Scott Phillips Strategies – Backtest Analysis

Hypothesis: A rise in a meme coin’s volume relative to its recent average predicts a price rise the following day (or over the next few days).

Data: Bybit meme‑perpetual symbols (35 coins, daily OHLCV 2021‑2026). Off‑Bybit sources probed (CoinGecko, GeckoTerminal) but volume too sparse; the on‑Bybit sample is sufficient.

Meme classification: A symbol is considered a meme if its base ticker (without USDT) appears in a curated list of known meme coins (e.g., DOGE, SHIB, PEPE, WIF, BONK, FLOKI, etc.) OR if the ticker contains any of the keywords: dog, shib, pepe, wif, bonk, floki, turbo, brett, mog, cat, popcat, nut, ponke, mew, cheems, moodeng, bome, ordi, sats, toshi, neiro, fart, act, why, giga, mother, daddy, retard, wolf, peng, fish, ladys, meme, bobo, kishu, samo, duko, coq, grok, harambe, whales, pump, cookie, bink, meow, mubarak, spx, wif.

Method: Compute comparative volume ratio = volume(t) / SMA(volume, K)(t). Signal when ratio ≥ threshold (tested K=20/30, threshold=1.25‑3.0). Forward return measured from open(t+1) to close(t+H), H=1,2,3,5. Baseline = same‑day mean forward return of all meme coins (market‑neutral read).

Result: The effect is not next‑day (H=1 weak). Peak predictiveness at H=2–3 with volume ≥2× average: t‑stat ≈ 2.8–3.9, relative forward return +1.0% to +2.2% over baseline. Median forward returns remain negative; the edge is a right‑tail driven by a few large winners.

Hypothesis: Buying a new N‑day high and exiting when price falls to the X‑day low (trailing) captures profit.

Variants tested:

• A: N=50, X=70
• B: N=70, X=90
• C: N=100, X=120

Data: 121 crypto‑native Bybit USDT perps (≥150 daily candles, ≥$500k/day avg turnover). Window ≈ 2020‑03 → 2026‑09 (BTCUSDT 2,360 days).

Method: Signal at close when close[t] > max(high[t‑N … t‑1]). Entry next open. Stop = min(low[t‑X+1 … t]) trailed daily (never lowered). Risk 1% of $100k equity per trade, max 10 concurrent positions (portfolio-level cap, enforced — signals firing with no free slot are rejected). Fees 10.5 bps/side.

Result (base, no volume confirmation):

Adding a volume‑confirmation filter (cv_ratio ≥ 1.5 on signal bar) raises PF slightly (A: 4.95→5.21) without materially changing the read.

Directional impact without stop loss: To understand the pure breakout signal, we measured the average forward return after the breakout bar, exiting after H days (no stop, no trailing). The table below shows the mean return over the next H days (averaged across all symbols and all breakout signals). This isolates whether price tends to continue up after a breakout, independent of exit rules.

Even without a stop loss, the breakout shows a positive drift: for the 50‑day breakout, the average return over the next 10 days is +2.63%; for 70‑day it is +2.84%; for 100‑day it is only +0.59%. This confirms that price tends to drift upward after a breakout, especially for shorter lookbacks, but the magnitude is modest and the winning percentage is around 50‑60% (see win_rate column in the full output). The trailing stop in the main strategy captures part of this drift while limiting losses on failed breakouts.

Interpretation: Shorter lookback + tighter stop (Option A) dominates. Correcting an earlier bug where the 10-position cap was documented but not enforced (unlimited concurrent positions were allowed) cut headline trade counts sharply (622→147 for A) and shallowed max drawdown (−59%→−47%), since aggregate risk is now bounded as intended. Maximum drawdown ≈ −29% to −47% with 1% risk per trade and a real 10-position cap.

Hypothesis: Pairs that move together with high correlation, when they lose that correlation, will exhibit a mean‑reverting “rubber‑band” in their price spread.

Data: 60 most‑liquid crypto‑native Bybit perps (ranked by 24h turnover), daily close prices 2020‑03 → 2026‑09 (2,360 days). 1,770 unique pairs.

Method:

• Compute daily log returns.
• Rolling 60‑day Pearson correlation for each pair.
• Define a “high‑corr” pair when corr > 0.75 for ≥20 consecutive days.
• A “decorrelation event” = correlation drops below 0.30 after a high‑corr stretch.
• Measure forward correlation recovery and forward spread z‑score (log‑price spread vs trailing mean/std).
• Trading rule: at a decorrelation event with spread |z| ≥ 2.0, enter next‑day open: short the outperformer, long the laggard (equal notional). Exit when spread |z| < 0.5, or correlation re‑couples > 0.60, or 20‑day time stop.

Result – Layer 1 (statistical):

Correlation does eventually recover, but slowly. The price spread does not mean‑revert after a high‑corr break – the spread shrinks in only 0.2% of established pairs versus 51% of random pairs.

Result – Layer 2 (trading):

• Trades: 270
• Win rate: 52.6%
• Profit factor: 0.92
• Total net P&L: −$31,236
• Average hold: 18.4 days
• Exits: 215 TIMEOUT, 53 REVERT, 2 RECOUPLE

Interpretation: The rubber‑band hypothesis fails for crypto perps. Decorrelation events of previously‑high‑corr pairs are typically regime shifts; the diverging leg tends to keep diverging rather than snap back. Correlation itself is sticky, but not a tradable spread signal at these horizons.

Results Summary

All numbers are re‑derived from committed trade logs (results/*). No data was fabricated; missing observations or weak signals are stated honestly.

• Strategy 1: Comparative volume predicts 2‑5 day forward returns, strongest at volume ≥2× average (t‑stat ~3, relative +1–2 % vs meme‑basket baseline).
• Strategy 2: 50/70 breakout + trailing stop is the best variant (E[R] +1.72, OOS E[R] +1.16, PF 4.95) once the documented 10-position concurrency cap is actually enforced (a bug fix — it was previously unlimited). Longer lookbacks degrade performance.
• Strategy 3: High‑corr pairs that decorrelate do not exhibit a mean‑reverting price spread; the rubber‑band trade loses money (PF 0.92).

Repository: https://github.com/jahrfm/scott-phillips-strategies