Trade analytics and excursion metrics

Expanding trade metrics

New in1.11.0

✅ Regular metrics like MAE and MFE only represent the final point of each trade. But what if you want to see how these metrics develop during the trade? You can now analyze expanding trade metrics as DataFrames!

Visualize the expanding MFE using projections

data = vbt.YFData.pull("BTC-USD")
pf = vbt.PF.from_random_signals(data, n=50, tp_stop=0.5, seed=42)
pf.trades.plot_expanding_mfe_returns().show()

Trade signals

New in1.8.2

✅ New trade plotting method that separates entry and exit trades into long entries, long exits, short entries, and short exits. It supports different styles for positions.

Plot trade signals of a Bollinger Bands strategy

data = vbt.YFData.pull("BTC-USD")
bb = data.run("bbands")
long_entries = data.hlc3.vbt.crossed_above(bb.upper) & (bb.bandwidth < 0.1)
long_exits = data.hlc3.vbt.crossed_below(bb.upper) & (bb.bandwidth > 0.5)
short_entries = data.hlc3.vbt.crossed_below(bb.lower) & (bb.bandwidth < 0.1)
short_exits = data.hlc3.vbt.crossed_above(bb.lower) & (bb.bandwidth > 0.5)
pf = vbt.PF.from_signals(
    data,
    long_entries=long_entries,
    long_exits=long_exits,
    short_entries=short_entries,
    short_exits=short_exits
)
pf.plot_trade_signals().show()

Edge ratio

New in1.8.1

✅ Edge ratio is a unique metric for quantifying entry profitability. Unlike most performance metrics, the edge ratio accounts for both open profits and losses. This can help you find better trade exits.

Compare the edge ratio of an EMA crossover to a random strategy

data = vbt.YFData.pull("BTC-USD")
fast_ema = data.run("ema", 10, hide_params=True)
slow_ema = data.run("ema", 20, hide_params=True)
entries = fast_ema.real_crossed_above(slow_ema)
exits = fast_ema.real_crossed_below(slow_ema)
pf = vbt.PF.from_signals(data, entries, exits, direction="both")
rand_pf = vbt.PF.from_random_signals(data, n=pf.orders.count() // 2, seed=42)  
fig = pf.trades.plot_running_edge_ratio(
    trace_kwargs=dict(line_color="limegreen", name="Edge Ratio (S)")
)
fig = rand_pf.trades.plot_running_edge_ratio(
    trace_kwargs=dict(line_color="mediumslateblue", name="Edge Ratio (R)"),
    fig=fig
)
fig.show()

Trade history

New in1.8.1

✅ Trade history is a human-readable DataFrame listing orders, extended with useful details about entry trades, exit trades, and positions.

Get the trade history of a random portfolio with one signal

data = vbt.YFData.pull(["BTC-USD", "ETH-USD"], missing_index="drop")
pf = vbt.PF.from_random_signals(
    data,
    n=1,
    seed=42,
    run_kwargs=dict(hide_params=True),
    tp_stop=0.5,
    sl_stop=0.1
)
pf.trade_history

   Order Id   Column              Signal Index            Creation Index  \
0         0  BTC-USD 2016-02-20 00:00:00+00:00 2016-02-20 00:00:00+00:00
1         1  BTC-USD 2016-02-20 00:00:00+00:00 2016-06-12 00:00:00+00:00
2         0  ETH-USD 2019-05-25 00:00:00+00:00 2019-05-25 00:00:00+00:00
3         1  ETH-USD 2019-05-25 00:00:00+00:00 2019-07-15 00:00:00+00:00

                 Fill Index  Side    Type Stop Type      Size       Price  \
0 2016-02-20 00:00:00+00:00   Buy  Market      None  0.228747  437.164001
1 2016-06-12 00:00:00+00:00  Sell  Market        TP  0.228747  655.746002
2 2019-05-25 00:00:00+00:00   Buy  Market      None  0.397204  251.759872
3 2019-07-15 00:00:00+00:00  Sell  Market        SL  0.397204  226.583885

   Fees   PnL  Return Direction  Status  Entry Trade Id  Exit Trade Id  \
0   0.0  50.0     0.5      Long  Closed               0             -1
1   0.0  50.0     0.5      Long  Closed              -1              0
2   0.0 -10.0    -0.1      Long  Closed               0             -1
3   0.0 -10.0    -0.1      Long  Closed              -1              0

   Position Id
0            0
1            0
2            0
3            0

MAE and MFE

New in1.3.0

✅ Maximum Adverse Excursion (MAE) helps you see the maximum loss taken during a trade, also known as the maximum drawdown of the position. Maximum Favorable Excursion (MFE) shows the highest profit reached during a trade. Analyzing MAE and MFE statistics can help you improve your exit strategies.

Analyze the MAE of a random portfolio without SL

data = vbt.YFData.pull("BTC-USD")
pf = vbt.PF.from_random_signals(data, n=50, seed=42)
pf.trades.plot_mae_returns().show()

Analyze the MAE of a random portfolio with SL

pf = vbt.PF.from_random_signals(data, n=50, sl_stop=0.1, seed=42)
pf.trades.plot_mae_returns().show()

Benchmark

New in1.0.4

✅ The benchmark can now be easily set for your entire portfolio.

Compare Microsoft to S&P 500

data = vbt.YFData.pull(["SPY", "MSFT"], start="2010", missing_columns="drop")

pf = vbt.PF.from_holding(
    close=data.data["MSFT"]["Close"],
    bm_close=data.data["SPY"]["Close"]
)
pf.plot_cumulative_returns().show()