def plottingPositionACF(positions,
ax,
title='Position auto correlation function'):
positions = aggregatePositons(positions, convert='raw')
if 'cash' in positions:
positions = positions.drop('cash', axis='columns')
nlags = 100
acf_mat = np.zeros((len(positions.columns), nlags + 1))
cols = positions.columns
for i, col in enumerate(cols):
acfs = acf(positions[col], nlags=nlags)
acf_mat[i, 0:len(acfs)] = acfs
acf_mean = np.nanmean(acf_mat, axis=0)
ax.plot(
acf_mean,
color='orangered',
alpha=0.5,
lw=2,
)
ax.set_title(title)
ax.set_ylabel('Auto correlation')
ax.set_xlabel('lags')
return ax
def plottingHodings(positions, ax, freq='M', title="Holdings Analysis"):
positions = aggregatePositons(positions, convert='daily')
if 'cash' in positions:
positions = positions.drop('cash', axis='columns')
df_holdings = positions.apply(lambda x: np.sum(x != 0), axis='columns')
df_holdings_by_freq = df_holdings.resample(freq).mean()
df_holdings.plot(color='steelblue', alpha=0.6, lw=0.5, ax=ax)
if freq == 'M':
freq = 'monthly'
else:
freq = 'daily'
df_holdings_by_freq.plot(color='orangered', alpha=0.5, lw=2, ax=ax)
ax.axhline(df_holdings.values.mean(),
color='steelblue',
ls='--',
lw=3,
alpha=1.0)
ax.set_xlim((positions.index[0], positions.index[-1]))
ax.legend([
'Holdings on each bar', 'Average {0} holdings'.format(freq),
'Average whole peirod {0} holdings'.format(freq)
],
loc="best")
ax.set_title(title)
ax.set_ylabel('Number of securities holdings')
ax.set_xlabel('')
return ax
def createPostionTearSheet(position, plot=True):
positions = aggregatePositons(position)
positions_weiget = calculatePosWeight(positions)
if plot:
verticalSections = 3
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axExposure = plt.subplot(gs[0, :])
axTopExposure = plt.subplot(gs[1, :], sharex=axExposure)
axHoldings = plt.subplot(gs[2, :])
plottingExposure(positions_weiget, axExposure)
plottingTopExposure(positions_weiget, axTopExposure)
plottingHodings(positions_weiget, axHoldings)
return positions
def createPostionTearSheet(position, plot=True):
positions = aggregatePositons(position)
positions_weiget = calculatePosWeight(positions)
if plot:
verticalSections = 3
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axExposure = plt.subplot(gs[0, :])
axTopExposure = plt.subplot(gs[1, :], sharex=axExposure)
axHoldings = plt.subplot(gs[2, :])
plottingExposure(positions_weiget, axExposure)
plottingTopExposure(positions_weiget, axTopExposure)
plottingHodings(positions_weiget, axHoldings)
return positions
def plottingTopExposure(positions,
ax,
top=10,
title="Top 10 securities exposure (%)"):
positions = aggregatePositons(positions, convert='daily')
y_axis_formatter = FuncFormatter(two_dec_places)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
df_mean = positions.abs().mean()
df_top = df_mean.nlargest(top)
(positions[df_top.index] * 100.).plot(ax=ax)
ax.legend(loc='upper center',
frameon=True,
bbox_to_anchor=(0.5, -0.14),
ncol=5)
ax.set_title(title)
return ax
def createTranscationTearSheet(transactions, positions, plot=True):
positions = aggregatePositons(positions)
transcations = aggregateTranscations(transactions)
if plot:
verticalSections = 1
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axTurnOver = plt.subplot(gs[0, :])
else:
axTurnOver = None
turnOverRate = plottingTurnover(transcations, positions, axTurnOver)[1]
turnOverRate.name = 'turnover_rate'
turnOverRate.index.name = 'date'
return pd.DataFrame(turnOverRate)
def createTranscationTearSheet(transactions, positions, plot=True):
positions = aggregatePositons(positions)
transcations = aggregateTranscations(transactions)
if plot:
verticalSections = 1
plt.figure(figsize=(16, 7 * verticalSections))
gs = gridspec.GridSpec(verticalSections, 3, wspace=0.5, hspace=0.5)
axTurnOver = plt.subplot(gs[0, :])
else:
axTurnOver = None
turnOverRate = plottingTurnover(transcations, positions, axTurnOver)[1]
turnOverRate.name = 'turnover_rate'
turnOverRate.index.name = 'date'
return pd.DataFrame(turnOverRate)
def plottingExposure(positions, ax, title="Total non cash exposure (%)"):
positions = aggregatePositons(positions, convert='daily')
y_axis_formatter = FuncFormatter(two_dec_places)
ax.yaxis.set_major_formatter(FuncFormatter(y_axis_formatter))
if 'cash' in positions:
positions_without_cash = positions.drop('cash', axis='columns')
else:
positions_without_cash = positions
longs = positions_without_cash[positions_without_cash > 0] \
.sum(axis=1).fillna(0) * 100
shorts = positions_without_cash[positions_without_cash < 0] \
.abs().sum(axis=1).fillna(0) * 100
df_long_short = pd.DataFrame({'long': longs, 'short': shorts})
df_long_short.plot(kind='area',
stacked=True,
color=['blue', 'green'],
linewidth=0.,
ax=ax)
ax.set_title(title)
return ax
