of Daily Return for ' + save_file + ' & SP500')
plt.grid(True)
# add normal distribution line
y = mlab.normpdf(mybins, mu, sigma)
plt.plot(mybins, y, 'r--', linewidth=1, label='Normal of strategy')
plt.legend(loc=0, shadow=True)
# plt.tight_layout()
plt.show()
return df_valid
def get_context_data(self, **kwargs):
context = super(WellChartView, self).get_context_data(**kwargs)
well = Well.objects.get(pk=context['pk'])
name = unicode(well)
options = {
'rangeSelector': {
'enabled': True,
'inputEnabled': True,
},
'navigator': {
'adaptToUpdatedData': True,
'enabled': True
},
'chart': {
'type': 'arearange',
'zoomType': 'x'
},
'title': {
'text': name
},
'xAxis': {
'type': 'datetime'
},
'yAxis': [{
'title': {
'text': 'm tov NAP'
}
}],
'tooltip': {
'valueSuffix': ' m',
'valueDecimals': 2,
'shared': True,
},
'legend': {
'enabled': True
},
'plotOptions': {
'line': {
'marker': {
'enabled': False
}
}
},
'credits': {
'enabled': True,
'text': 'acaciawater.com',
'href': 'http://www.acaciawater.com',
},
}
series = []
xydata = []
for screen in well.screen_set.all():
name = unicode(screen)
data = screen.to_pandas(ref='nap')
xydata = zip(data.index.to_pydatetime(), data.values)
series.append({
'name': name,
'type': 'line',
'data': xydata,
'zIndex': 1,
})
mean = pd.expanding_mean(data)
# series.append({'name': 'gemiddelde',
# 'type': 'line',
# 'data': zip(mean.index.to_pydatetime(), mean.values),
# 'linkedTo' : ':previous',
# })
std = pd.expanding_std(data)
a = (mean - std).dropna()
b = (mean + std).dropna()
ranges = zip(a.index.to_pydatetime(), a.values, b.values)
series.append({
'name': 'spreiding',
'data': ranges,
'type': 'arearange',
'lineWidth': 0,
'fillOpacity': 0.2,
'linkedTo': ':previous',
'zIndex': 0,
})
if len(xydata) > 0:
mv = []
for i in range(len(xydata)):
mv.append((xydata[i][0], screen.well.maaiveld))
series.append({'name': 'maaiveld', 'type': 'line', 'data': mv})
options['series'] = series
context['options'] = json.dumps(
options, default=lambda x: int(time.mktime(x.timetuple()) * 1000))
context['object'] = well
return context
def return_reversal(data_df):
data_df[['HY Tot Index','US Trs Tot Index']] = data_df[['US HY Return','US Int. Trsy Return']].add(1).cumprod()
data_df[['HY Rolling Return','Rolling Return']] = pd.DataFrame.pct_change(data_df[['HY Tot Index','US Trs Tot Index']],periods=36)
data_df['diff'] = data_df['HY Rolling Return'] - data_df['Rolling Return']
data_df['Return Z'] = (data_df['diff']- pd.expanding_mean(data_df['diff'], min_periods=24))/ pd.expanding_std(data_df['diff'], min_periods=24)
data_df['Return Z'].dropna(inplace=True)
data_df['Return Z'].plot()
hp_months_cheap = 24
hp_months_rich = 24
counter = 0
date_array = data_df.index.values
signal = []
signal_abs_threshold_cheap= 2
signal_abs_threshold_rich= 2
for date in range(0,len(date_array)):
if len(data_df['Return Z']) - 1 >= counter:
score = data_df['Return Z'].ix[counter]
if score > signal_abs_threshold_cheap:
counter = counter+hp_months_cheap
temp_list = [-1] * hp_months_cheap
signal.extend(temp_list)
elif score < (signal_abs_threshold_rich * -1):
counter = counter+hp_months_rich
temp_list = [1] * hp_months_rich
signal.extend(temp_list)
elif (score <= signal_abs_threshold_cheap) and score >= (signal_abs_threshold_rich * -1):
counter = counter + 1
signal.extend([0])
signal = signal[:len(date_array)]
weights = pd.DataFrame(signal,index=data_df['Return Z'].index,columns=['US HY Return'])
weights['bond_wght'] = weights
weights['treasury_wght'] = weights['US HY Return'] * -1
bond_wght = weights['bond_wght'].to_frame()
bond_wght.columns = ['US HY Return']
treasury_wght = weights['treasury_wght'].to_frame()
treasury_wght.columns = ['US Int. Trsy Return']
combined_wghts = pd.concat([bond_wght,treasury_wght], axis=1)
combined_wghts = combined_wghts.shift(1)
combined_wghts.dropna(inplace=True)
weighted_returns = combined_wghts * data_df[['US HY Return','US Int. Trsy Return']]
portfolio_return = weighted_returns.sum(axis=1).to_frame()
portfolio_return = portfolio_return.add(1).cumprod()
return_relative_test = long_only_ew(portfolio_return, name='Reversal')
return return_relative_test, combined_wghts
# -*- coding: utf-8 -*-
"""
Created on 05/10/15
@author: Carlos Eduardo Barbosa
Test convergence of Lick errors as a function of the number of simulation.
