def test_daily_txns_with_bar_data(self):
daily_txn = daily_txns_with_bar_data(
self.transactions, self.market_data)
expected = DataFrame(data=[['A', 100000, 1.0, 1000000.],
['A', 100000, 1.0, 2000000.],
['A', 100000, 1.0, 3000000.]],
columns=['symbol', 'amount', 'price', 'volume'],
index=self.dates)
assert_frame_equal(daily_txn, expected, check_less_precise=True)
def test_daily_txns_with_bar_data(self):
daily_txn = daily_txns_with_bar_data(
self.transactions, self.market_data)
expected = DataFrame(data=[['A', 100000, 1.0, 1000000.],
['A', 100000, 1.0, 2000000.],
['A', 100000, 1.0, 3000000.]],
columns=['symbol', 'amount', 'price', 'volume'],
index=self.dates)
assert_frame_equal(daily_txn, expected, check_less_precise=True)
def test_apply_slippage_penalty(self, starting_base, impact,
expected_adj_returns):
returns = Series([1., 1., 1.], index=self.dates)
daily_txn = daily_txns_with_bar_data(
self.transactions, self.market_data)
adj_returns = apply_slippage_penalty(
returns, daily_txn, starting_base, 1000000, impact=impact)
expected_adj_returns = Series(expected_adj_returns, index=self.dates)
assert_series_equal(adj_returns, expected_adj_returns)
def test_apply_slippage_penalty(self, starting_base, impact,
expected_adj_returns):
returns = Series([1., 1., 1.], index=self.dates)
daily_txn = daily_txns_with_bar_data(
self.transactions, self.market_data)
adj_returns = apply_slippage_penalty(
returns, daily_txn, starting_base, 1000000, impact=impact)
expected_adj_returns = Series(expected_adj_returns, index=self.dates)
assert_series_equal(adj_returns, expected_adj_returns)
def capacity_sweep(self,
returns,
transactions,
market_data,
bt_starting_capital,
min_pv=100000,
max_pv=300000000,
step_size=1000000):
"""
考虑资金量（每百万元）带来市场冲击后的修正夏普率
Parameters
----------
returns : pd.Series
Timeseries of portfolio returns to be adjusted for various
degrees of slippage.
transactions : pd.DataFrame
Prices and amounts of executed trades. One row per trade.
- See full explanation in tears.create_full_tear_sheet.
market_data : pd.Panel, optional
Panel with items axis of 'price' and 'volume' DataFrames.
The major and minor axes should match those of the
the passed positions DataFrame (same dates and symbols).
min_pv:int
最小入场资金
max_pv:int
最大入场资金
step_size:int
步长
Returns
-------
考虑资金量（每百万元）带来市场冲击后的修正夏普率（adj_sharpe）
资金量为index，单位为百万元
"""
txn_daily_w_bar = capacity.daily_txns_with_bar_data(
transactions, market_data)
captial_base_sweep = pd.Series()
for start_pv in range(min_pv, max_pv, step_size):
adj_ret = capacity.apply_slippage_penalty(returns, txn_daily_w_bar,
start_pv,
bt_starting_capital)
sharpe = empyrical.sharpe_ratio(adj_ret)
if sharpe < -1:
break
captial_base_sweep.loc[start_pv] = sharpe
captial_base_sweep.index = captial_base_sweep.index / MM_DISPLAY_UNIT
return captial_base_sweep
def test_daily_txns_with_bar_data(self):
daily_txn = daily_txns_with_bar_data(self.transactions,
self.market_data)
columns = ["symbol", "amount", "price", "volume"]
expected = pd.DataFrame(
data=[
["A", 100000, 1.0, 1000000.0],
["A", 100000, 1.0, 2000000.0],
["A", 100000, 1.0, 3000000.0],
],
columns=columns,
index=self.dates,
)
assert_frame_equal(daily_txn, expected)
