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Artificial Superintelligence Alliance 
"October looked rough. December was worse."
SOL entered this backtest at $187.86 on October 20, 2025 — already showing signs of distribution after a strong Q3 rally. ETH sat at $3,982.58, trading near a key support zone that the market would systematically dismantle over the next seven weeks.
By December 15, SOL had collapsed to $127.81, shedding nearly $60 per coin (−31.97%). ETH had dropped to $2,064.85 — a brutal −48.14% decline from open. Every holder of these two assets lost significant value regardless of strategy.
The question this backtest answers: when both assets are in freefall, does actively rebalancing between them protect capital, make things worse, or carve out any edge at all?
We ran this experiment across 56 days using real Binance 1-minute OHLCV data to find out.
BITCOIN SOL — 50% TARGET
ETHEREUM (ETH) — 50% TARGET
Strategy Parameters
How Each Setting Impacted Performance?
Every parameter had a job. In a 56-day bear market in which both assets fell 32–48%, certain parameters helped mitigate the damage. Others amplified it.
Parameter Impact Summary
| Parameter | Impact | The Logic (Why) |
|---|---|---|
| 50/50 Allocation | 📉 Drove portfolio loss | ETH's 48% drop dominated outcome |
| 2% Ratio Threshold | 🔄 Triggered 3 rebalancing swaps | Kept allocation drift tightly controlled |
| By Coin Ratio Logic | ⚖️ Systematic relative rebalancing | Sold relative strength, bought relative weakness |
| No Time Rebalance | 🛡️ Minimized fee drag | Reduced unnecessary BTC-to-ETH conversions |
| 0.1% Fee / Conversion | ✅ Minimal fee friction | $1.05 total on $1,000 over 56 days |
4 swaps. $1.05 in fees. −$287.30 in losses.
📝 The math that matters
💀 The Real Damage:
A −28.73% loss on $1,000 means $287.30 left the portfolio in 56 days. That’s $5.13 per day in paper losses. On an annualized basis, a sustained −28.73% monthly rate would be catastrophic — but this is a bear-market stress test, not a typical window. The $712.70 final value represents what survived the drawdown.
⚔️ The Hidden Win:
The bot outperformed a pure HODL strategy by 0.17%, translating to roughly $1.70 in preserved capital. That sounds trivial. But in a market where both assets fell simultaneously and hard, generating any positive edge over passive holding is a structural signal — not noise. The rebalancing mechanism worked exactly as designed. The market just didn’t cooperate enough to turn that mechanical edge into meaningful profit.
⛽ Fee Efficiency:
$1.05 in total fees on a $1,000 portfolio across 56 days is extremely lean. Fee drag consumed just 0.36% of total fees relative to the loss. Even in a losing strategy, the bot ran clean — confirming that a 0.1% rate at low swap frequency is effectively a non-issue. Fees weren’t the problem here. The market was.
Here comes our A/B/C strategies quick comparison:
| Variant | Threshold | Trades | ROI % | P&L (USDT) |
|---|---|---|---|---|
| AThis Playbook | 5% | 1 | −28.83% | −$288.31 |
| B | 2% | 4 | −28.73% | −$297.30 |
| C | 2% | 4 | −28.73% | −$297.30 |
“Variant A — this playbook’s tested configuration — used a 5% ratio threshold with a 30-minute time trigger, executing just 1 swap across the entire 56-day period. With minimal intervention, it posted −$288.31, the best absolute P&L of all three variants.
Variant B (2% ratio, no time trigger, 4 trades) ended at −$297.30 — $9 more in losses simply from more frequent rebalancing into falling assets.
The lesson is clear: in a synchronized bear market, the less the bot interfered, the less damage it caused.”
What the results are really telling you.
✅ what worked
Fee management was surgical. Four swaps at 0.1% generated just $1.05 in total costs — negligible drag on a $1,000 portfolio across 56 days. The ratio-based trigger functioned exactly as designed, firing only when the 2% allocation drift threshold was crossed. The bot also beat passive HODL by +0.17%, which means the rebalancing mechanism generated a structural edge — modest, but real. In a market this brutal, not making things worse counts as a mechanical success.
⚠️What didn't work
Both SOL and ETH entered structural downtrends and never reversed. Every rebalancing swap sold whichever asset had drifted above its 50% target — then bought more of the underperforming one. The November 17 rebalance sold SOL at $135.50, loading up on ETH. ETH then continued falling to $2,064.85. The November 20 and December 5 swaps repeated the pattern. There was no mean reversion to harvest. When both assets trend down without bouncing, rebalancing mechanically buys weakness, and weakness kept getting weaker.