"""
import os
import numpy as np
from pandas import expanding_std
import matplotlib.pyplot as plt
from config import *
if __name__ == "__main__":
os.chdir(os.path.join(home, "single2/mc_logs"))
cols = np.array([12, 13, 16, 17, 18, 19, 20])
logs = os.listdir(".")
fig = plt.figure(1, figsize=(5, 15))
for log in logs:
data = np.loadtxt(log).T[cols]
for i, d in enumerate(data):
ax = plt.subplot(7, 1, i + 1)
ax.plot(expanding_std(d, min_periods=1) / d.std(), "-k")
plt.pause(1)
plt.show(block=False)
reg = SVC(C=1,cache_size=200,class_weight=None,coef0=0,decision_function_shape=None,degree=3,gamma='auto',kernel='rbf',max_iter=1000,probability=False,random_state=None,shrinking=True,tol=0.001,verbose=False)
reg.fit(X[:split],y[:split])
y_predict = reg.predict(X[split:])
Df = Df.assign(P_Trend=pd.Series(np.zeros(len(X))).values)
Df['P_Trend'][split:] = y_predict
accuracy = scorer.accuracy_score(Df['Signal'][split:],Df['P_Trend'][split:])
Df = Df.assign(Ret=pd.Series(np.zeros(len(X))).values)
Df['Ret'] = np.log(Df['Open'].shift(-1)/Df['Open'])
Df = Df.assign(Ret1=pd.Series(np.zeros(len(X))).values)
Df['Ret1'] = Df['P_Trend']*Df['Ret']
Df = Df.assign(Cu_Ret1=pd.Series(np.zeros(len(X))).values)
Df['Cu_Ret1'] = np.cumsum(Df['Ret1'][split:])
Df = Df.assign(Cu_Ret=pd.Series(np.zeros(len(X))).values)
Df['Cu_Ret'] = np.cumsum(Dp['Ret'][split:])
Std = pd.expanding_std(Df['Cu_Ret1'])
Sharpe = (Df['Cu_Ret1']-Df['Cu_Ret'])/Std
Sharpe = Sharpe[split:].mean()
print('\n\nAccuracy:',accuracy)
plt.plot(Df['Cu_Ret1'],color='r',label='Strategy Returns')
plt.plot(Df['Cu_Ret'],color='g',label='Market Returns')
plt.figtext(0.14,0.7,s='Sharpe ratio: %.2f'%Sharpe)
plt.legend(log='best')
# Increment dockets seen
num_dockets += 1
if max_record_count != None and num_dockets > max_record_count:
break
# Get rows of feature and target data
feature_rows, target_rows = get_ml_row(docket_id, scdb_case_data, scdb_justice_data)
# Now append to the feature and target lists
feature_data = feature_data.append(feature_rows.copy())
target_data = target_data.append(target_rows.copy())
# Now re-calculate all the z-scaled values
feature_data['justice_direction_mean_z'] = (feature_data['justice_direction_mean'] - pandas.expanding_mean(
feature_data['justice_direction_mean'])) / pandas.expanding_std(feature_data['justice_direction_mean'])
feature_data['diff_justice_lc_direction_abs_z'] = (feature_data[
'diff_justice_lc_direction_abs'] - pandas.expanding_mean(
feature_data['diff_justice_lc_direction_abs'])) / pandas.expanding_std(
feature_data['diff_justice_lc_direction_abs'])
feature_data['diff_justice_lc_direction_z'] = (feature_data['diff_justice_lc_direction'] - pandas.expanding_mean(
feature_data['diff_justice_lc_direction'])) / pandas.expanding_std(feature_data['diff_justice_lc_direction'])
feature_data['diff_court_lc_direction_abs_z'] = (
feature_data[
'diff_court_lc_direction_abs'] - pandas.expanding_mean(
feature_data[
'diff_court_lc_direction_abs'])) / pandas.expanding_std(
feature_data['diff_court_lc_direction_abs'])
feature_data['justice_direction_issue_mean_z'] = (feature_data[
'justice_direction_issue_mean'] - pandas.expanding_mean(
feature_data['justice_direction_issue_mean'])) / pandas.expanding_std(
def std_annualized(returns, scale=None, expanding=0):
scale = _resolve_periods_in_year(scale, returns)
if expanding:
return np.sqrt(scale) * pd.expanding_std(returns)
else:
return np.sqrt(scale) * returns.std()
