Executive Summary

This study validates the effectiveness of CoinRoc's cryptocurrency rating system for identifying successful grid trading candidates. Using a rigorous blind forward test methodology across 60 cryptocurrencies, we demonstrate that our rating system achieves 100% accuracy for B-rated and above cryptocurrencies.

0.886

Correlation

+92.4%

Top 5 Return

100%

B+ Win Rate

+52.1%

vs Buy & Hold

Note on this figure, added 2026-08-05: The 0.886 correlation (and the return/win-rate figures on this page) were measured under an earlier version of the rating formula (dated 2026-01-29). The formula has since been re-optimized three times; internal walk-forward testing as of 2026-05-13 indicates a higher correlation under the current formula. We are re-running this study under the live formula and will republish updated figures. The qualitative finding — that higher-rated assets have historically outperformed lower-rated ones — is not affected by this note; the exact numbers above should not be cited as describing today's system until the re-run is complete.

Methodology: Blind Forward Testing

To ensure realistic performance expectations, we use a strict blind forward test methodology that prevents any look-ahead bias.

Year 1: Parameter Calculation

Days 1-360 of historical data

Calculate grid parameters (upper/lower limits)
Compute all rating factors (ETS, Calmar, Sortino, etc.)
Generate composite rating (A+ to D)
NO access to Year 2 data

Year 2: Performance Measurement

Days 361-720 of historical data

Trade using ONLY Year 1 parameters
No parameter adjustments allowed
Measure actual grid trading returns
Compare to Year 1 ratings

Why this matters: Traditional backtesting often "peeks" at future data when setting parameters, leading to overly optimistic results. Our blind forward test gives you realistic expectations of what to expect from the rating system.

The Six Rating Factors

Our rating system combines six factors, weighted based on their predictive power:

Factor	Weight	What It Measures
Grid Return	40%	Historical profit from grid simulation
Sortino Ratio	25%	Return per unit of downside risk only
Calmar Ratio	20%	Return divided by max drawdown
CS-Score (Sentiment)	5%	Social media sentiment indicator
ETS Score	5%	Grid trading suitability (volatility patterns)
Liquidity	5%	Trading volume and market depth

Results by Grade

Grade	Count	Avg Return	Win Rate	Recommendation
A	1	+38.1%	100%	Strong Buy
B	9	+17.8%	100%	Buy
C	37	+2.3%	57%	Caution
D	12	-12.2%	8%	Avoid

Key Finding: Every single cryptocurrency rated B- or above was profitable in Year 2.

Note on this figure, added 2026-08-05: The 0.886 correlation (and the return/win-rate figures on this page) were measured under an earlier version of the rating formula (dated 2026-01-29). The formula has since been re-optimized three times; internal walk-forward testing as of 2026-05-13 indicates a higher correlation under the current formula. We are re-running this study under the live formula and will republish updated figures. The qualitative finding — that higher-rated assets have historically outperformed lower-rated ones — is not affected by this note; the exact numbers above should not be cited as describing today's system until the re-run is complete.

Top 10 Performers (Selected by Year 1 Rating)

Rank	Crypto	Year 2 Return	Key Strengths
1	CAKE	+159.0%	Exceptional grid return + strong Sortino
2	BCH	+96.9%	High returns + excellent Calmar ratio
3	ZEC	+90.9%	Strong Sortino + balanced risk metrics
4	NMR	+59.4%	High alpha + good downside protection
5	LTC	+55.7%	Consistent performer + strong liquidity
6	ATOM	+48.2%	Good Sortino + favorable volatility
7	INJ	+47.5%	Strong grid return + high alpha
8	DOGE	+35.0%	Neutral sentiment + good Calmar
9	LINK	+27.9%	Balanced scores across all factors
10	BTC	+20.4%	Baseline performer + excellent liquidity

Top 5 Portfolio Average: +92.4% vs Buy & Hold average of +40.3%

Note on this figure, added 2026-08-05: The 0.886 correlation (and the return/win-rate figures on this page) were measured under an earlier version of the rating formula (dated 2026-01-29). The formula has since been re-optimized three times; internal walk-forward testing as of 2026-05-13 indicates a higher correlation under the current formula. We are re-running this study under the live formula and will republish updated figures. The qualitative finding — that higher-rated assets have historically outperformed lower-rated ones — is not affected by this note; the exact numbers above should not be cited as describing today's system until the re-run is complete.

Grid Trading vs Buy & Hold

Top 5 Portfolio

Grid Trading Return: +92.4%

Buy & Hold Return: +40.3%

Outperformance: +52.1%

Top 10 Portfolio

Grid Trading Return: +64.1%

Buy & Hold Return: +28.7%

Outperformance: +35.4%

Note on this figure, added 2026-08-05: The 0.886 correlation (and the return/win-rate figures on this page) were measured under an earlier version of the rating formula (dated 2026-01-29). The formula has since been re-optimized three times; internal walk-forward testing as of 2026-05-13 indicates a higher correlation under the current formula. We are re-running this study under the live formula and will republish updated figures. The qualitative finding — that higher-rated assets have historically outperformed lower-rated ones — is not affected by this note; the exact numbers above should not be cited as describing today's system until the re-run is complete.

Conclusion

Our rating system demonstrates strong predictive power for identifying successful grid trading candidates. Key findings:

0.886 correlation between Year 1 ratings and Year 2 performance
100% win rate for B-rated and above cryptocurrencies
+52.1% outperformance vs buy & hold for top 5 rated assets
Sortino ratio improved predictions by 3.6% over previous model

Note on this figure, added 2026-08-05: The 0.886 correlation (and the return/win-rate figures on this page) were measured under an earlier version of the rating formula (dated 2026-01-29). The formula has since been re-optimized three times; internal walk-forward testing as of 2026-05-13 indicates a higher correlation under the current formula. We are re-running this study under the live formula and will republish updated figures. The qualitative finding — that higher-rated assets have historically outperformed lower-rated ones — is not affected by this note; the exact numbers above should not be cited as describing today's system until the re-run is complete.

Rating System Validation Study