def _add_pnl_and_performance_contribution_tables(
self, ticker_to_pnl: Dict[Ticker, PricesSeries]):
# For each ticker compute the PnL for each period (each year, month etc)
pnl_df = QFDataFrame.from_dict(ticker_to_pnl)
agg_performance = pnl_df.groupby(pd.Grouper(key=pnl_df.index.name, freq=self._frequency.to_pandas_freq())) \
.apply(lambda s: s.iloc[-1] - s.iloc[0])
# Format the column labels, so that they point exactly to the considered time frame
column_labels_format = {
Frequency.YEARLY: "%Y",
Frequency.MONTHLY: "%b %Y",
}
columns_format = column_labels_format[self._frequency]
performance_df = agg_performance.rename(
index=lambda timestamp: timestamp.strftime(columns_format))
# Transpose the original data frame, so that performance for each period is presented in a separate column
performance_df = performance_df.transpose()
performance_df.index = performance_df.index.set_names("Asset")
performance_df = performance_df.reset_index()
performance_df["Asset"] = performance_df["Asset"].apply(
lambda t: t.name)
performance_tables = self._create_performance_tables(
performance_df.copy())
performance_contribution_tables = self._create_performance_contribution_tables(
performance_df.copy())
# Add the text and all figures into the document
self.document.add_element(
HeadingElement(level=2, text="Profit and Loss"))
self.document.add_element(
ParagraphElement(
"The following tables provide the details on the Total profit and "
"loss for each asset (notional in currency units)."))
self.document.add_element(ParagraphElement("\n"))
for table in performance_tables:
self.document.add_element(
HeadingElement(level=3,
text="Performance between: {} - {}".format(
table.model.data.columns[1],
table.model.data.columns[-1])))
self.document.add_element(table)
self.document.add_element(ParagraphElement("\n"))
self.document.add_element(NewPageElement())
# Add performance contribution table
self.document.add_element(
HeadingElement(level=2, text="Performance contribution"))
for table in performance_contribution_tables:
self.document.add_element(
HeadingElement(
level=3,
text="Performance contribution between {} - {}".format(
table.model.data.columns[1],
table.model.data.columns[-1])))
self.document.add_element(table)
def _add_open_positions_table(self):
open_positions_dict = self._portfolio.open_positions_dict
contracts = open_positions_dict.keys()
# Return a readable name for each ticker (name property for FutureTickers and ticker for Tickers)
tickers = [
self._portfolio.contract_ticker_mapper.contract_to_ticker(
contract, strictly_to_specific_ticker=False)
for contract in contracts
]
tickers = [ticker.name for ticker in tickers]
specific_tickers = [
self._portfolio.contract_ticker_mapper.contract_to_ticker(
contract).ticker for contract in contracts
]
# Get the information whether it is a long or short position
directions = [
open_positions_dict[contract].direction() for contract in contracts
]
directions = [
"LONG" if direction == 1 else "SHORT" for direction in directions
]
# Get the total exposure and market value for each open position
total_exposures = [
"{:,.2f}".format(open_positions_dict[contract].total_exposure())
for contract in contracts
]
pnls = [
"{:,.2f}".format(open_positions_dict[contract].unrealised_pnl)
for contract in contracts
]
# Get the time of opening the positions
start_time = [
open_positions_dict[contract].start_time.date()
for contract in contracts
]
data = {
"Tickers name": tickers,
"Specific ticker": specific_tickers,
"Direction": directions,
"Total Exposure": total_exposures,
"PnL": pnls,
"Position Creation": start_time
}
table = DFTable(QFDataFrame.from_dict(data),
css_classes=['table', 'left-align'])
self.document.add_element(table)
def _add_open_positions_table(self):
open_positions_dict = self._portfolio.open_positions_dict
tickers = open_positions_dict.keys()
# Get the information whether it is a long or short position
directions = [open_positions_dict[t].direction() for t in tickers]
directions = [
"LONG" if direction == 1 else "SHORT" for direction in directions
]
# Get the total exposure and market value for each open position
total_exposures = [
"{:,.2f}".format(open_positions_dict[t].total_exposure())
for t in tickers
]
pnls = [
"{:,.2f}".format(open_positions_dict[t].unrealised_pnl)
for t in tickers
]
# Get the time of opening the positions
start_time = [
open_positions_dict[t].start_time.date() for t in tickers
]
data = {
"Tickers name": [t.name for t in tickers],
"Specific ticker": tickers,
"Direction": directions,
"Total Exposure": total_exposures,
"PnL": pnls,
"Position Creation": start_time
}
table = DFTable(QFDataFrame.from_dict(data),
css_classes=['table', 'left-align'])
self.document.add_element(table)