class CumulativeRets(object):
def __init__(self, rets=None, ltd_rets=None):
if rets is None and ltd_rets is None:
raise ValueError('rets or ltd_rets must be specified')
if rets is None:
if ltd_rets.empty:
rets = ltd_rets
else:
rets = (1. + ltd_rets).pct_change()
rets.iloc[0] = ltd_rets.iloc[0]
if ltd_rets is None:
if rets.empty:
ltd_rets = rets
else:
ltd_rets = (1. + rets).cumprod() - 1.
self.rets = rets
self.ltd_rets = ltd_rets
pds_per_year = property(lambda self: periodicity(self.rets))
def asfreq(self, freq):
other_pds_per_year = periodicity(freq)
if self.pds_per_year < other_pds_per_year:
msg = 'Cannot downsample returns. Cannot convert from %s periods/year to %s'
raise ValueError(msg % (self.pds_per_year, other_pds_per_year))
if freq == 'B':
rets = (1. + self.rets).groupby(self.rets.index.date).apply(lambda s: s.prod()) - 1.
# If you do not do this, it will be an object index
rets.index = pd.DatetimeIndex([i for i in rets.index])
return CumulativeRets(rets)
else:
rets = (1. + self.rets).resample(freq, how='prod') - 1.
return CumulativeRets(rets)
# -----------------------------------------------------------
# Resampled data
dly = lazy_property(lambda self: self.asfreq('B'), 'dly')
weekly = lazy_property(lambda self: self.asfreq('W'), 'weekly')
monthly = lazy_property(lambda self: self.asfreq('M'), 'monthly')
quarterly = lazy_property(lambda self: self.asfreq('Q'), 'quarterly')
annual = lazy_property(lambda self: self.asfreq('A'), 'annual')
# -----------------------------------------------------------
# Basic Metrics
@lazy_property
def ltd_rets_ann(self):
return (1. + self.ltd_rets) ** (self.pds_per_year / pd.expanding_count(self.rets)) - 1.
cnt = property(lambda self: self.rets.notnull().astype(int).sum())
mean = lazy_property(lambda self: self.rets.mean(), 'avg')
mean_ann = lazy_property(lambda self: self.mean * self.pds_per_year, 'avg_ann')
ltd = lazy_property(lambda self: self.ltd_rets.iloc[-1], name='ltd')
ltd_ann = lazy_property(lambda self: self.ltd_rets_ann.iloc[-1], name='ltd_ann')
std = lazy_property(lambda self: self.rets.std(), 'std')
std_ann = lazy_property(lambda self: self.std * np.sqrt(self.pds_per_year), 'std_ann')
drawdown_info = lazy_property(lambda self: drawdown_info(self.rets), 'drawdown_info')
drawdowns = lazy_property(lambda self: drawdowns(self.rets), 'drawdowns')
maxdd = lazy_property(lambda self: self.drawdown_info['maxdd'].min(), 'maxdd')
dd_avg = lazy_property(lambda self: self.drawdown_info['maxdd'].mean(), 'dd_avg')
kurtosis = lazy_property(lambda self: self.rets.kurtosis(), 'kurtosis')
skew = lazy_property(lambda self: self.rets.skew(), 'skew')
sharpe_ann = lazy_property(lambda self: np.divide(self.ltd_ann, self.std_ann), 'sharpe_ann')
downside_deviation = lazy_property(lambda self: downside_deviation(self.rets, mar=0, full=0, ann=1),
'downside_deviation')
sortino = lazy_property(lambda self: self.ltd_ann / self.downside_deviation, 'sortino')
@lazy_property
def maxdd_dt(self):
ddinfo = self.drawdown_info
if ddinfo.empty:
return None
else:
return self.drawdown_info['maxdd dt'].ix[self.drawdown_info['maxdd'].idxmin()]
# -----------------------------------------------------------
# Expanding metrics
expanding_mean = property(lambda self: pd.expanding_mean(self.rets), 'expanding_avg')
expanding_mean_ann = property(lambda self: self.expanding_mean * self.pds_per_year, 'expanding_avg_ann')
