def exit_by_max_holding_time(
mkt: MarketData,
sig: Optional[Signal],
entries: Trades,
max_holding_time: pd.Timedelta,
exit_condition: Callable[[pd.DataFrame, pd.Series, pd.Timestamp], bool],
) -> Trades:
"""Exit trade at max holding time or satisfying condition.
Args:
mkt: Market data
sig: Signal data
entries: Tuple of entry trades.
max_holding_time: maximum holding time
exit_condition: The entry is closed most closest time which
condition is `True`.
Result:
Trades
"""
df = _concat(mkt, sig)
def _exit_by_max_holding_time(
trades: Trades,
df: pd.DataFrame,
max_holding_time: pd.Timedelta,
exit_condition: Callable[[pd.DataFrame, pd.Series, pd.Timestamp],
bool],
) -> Trades:
indices = [] # type: List[pd.Timestamp]
exits = [] # type: List[Tuple[float, int]]
for i in trades.ids:
trade = trades.get_trade(i)
if trade.sum() == 0:
continue
idx = trade.index[0]
start = max(idx, df.index[0])
end = min(idx + max_holding_time, df.index[-1])
df_exit = df.loc[start:end]
transaction = _exit_transaction(df_exit, trade, exit_condition)
indices.append(transaction.timestamp)
exits.append((transaction.amount, i))
df = pd.DataFrame(index=indices, data=exits, columns=["amount", "_id"])
return from_dataframe(df, symbol, currency_unit)
symbol = entries.symbol
currency_unit = entries.currency_unit
exits = _exit_by_max_holding_time(entries, df, max_holding_time,
exit_condition)
return concat([entries, exits])
def exit(
mkt: MarketData,
sig: Optional[Signal],
entries: Trades,
exit_condition: Callable[[pd.DataFrame, pd.Series], pd.Series],
) -> Trades:
"""Exit trade when satisfying condition.
Args:
mkt: Market data
sig: Signal data
entries: Tuple of entry trades.
max_holding_time: maximum holding time
exit_condition: The entry is closed most closest time which
condition is `True`.
Result:
Trades
"""
df = _concat(mkt, sig)
def _exit(
trades: Trades,
df: pd.DataFrame,
exit_condition: Callable[[pd.DataFrame, pd.Series], pd.Series],
) -> pd.Series:
indices = [] # type: List[pd.Timestamp]
exits = [] # type: List[Tuple[float, int]]
for i in trades.ids:
trade = trades.get_trade(i)
if trade.sum() == 0:
continue
idx = trade.index[0]
df_exit = df[idx <= df.index]
transaction = _exit_transaction(df_exit, trade, exit_condition)
indices.append(transaction.timestamp)
exits.append((transaction.amount, i))
df = pd.DataFrame(index=indices, data=exits, columns=["amount", "_id"])
return from_dataframe(df, symbol)
symbol = entries.symbol
exits = _exit(entries, df, exit_condition)
return concat([entries, exits])
def test_concat(trades, expected_ids, refresh_id):
trades1 = trades.copy()
trades2 = trades.copy()
result = module.concat([trades1, trades2], refresh_id)
# check symbol
expected = trades1.symbol
assert result.symbol == expected
# check len
expected = len(trades1) + len(trades2)
assert len(result) == expected
# check ids
expected = expected_ids
assert result.ids == expected
# check amount
data = trades.amount * 2.0
index = pd.date_range(start="2018-06-06", freq="1min", periods=len(data))
expected = pd.Series(data=data, index=index, name="amount")
pd.testing.assert_series_equal(result.amount, expected)