Bybit Perps
Backtest
Python / stdlib

NewByPerp — Short New Bybit Perp Listings

A systematic study of price/volume/OI behaviour after new perp listings, and the search for a tradeable edge on the crypto-native universe (162 symbols).

Executive Summary

+17.4%
Expectancy / trade (best strategy)
2.73×
Profit factor (after-fees 2.70×)
73.3%
Win rate (30 trades)
98.9%
P(expectancy > 0) — 10k Monte Carlo
95.3%
P(expectancy > 5%)
2.5 / mo
Trade frequency
Verdict: The naive "short at day 7, hold to 90" idea has a raw positive expectancy (avg +28%, 82% win-rate over 90 days) but every single trade suffers a 10–15%+ adverse excursion — so any realistic stop-loss destroys the edge. However, switching from a time-based to a condition-based entry (short only after the pump shows volatility contraction + breakdown below VWAP/EMA50) unlocks a robust edge: +17.4% expectancy/trade, PF 2.73×, 73% win-rate, with 99% Monte-Carlo confidence of positive expectancy. This is a low-frequency overlay (~2.5 trades/month), not a core strategy.

The Thesis

New Bybit perp listings are supported by market makers who are contractually obliged to provide liquidity — commonly structured as a 7-day TWAP over a contract period often quoted at ~90 days. When the obligation ends, MMs withdraw liquidity, which should cause a drop in price, volume and open interest. The original hypothesis was to short new listings after the first seven days and hold for 90 days to harvest that predictable decline.

We tested this rigorously: 349 new USDT perps listed in the last 365 days, daily OHLCV + volume + open-interest, 321 symbols with ≥14 days of history, separated into 162 crypto-native vs 109 stock-ETF perps (AAPL, TSLA, NVDA, QQQ…).

Universe & The Two Cohorts

739
Total USDT perps (linear)
349
Listings in last 365d
321
With ≥14 days history
162
Crypto-native (short universe)
109
Stock-ETF (excluded)
50%+
Recent listings are stock ETFs
Critical finding — two different asset classes. The 2026 wave of stock-ETF perps behaves opposite to the thesis: their open interest grows 10–50× over 13 weeks and volume is flat-to-growing (retail discovery). Shorting them after day 7 loses money (–0.9% d7→14, –1.8% d7→30). The short edge exists only on crypto-native perps. The market-maker expiry thesis applies to the crypto-native cohort.
CohortShort d7→14Short d7→30Interpretation
Stock-ETF (109)–0.90% (45% WR)–1.83% (37% WR)Trend up, OI +10–50×, no edge
Crypto-native (162)+4.13% (59% WR)+7.87% (70% WR)Edge present but fat-tailed

Naive Strategy: Short at Day 7 → Raw vs Risk-Adjusted

HorizonAvg ReturnMedianWin RateProfit Factor
Short d7→14 (n=321)+2.42%+0.87%54.5%1.46×
Short d7→30 (n=282)+4.71%+4.65%58.9%1.46×
Short d7→90 (n=154)+28.13%+40.03%81.8%4.19×

Why it breaks down under risk management:

Max Adverse Excursion (MAE) — all trades

Every single trade (100%) sees an adverse move before paying off. Median MAE 15–20%; many exceed 50–100%.

Stop-lossSurviving tradesExpectancy
None100%+16.0% (d7→30)
5%5%–3.7%
10%18%–1.4%
15%31%+1.5%
20%43%+2.8%
30%59%+6.5%

Why the MAE happens (first-7-day diagnosis)

Launch day is extremely volatile: average intraday adverse excursion –12%, favorable +19%. Day 0–4 routinely swing 10–20% against the open.

Critical insight: a strong launch (day-0 return > +10%) predicts continuation, not reversal — these symbols average +51% over the next 7 days. The pump is a fade only after it exhausts — which is why condition-based entries (waiting for breakdown) beat time-based entries.

Optimal Strategy (Verified Backtest)

Entry conditions (all must hold at close):
  1. ATR(14) < SMA(ATR,20) — volatility contraction after the pump
  2. Close < prior-day VWAP (typical price) — price below value
  3. Close < EMA50 — below medium-term trend
  4. Launch-to-date return > +30% — confirmed pump that can now exhaust

Execution: Enter short at next day's open · Stop = entry + 3.0 × ATR(14), trailing down · Max hold 30 days · 1 trade per symbol.