expanding_std = lazy_property(lambda self: pd.expanding_std(self.rets), 'expanding_std')
expanding_std_ann = lazy_property(lambda self: self.expanding_std * np.sqrt(self.pds_per_year), 'expanding_std_ann')
expanding_sharpe_ann = property(lambda self: np.divide(self.ltd_rets_ann, self.expanding_std_ann))
# -----------------------------------------------------------
# Rolling metrics
rolling_mean = property(lambda self: pd.rolling_mean(self.rets), 'rolling_avg')
rolling_mean_ann = property(lambda self: self.rolling_mean * self.pds_per_year, 'rolling_avg_ann')
def rolling_ltd_rets(self, n):
return pd.rolling_apply(self.rets, n, lambda s: (1. + s).prod() - 1.)
def rolling_ltd_rets_ann(self, n):
tot = self.rolling_ltd_rets(n)
return tot ** (self.pds_per_year / n)
def rolling_std(self, n):
return pd.rolling_std(self.rets, n)
def rolling_std_ann(self, n):
return self.rolling_std(n) * np.sqrt(self.pds_per_year)
def rolling_sharpe_ann(self, n):
return self.rolling_ltd_rets_ann(n) / self.rolling_std_ann(n)
def iter_by_year(self):
"""Split the return objects by year and iterate"""
for key, grp in self.rets.groupby(lambda x: x.year):
yield key, CumulativeRets(rets=grp)
def truncate(self, before=None, after=None):
rets = self.rets.truncate(before=before, after=after)
return CumulativeRets(rets=rets)
@lazy_property
def summary(self):
d = OrderedDict()
d['ltd'] = self.ltd
d['ltd ann'] = self.ltd_ann
d['mean'] = self.mean
d['mean ann'] = self.mean_ann
d['std'] = self.std
d['std ann'] = self.std_ann
d['sharpe ann'] = self.sharpe_ann
d['sortino'] = self.sortino
d['maxdd'] = self.maxdd
d['maxdd dt'] = self.maxdd_dt
d['dd avg'] = self.dd_avg
d['cnt'] = self.cnt
return pd.Series(d, name=self.rets.index.freq or guess_freq(self.rets.index))
def _repr_html_(self):
from tia.util.fmt import new_dynamic_formatter
fmt = new_dynamic_formatter(method='row', precision=2, pcts=1, trunc_dot_zeros=1, parens=1)
df = self.summary.to_frame()
return fmt(df)._repr_html_()
def get_alpha_beta(self, bm_rets):
if isinstance(bm_rets, pd.Series):
bm = CumulativeRets(bm_rets)
elif isinstance(bm_rets, CumulativeRets):
bm = bm_rets
else:
raise ValueError('bm_rets must be series or CumulativeRetPerformace not %s' % (type(bm_rets)))
bm_freq = guess_freq(bm_rets)
if self.pds_per_year != bm.pds_per_year:
tgt = {'B': 'dly', 'W': 'weekly', 'M': 'monthly', 'Q': 'quarterly', 'A': 'annual'}.get(bm_freq, None)
if tgt is None:
raise ValueError('No mapping for handling benchmark with frequency: %s' % bm_freq)
tmp = getattr(self, tgt)
y = tmp.rets
y_ann = tmp.ltd_ann
else:
y = self.rets
y_ann = self.ltd_ann
x = bm.rets.truncate(y.index[0], y.index[-1])
x_ann = bm.ltd_ann
model = pd.ols(x=x, y=y)
beta = model.beta[0]
alpha = y_ann - beta * x_ann
return pd.Series({'alpha': alpha, 'beta': beta}, name=bm_freq)
def plot_ltd(self, ax=None, style='k', label='ltd', show_dd=1, title=True, legend=1):
ltd = self.ltd_rets
ax = ltd.plot(ax=ax, style=style, label=label)
if show_dd:
dd = self.drawdowns
dd.plot(style='r', label='drawdowns', alpha=.5, ax=ax)
ax.fill_between(dd.index, 0, dd.values, facecolor='red', alpha=.25)
fmt = PercentFormatter
AxesFormat().Y.percent().X.label("").apply(ax)
legend and ax.legend(loc='upper left', prop={'size': 12})
# show the actualy date and value
mdt, mdd = self.maxdd_dt, self.maxdd
bbox_props = dict(boxstyle="round", fc="w", ec="0.5", alpha=0.25)
try:
dtstr = '{0}'.format(mdt.to_period())
except:
# assume daily
dtstr = '{0}'.format(hasattr(mdt, 'date') and mdt.date() or mdt)
