def example_calculate_market_impact():
"""Calculates the transient market impact for a trade
"""
from tcapy.analysis.algos.metric import MetricTransientMarketImpact
market_df, trade_df = get_sample_data()
metric_market_impact = MetricTransientMarketImpact()
metric_market_impact.calculate_metric(trade_df, market_df)
print(trade_df)
def test_metric_calculation(fill_market_trade_databases):
"""Tests slippage calculation on a test set of market and trade data
"""
market_df, trade_df, order_df = get_sample_data()
#### Calculate slippage
market_df.index = market_df.index + timedelta(milliseconds=5)
# Add a mid point (in case it doesn't exist)
market_df = BenchmarkMarketSpreadToMid().calculate_benchmark(
market_df=market_df)
trade_df, _ = MetricSlippage().calculate_metric(trade_order_df=trade_df,
market_df=market_df,
bid_benchmark='mid',
ask_benchmark='mid')
# a selection of points to try
ind_list = [0, 1, 2, -2 - 1]
for i in ind_list:
# Now replicate slippage calculation from first principles (get the last available point if no match)
mid_index = market_df['mid'].index.get_loc(trade_df.index[i],
method='ffill')
trade = trade_df['executed_price'][i]
side = trade_df['side'][i]
slippage = trade_df['slippage'][i]
market_slippage = trade_df['slippage_benchmark'][i]
# market = market_df.ix[mid_index]['mid']
market = market_df['mid'][mid_index]
# Do slippage calculation for comparison with our method
slippage_comp = -side * (trade - market)
# Check that the 'slippage' column exists and is consistent
assert ('slippage' in trade_df.columns
and abs(slippage - slippage_comp) < eps)
### Check anomalous trade identification
market_df, trade_df, order_df = get_sample_data()
market_df.index = market_df.index + timedelta(milliseconds=10)
# Force spread to mid to be 0.25bp
anomalous_spread_to_mid_bp = 0.25
market_df = BenchmarkMarketSpreadToMid().calculate_benchmark(
market_df=market_df,
bid_mid_bp=anomalous_spread_to_mid_bp,
ask_mid_bp=anomalous_spread_to_mid_bp,
overwrite_bid_ask=True)
trade_df, _ = MetricSlippage().calculate_metric(trade_order_df=trade_df,
market_df=market_df)
anomalous_metric = trade_df[trade_df['slippage_anomalous'] == 1]
anomalous_comparison = trade_df[
trade_df['slippage'] <= -(anomalous_spread_to_mid_bp /
(100.0 * 100.0))]
# Now test if the correct trades have been identified as anomalous
assert_frame_equal(anomalous_metric[['slippage_anomalous']],
anomalous_comparison[['slippage_anomalous']])
#### Calculate market impact (using bid/ask)
market_df, trade_df, order_df = get_sample_data()
trade_df, _ = MetricTransientMarketImpact(transient_market_impact_gap={
'ms': 1250
}).calculate_metric(trade_order_df=trade_df,
market_df=market_df,
bid_benchmark='bid',
ask_benchmark='ask')
for i in ind_list:
# Now replicate transient market impact calculation from first principles (get the NEXT available point if not available)
time_to_search = trade_df.index[i] + timedelta(milliseconds=1250)
index = market_df.index.get_loc(time_to_search, method='bfill')
index_time = market_df.index[index]
trade = trade_df['executed_price'][i]
side = trade_df['side'][i]
if 'bid' in market_df.columns and 'ask' in market_df.columns:
if side == 1:
# market = market_df.ix[index]['ask']
market = market_df.iloc[index]['ask']
elif side == -1:
# market = market_df.ix[index]['bid']
market = market_df.iloc[index]['bid']
else:
# market = market_df.ix[index]['mid']
market = market_df.iloc[index]['mid']
market_transient_impact_benchmark = trade_df[
'transient_market_impact_benchmark'][i]
transient_market_impact = trade_df['transient_market_impact'][i]
# do transient market impact calculation for comparison with our method
transient_market_impact_comp = side * (trade - market)
# check that the 'transient_market_impact' column exists and is consistent
assert ('transient_market_impact' in trade_df.columns) and \
(abs(transient_market_impact - transient_market_impact_comp) < eps)