73.3%
Win rate
+37.44%
Avg win
–37.69%
Avg loss
+17.41%
Expectancy/trade
2.73×
Profit factor
16.7%
Stop-hit rate
32.6%
Avg MAE (was ~40%+ naive)
+26.9%
Median return
30
Trades over ~12 months

After Bybit taker fees (0.055%/side) + estimated slippage (≈0.21% round-trip): expectancy +17.20%, profit factor 2.70×.

Parameter sweep — what the optimisation found

Grid-search over ATR stop multiplier (1.5–5×), pump threshold (5–40%), hold (15–90d), trades/symbol (1–3):

ConfigNWRAvgWAvgLExpPF
ATR3 · Pump30 · Hold30 (best)3073.3%+37.4%–37.7%+17.4%2.73
ATR3 · Pump35 · Hold302774.1%+39.2%–35.2%+19.9%3.18
ATR4 · Pump30 · Hold303073.3%+37.4%–49.6%+14.2%2.08
ATR3 · Pump30 · Hold453070.0%+37.8%–47.5%+12.2%1.85
ATR4 · Pump30 · Hold30 (no ATR contraction)3278.1%+39.2%–58.0%+17.9%2.41

Filters that hurt: volume-spike-at-entry (too rare, high stop-out), distribution-day only (21 trades, negative expectancy), EMA20+EMA50 combo (over-filters). Wider stops (ATR4+) raise MAE exposure without better risk-adjusted return.

Validation: Walk-Forward & Monte Carlo

Walk-forward (temporal split by launch date)

SplitTrain symbolsTrain expTest symbolsTest tradesTest exp
60 / 4097+17.6%653+16.0%
70 / 30113+17.4%490
80 / 20129+17.4%330

Train expectancy is extremely stable across splits. Out-of-sample counts are small because the signal is rare — a real constraint of this universe.

Monte Carlo — 10,000 bootstrap runs

StatisticValue
Median expectancy+17.68%
5th percentile+5.23%
95th percentile+28.94%
P(expectancy > 0)98.9%
P(expectancy > 5%)95.3%
P(expectancy > 10%)84.5%

The edge is statistically significant — not a lucky cluster. But sample is small (30 trades): treat estimates as lower-confidence until 50+ live/paper signals.

Trade Log (all 30)

#SymbolReturnMAEHeld (d)Stop hit

Top-10 winners avg +53%; the 5 losers avg –57% and are the only trades that hit the stop. BSBUSDT survived a +148% adverse excursion to finish +22.6% — exactly the fat tail the 3×ATR stop is designed to tolerate.

The Inverted Idea: Catch the Pump, Then Short the Exhaustion

We also tested going long on the pump and flipping to short on distribution (long when price > VWAP & EMA50 after a strong launch; exit long & short on a distribution day: down-day + volume ≥1.5× SMA20 + close in lower third of range).

LegNWRAvgWAvgLExpPF
Long pump14733.3%+65.8%–21.2%+7.8%1.55
Short after distribution13850.7%+39.7%–48.8%–3.9%0.84
Combined28541.8%+50.5%–32.5%+2.1%1.11

Verdict: The long pump leg works (huge wins, +66% avg) but with a 33% win rate, and the short-after-distribution leg is negative — distribution-day signals are too rare and enter too late. The pure short-on-breakdown (optimal strategy) is strictly better.

Key Insights

Recommendations / Deployment

  1. Universe: crypto-native Bybit perps only, age > 50 days. Exclude stock-ETF perps (AAPL/TSLA/NVDA/QQQ & all *STOCK/USDT and ETF tickers).
  2. Run as a systematic daily scan at close, enter short at next open when the 4 entry conditions all hold.
  3. Position sizing: 0.5–1% risk per trade (half-Kelly; full Kelly ≈1.5%).
  4. Risk: stop = 3.0 × ATR(14) trailing down; max hold 30 days; 1 concurrent position per symbol.
  5. Expect ~2.5 trades/month — low-frequency overlay, not a core strategy. Scale capital accordingly.
  6. Regime guard: monitor BTC — strong BTC bull phases may correlate with alt short failures; consider pausing shorts in confirmed BTC uptrends.
  7. Paper trade 30–50 live signals before committing real capital; re-run Monte-Carlo to confirm the edge persists out-of-sample.
  8. Future refinement: add funding-rate extremes and OI-decline as filters; pull 1m data for precise stop/slippage modelling; track order-book depth for true MM-withdrawal detection.