ax.text(mdt, dd[mdt], "{1} \n {0}".format(fmt(mdd), dtstr).strip(), ha="center", va="top", size=8,
bbox=bbox_props)
if title is True:
pf = new_percent_formatter(1, parens=False, trunc_dot_zeros=True)
ff = new_float_formatter(precision=1, parens=False, trunc_dot_zeros=True)
total = pf(self.ltd_ann)
vol = pf(self.std_ann)
sh = ff(self.sharpe_ann)
mdd = pf(self.maxdd)
title = 'ret$\mathregular{_{ann}}$ %s vol$\mathregular{_{ann}}$ %s sharpe %s maxdd %s' % (
total, vol, sh, mdd)
title and ax.set_title(title, fontdict=dict(fontsize=10, fontweight='bold'))
return ax
def plot_ret_on_dollar(self, title=None, show_maxdd=1, figsize=None, ax=None, append=0, label=None, **plot_args):
plot_return_on_dollar(self.rets, title=title, show_maxdd=show_maxdd, figsize=figsize, ax=ax, append=append,
label=label, **plot_args)
def plot_hist(self, ax=None, **histplot_kwargs):
pf = new_percent_formatter(precision=1, parens=False, trunc_dot_zeros=1)
ff = new_float_formatter(precision=1, parens=False, trunc_dot_zeros=1)
ax = self.rets.hist(ax=ax, **histplot_kwargs)
AxesFormat().X.percent(1).apply(ax)
m, s, sk, ku = pf(self.mean), pf(self.std), ff(self.skew), ff(self.kurtosis)
txt = '$\mathregular{\mu}$=%s $\mathregular{\sigma}$=%s skew=%s kurt=%s' % (m, s, sk, ku)
bbox = dict(facecolor='white', alpha=0.5)
ax.text(0, 1, txt, fontdict={'fontweight': 'bold'}, bbox=bbox, ha='left', va='top', transform=ax.transAxes)
return ax
def filter(self, mask, keep_ltd=0):
if isinstance(mask, pd.Series):
mask = mask.values
rets = self.rets.ix[mask]
ltd = None
if keep_ltd:
ltd = self.ltd_rets.ix[mask]
return CumulativeRets(rets=rets, ltd_rets=ltd)
raw = requests.get("http://www.google.com/finance/getprices?i="+interval+"&p="+lookback+"d&f=c&df=cpct&q="+symbol).text
# Take the data and put it into a DataFrame
raw = raw.split()[7:]
data = pd.DataFrame(raw)
data = data.astype("float")
data["price"] = data[0]
del data[0]
# We only need 60 minutes worth of data
if len(data["price"] >= 60): data["price"] = data["price"][-60:]
# Columns for expanding mean and standard deviation
data["mean"] = pd.expanding_mean(data["price"])
data["vol"] = pd.expanding_std(data["price"])
# Linear regression on price data
x = range(len(data["price"][-60:]))
y = data["price"][-60:].values
A,B = curve_fit(f,x,y)
# Print the trend to the console
if A[0] < 0 : print("downtrend")
else: print("uptrend")
# Plot window
plt.figure(1)
# Plot for the price and its moving average
def spread_crossover(data_df,slow=1,fast=12):
spread_log = pd.DataFrame(np.log(data_df.ix[:,0] * 100))
data_df['spread_z_ma'] = (spread_log - pd.expanding_mean(spread_log, min_periods=24))/ pd.expanding_std(spread_log, min_periods=24)
data_df['spread_z_ema'] = (spread_log - pd.ewma(spread_log, min_periods = 24, halflife=12)) / pd.ewmstd(spread_log, halflife=12)
data_df['spread_z_ema'] = pd.rolling_mean(data_df['spread_z_ema'], window=3)
data_df['slow'] = pd.rolling_mean(data_df['US HY Spread'],slow)
data_df['fast'] = pd.rolling_mean(data_df['US HY Spread'],fast)
data_df['diff'] = (data_df['slow'] - data_df['fast']) * -1
data_df['diff'] = data_df['diff'] + 1
data_df['diff'] = np.log(data_df['diff'])
data_df['tren_z_ma'] = (data_df['diff'] - pd.expanding_mean(data_df['diff'], min_periods=24))/ pd.expanding_std(data_df['diff'], min_periods=24)
data_df['tren_z_ma'] = pd.rolling_mean(data_df['tren_z_ma'], window=3)