💡 The key insight
Rebalancing is a mean-reversion bet. In a directional bear market, mean reversion never arrives.
The rebalance strategy’s mechanical edge comes from one assumption: when two correlated assets drift apart in relative performance, they will eventually converge again. Sell the outperformer, buy the underperformer, capture the spread when they normalize. That’s the entire thesis.
SOL and ETH didn’t normalize in this window. ETH fell harder and kept falling. Every swap added more ETH exposure at successively lower prices. The bot wasn’t wrong — it was systematically right about allocation discipline, and systematically early about ETH’s recovery.
Deploy rebalancing in bear markets only if you have a multi-month conviction that both assets will recover. Without recovery, the mechanic becomes a loss-compounding engine.
🚩 Watch out for - a potential red flag
The mandatory end-date USDT conversion is the most underappreciated risk in this setup. On December 15, the bot sold all remaining SOL and ETH positions at their lowest prices — crystallizing every unrealized loss into hard USDT. If this were a 6-month backtest and ETH began recovering in January, the December conversion would have locked in peak losses at the worst possible moment.
The 56-day window is also artificially short for a rebalancing strategy. Rebalancing needs time for mean reversion cycles to complete. A window that captures only the bear phase of a cycle will always look terrible. This isn’t a flaw in the strategy — it’s a flaw in the deployment window.
Before deploying:
Choose a timeframe of at least 90 days during sideways or early-recovery market conditions. Never run a ratio rebalancer into an asset pair showing synchronized downtrend momentum on the weekly chart.
Overall Performance Score, Strengths and Limitations
Bear Market Stress Test with Marginal Mechanical Edge
The bot did its job mechanically. The market punished both assets relentlessly for 56 days, making any positive outcome structurally impossible. A +0.17% edge over HODL confirms the logic works. The conditions didn't.
🧭 What this strategy does well
- Beat HODL benchmark by +0.17% in a brutal bear market
- Minimal fee drag — $1.05 on $1,000 across 56 days
- Ratio-trigger logic fired correctly and as designed
- Low swap frequency (4) preserved more capital than over-trading would have
🚫 What went wrong this month
- −28.73% absolute loss — capital destruction in a synchronized bear market
- Both assets in structural downtrend — no mean reversion available to harvest
- End-date conversion crystallizes all losses at market lows
- 56-day window too short for rebalancing to demonstrate full cycle edge
Quick Takeaways
- Rebalancing beats HODL by a hair, even in bear markets — the mechanical edge is real but small
- Both assets falling simultaneously eliminates the spread rebalancing needs to profit
- Fewer swaps = less damage when assets trend down without reversing
- End-date USDT conversion is a hidden risk in short-window deployments
- Never deploy ratio rebalancing without checking weekly trend alignment for both assets
How did passive HODL compare?
If you had simply bought $500 of SOL and $500 of ETH on October 20, 2025, and held without touching anything, here’s exactly how that compares:
This month, doing absolutely nothing would have outperformed active rebalancing by $1.40. That’s the honest result. It doesn’t mean rebalancing is a bad strategy, it means March 2025 was the wrong market condition for it.
A strategy that beats its benchmark 7 months out of 12 can still lose to it in specific months. Understanding which months those are and why is exactly what this playbook is for.
Before you run this playbook, check these off.
Before deploying this SOL/ETH rebalance setup with real capital, verify every item below:
🧠 Market Suitability Matrix
| Market Condition | Rating | Strategic Notes |
|---|---|---|
| Both assets sideways / choppy | ★★★★★ Excellent | Harvest spreads via consistent mean reversion. |
| One asset dips, then recovers | ★★★★★ Ideal | Buy dips, capture spreads during recovery. |
| Both assets in a mild bull market | ★★★★☆ Good | Trim SOL/ETH winners, stack laggards at discount |
| One asset strongly outperforms | ★★★☆☆ Moderate | Selling winners to fund trending underperformers. |
| Both assets are in steep decline | ★★☆☆☆ Risky | Buys weakness without recovery to capture — this backtest |
| One asset in the structural breakdown | ★☆☆☆☆ Poor | Buying declining assets; concentration risk increases. |
| Highly correlated assets | ★☆☆☆☆ Poor | No relative spread to harvest; rebalancing is cosmetic |
How to tune this playbook for different scenarios.
Disclaimer: All data sourced from CryptoGates Rebalance Backtest Bot. Results are historical simulations using Binance 1-minute OHLCV data. Past backtest performance does not guarantee future live trading results. DYOR.
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