trend_valuation_df = pd.concat([data_df['spread_z_ema'],data_df['tren_z_ma']], axis=1)
trend_valuation_df.dropna(inplace=True)
trend_valuation_df.plot()
plt.show()
algo_wghts_df = pd.DataFrame()
wghts_array = []
valuation_threshold_cheap = 1
valuation_threshold_rich = -1.0
trend_threshold_tightening = 0.1
trend_threshold_widening = -0.1
data_df['spread_z_ma'].plot()
plt.show()
for score in trend_valuation_df.values:
valuation_score = score[0]
trend_score = score[1]
if (trend_score >= -0.2 and valuation_score >= -1):
wghts_array.append(min(1,abs(trend_score-valuation_score) / 1))
else:
wghts_array.append(0)
#elif trend_score <= -0.1 and valuation_score <= valuation_threshold_cheap:
# wghts_array.append(-1)
#elif valuation_score >= valuation_threshold_cheap:
# wghts_array.append(1)
#else:
# wghts_array.append(0)
wghts_df = pd.DataFrame(wghts_array, index = trend_valuation_df.index)
long = wghts_df[wghts_df == 1].count()[0] / len(trend_valuation_df)
neutral = wghts_df[wghts_df == 0].count()[0] / len(trend_valuation_df)
short = wghts_df[wghts_df == -1].count()[0] / len(trend_valuation_df)
wghts_df.columns = [data_df.columns.values[1]]
wghts_df = wghts_df.shift(1)
s1 = bt.Strategy('Valuation & Trend ', [bt.algos.WeighTarget(wghts_df),
bt.algos.Rebalance()])
return_data = data_df.ix[:,1].to_frame()
return_data.columns = [data_df.columns.values[1]]
strategy = bt.Backtest(s1, return_data)
res = bt.run(strategy)
res.plot(logy=True)
res.display()
print(long,neutral,short)
def spread_holding_test(data_df):
data_df['lg_spread'] = np.log(data_df['US HY Spread'] * 100)
data_df['spread_z_ema'] = (data_df['lg_spread'] - pd.expanding_mean(data_df['lg_spread'], min_periods=24))/ pd.expanding_std(data_df['lg_spread'], min_periods=24)
data_df['spread_z_ema'].dropna(inplace=True)
hp_months_cheap = 12
hp_months_rich = 12
counter = 0
date_array = data_df.index.values
signal = []
signal_abs_threshold_cheap= 1.5
signal_abs_threshold_rich= 1.5
for date in range(0,len(date_array)):
if len(data_df['spread_z_ema']) - 1 >= counter:
score = data_df['spread_z_ema'].ix[counter]
if score > signal_abs_threshold_cheap:
counter = counter+hp_months_cheap
temp_list = [1] * hp_months_cheap
signal.extend(temp_list)
elif score < (signal_abs_threshold_rich * -1):
counter = counter+hp_months_rich
temp_list = [-1] * hp_months_rich
signal.extend(temp_list)
elif (score <= signal_abs_threshold_cheap) and score >= (signal_abs_threshold_rich * -1):
counter = counter + 1
signal.extend([1])
signal = signal[:len(date_array)]
weights = pd.DataFrame(signal,index=data_df['spread_z_ema'].index,columns=['US HY Return'])
weights['bond_wght'] = weights
weights['treasury_wght'] = weights['US HY Return'] * -1
bond_wght = weights['bond_wght'].to_frame()
bond_wght.columns = ['US HY Return']
treasury_wght = weights['treasury_wght'].to_frame()
treasury_wght.columns = ['US Int. Trsy Return']
combined_wghts = pd.concat([bond_wght,treasury_wght], axis=1)
combined_wghts = combined_wghts.shift(1)
combined_wghts.dropna(inplace=True)
weighted_returns = combined_wghts * data_df[['US HY Return','US Int. Trsy Return']]
portfolio_return = weighted_returns.sum(axis=1).to_frame()
portfolio_return = portfolio_return.add(1).cumprod()
risk_premia = long_only_ew(portfolio_return, name='Risk Premia')
return risk_premia, combined_wghts
"""
Created on 05/10/15
@author: Carlos Eduardo Barbosa
Test convergence of Lick errors as a function of the number of simulation.
"""
import os
import numpy as np
from pandas import expanding_std
import matplotlib.pyplot as plt
from config import *
if __name__ == "__main__":
os.chdir(os.path.join(home, "single2/mc_logs"))
cols = np.array([12, 13,16,17,18,19,20])
logs = os.listdir(".")
fig = plt.figure(1, figsize=(5,15))
for log in logs:
data = np.loadtxt(log).T[cols]
for i,d in enumerate(data):
ax = plt.subplot(7,1,i+1)
ax.plot(expanding_std(d, min_periods=1) / d.std(), "-k")
plt.pause(1)
plt.show(block=False)
"&p=" + lookback + "d&f=c&df=cpct&q=" + symbol).text
# Take the data and put it into a DataFrame
raw = raw.split()[7:]
data = pd.DataFrame(raw)
data = data.astype("float")
data["price"] = data[0]
del data[0]
# We only need 60 minutes worth of data
if len(data["price"] >= 60): data["price"] = data["price"][-60:]
# Columns for expanding mean and standard deviation
data["mean"] = pd.expanding_mean(data["price"])
data["vol"] = pd.expanding_std(data["price"])
# Linear regression on price data
x = range(len(data["price"][-60:]))
y = data["price"][-60:].values
A, B = curve_fit(f, x, y)
# Print the trend to the console
if A[0] < 0: print("downtrend")
else: print("uptrend")
# Plot window
plt.figure(1)
# Plot for the price and its moving average
def get_context_data(self, **kwargs):
context = super(WellChartView, self).get_context_data(**kwargs)
well = Well.objects.get(pk=context['pk'])
name = unicode(well)
options = {
'rangeSelector': { 'enabled': True,
'inputEnabled': True,
},
'navigator': {'adaptToUpdatedData': True, 'enabled': True},
'chart': {'type': 'arearange', 'zoomType': 'x'},
'title': {'text': name},
'xAxis': {'type': 'datetime'},
'yAxis': [{'title': {'text': 'Grondwaterstand\n(m tov NAP)'}}
],
'tooltip': {'valueSuffix': ' m',
'valueDecimals': 2,
'shared': True,
},
'legend': {'enabled': True},
'plotOptions': {'line': {'marker': {'enabled': False}}},
'credits': {'enabled': True,
'text': 'acaciawater.com',
'href': 'http://www.acaciawater.com',
},
}
series = []
xydata = []
for screen in well.screen_set.all():
name = unicode(screen)
data = screen.to_pandas(ref='nap')
if data.size > 0:
xydata = zip(data.index.to_pydatetime(), data.values)
series.append({'name': name,
'type': 'line',
'data': xydata,
'lineWidth': 1,
'color': '#0066FF',
'zIndex': 2,
})
mean = pd.expanding_mean(data)
std = pd.expanding_std(data)
a = (mean - std).dropna()
b = (mean + std).dropna()
ranges = zip(a.index.to_pydatetime(), a.values, b.values)
series.append({'name': 'spreiding',
'data': ranges,
'type': 'arearange',
'lineWidth': 0,
'color': '#0066FF',
'fillOpacity': 0.2,
'linkedTo' : ':previous',
'zIndex': 0,
})
data = screen.to_pandas(ref='nap',kind='HAND')
if data.size > 0:
hand = zip(data.index.to_pydatetime(), data.values)
series.append({'name': 'handpeiling',
'type': 'scatter',
'data': hand,
'zIndex': 3,
'marker': {'symbol': 'circle', 'radius': 6, 'lineColor': 'white', 'lineWidth': 2, 'fillColor': 'red'},
})
if len(xydata)>0:
mv = []
mv.append((xydata[0][0], screen.well.maaiveld))
mv.append((xydata[-1][0], screen.well.maaiveld))
series.append({'name': 'maaiveld',
'type': 'line',
'lineWidth': 2,
'color': '#009900',
'dashStyle': 'Dash',
'data': mv,
'zIndex': 4,
})
# neerslag toevoegen
try:
closest = Station.closest(well.location)
name = 'Meteostation {} (dagwaarden)'.format(closest.naam)
neerslag = Series.objects.get(name='RH',mlocatie__name=name)
data = neerslag.to_pandas(start=xydata[0][0], stop=xydata[-1][0]) / 10.0 # 0.1 mm -> mm
data = zip(data.index.to_pydatetime(), data.values)
series.append({'name': 'Neerslag '+ closest.naam,
'type': 'column',
'data': data,
'yAxis': 1,
'pointRange': 24 * 3600 * 1000, # 1 day
'pointPadding': 0.01,
'pointPlacement': 0.5,
'zIndex': 1,
'color': 'orange',
'borderColor': '#cc6600',
})
options['yAxis'].append({'title': {'text': 'Neerslag (mm)'},
'opposite': 1,
'min': 0,
})
except:
pass
options['series'] = series
context['options'] = json.dumps(options, default=lambda x: int(time.mktime(x.timetuple())*1000))
context['object'] = well
return context
def get_context_data(self, **kwargs):
context = super(WellChartView, self).get_context_data(**kwargs)
well = Well.objects.get(pk=context['pk'])
name = unicode(well)
options = {
'rangeSelector': { 'enabled': True,
'inputEnabled': True,
},
'navigator': {'adaptToUpdatedData': True, 'enabled': True},
'chart': {'type': 'arearange', 'zoomType': 'x'},
'title': {'text': name},
'xAxis': {'type': 'datetime'},
'yAxis': [{'title': {'text': 'm tov NAP'}}
],
'tooltip': {'valueSuffix': ' m',
'valueDecimals': 2,
'shared': True,
},
'legend': {'enabled': True},
'plotOptions': {'line': {'marker': {'enabled': False}}},
'credits': {'enabled': True,
'text': 'acaciawater.com',
'href': 'http://www.acaciawater.com',
},
}
series = []
xydata = []
start = datetime.datetime(2013,1,1)
stop = datetime.datetime(2016,1,1)
for screen in well.screen_set.all():
name = unicode(screen)
data = screen.to_pandas(ref='nap')[start:stop]
if data.size > 0:
xydata = zip(data.index.to_pydatetime(), data.values)
series.append({'name': name,
'type': 'line',
'data': xydata,
'lineWidth': 1,
'zIndex': 1,
})
mean = pd.expanding_mean(data)
# series.append({'name': 'gemiddelde',
# 'type': 'line',
# 'data': zip(mean.index.to_pydatetime(), mean.values),
# 'linkedTo' : ':previous',
# })
std = pd.expanding_std(data)
a = (mean - std).dropna()
b = (mean + std).dropna()
ranges = zip(a.index.to_pydatetime(), a.values, b.values)
series.append({'name': 'spreiding',
'data': ranges,
'type': 'arearange',
'lineWidth': 0,
'fillOpacity': 0.2,
'linkedTo' : ':previous',
'zIndex': 0,
})
data = screen.to_pandas(ref='nap',kind='HAND')[start:stop]
if data.size > 0:
hand = zip(data.index.to_pydatetime(), data.values)
series.append({'name': 'handpeiling',
'type': 'scatter',
'data': hand,
'zIndex': 2,
'marker': {'symbol': 'circle', 'radius': 6, 'lineColor': 'white', 'lineWidth': 2, 'fillColor': 'blue'},
})
if len(xydata)>0:
mv = []
mv.append((xydata[0][0], screen.well.maaiveld))
mv.append((xydata[-1][0], screen.well.maaiveld))
series.append({'name': 'maaiveld',
'type': 'line',
'lineWidth': 1,
'dashStyle': 'Dash',
'color': 'white',
'data': mv
})
options['series'] = series
context['options'] = json.dumps(options, default=lambda x: int(time.mktime(x.timetuple())*1000))
context['object'] = well
return context