Ranking Methodology
We use two separate ranking algorithms: Global Rank for cross-market discovery and Market Rank for comparing bots within a specific asset class. Both are computed server-side on every request, so rankings always reflect the latest data.
1. Global Rank
Used on the homepage to surface the best overall trading bots. Favors versatility, community trust, and affordability.
| Signal | Weight | Why it matters |
|---|---|---|
| Market Coverage | x 12 / market | Bots supporting multiple asset classes (crypto, stocks, forex) rank higher. Each additional market adds 12 points. |
| User Reviews | rating x count x 8 | Real user feedback is the strongest signal. A bot with 10 reviews averaging 4.5 gets 360 points (4.5 x 10 x 8). |
| GitHub Stars | x 0.2 / star | Developer community endorsement. 500 stars = 100 points. Moderate influence — complements reviews, doesn't replace them. |
| Open Source | +15 | Transparency bonus. Open-source bots let you audit the code. No black boxes. |
| Features | x 3 / feature | More capabilities = more value. Backtesting, API access, paper trading each add 3 points. |
| Free Tier | +5 | Bots with a genuine free tier get a bonus. Accessibility matters. |
| Price Penalty | -price / 10 | Higher starting prices are penalized. A $90/mo bot loses 9 points. Capped at -10. |
x 12 / marketBots supporting multiple asset classes rank higher. Each additional market adds 12 points.
rating x count x 8Real user feedback is the strongest signal. 10 reviews at 4.5 avg = 360 points.
x 0.2 / starDeveloper community endorsement. 500 stars = 100 points. Complements reviews.
+15Transparency bonus. Open-source bots let you audit the code.
x 3 / featureBacktesting, API access, paper trading each add 3 points.
+5Bots with a genuine free tier get a bonus.
-price / 10Higher prices penalized. $90/mo = -9 points. Capped at -10.
global_score =markets x 12+ rating x reviews x 8+ stars x 0.2+ (open_source ? 15 : 0)+ features x 3+ (has_free_tier ? 5 : 0)− min(price / 10, 10)
2. Market Rank
Used on market pages like /crypto and/stocks. When comparing bots within the same asset class, we remove market coverage and increase the weight of reviews.
| Signal | Weight | vs Global |
|---|---|---|
| Market Coverage | removed | Irrelevant when all bots serve the same market. |
| User Reviews | rating x count x 12 | Boosted from x8 to x12. Social proof matters more in head-to-head market comparisons. |
| GitHub Stars | x 0.2 | Same as global. |
| Open Source | +15 | Same as global. |
| Features | x 3 | Same as global. |
| Free Tier | +5 | Same as global. |
| Price Penalty | -price / 10 | Same as global. Capped at -10. |
removedIrrelevant when all bots serve the same market.
rating x count x 12Boosted from x8 to x12. Social proof matters more head-to-head.
x 0.2Same as global.
+15Same as global.
x 3Same as global.
+5Same as global.
-price / 10Same as global. Capped at -10.
market_score =rating x reviews x 12+ stars x 0.2+ (open_source ? 15 : 0)+ features x 3+ (has_free_tier ? 5 : 0)− min(price / 10, 10)
3. Data Sources & Freshness
Bot metadata (features, pricing, markets, deployment) is maintained by our editorial team. Updated whenever a bot ships new capabilities or changes pricing.
User reviews are submitted by real traders through our review system. Only approved reviews count toward rankings. We moderate every submission to filter spam and fake reviews.
GitHub stars are fetched daily from the GitHub API, reflecting the live star count of each bot's public repository.
Rankings are recomputed on every request. No caching, no stale scores. When a new review is approved or GitHub stars change, rankings update immediately.
4. Design Philosophy
No pay-to-rank. Bots cannot buy higher positions. Rankings are purely algorithmic based on public data and user reviews.
Transparent weights. Every signal and its multiplier is documented here. No black-box ranking. You can verify the math.
Two ranks, one truth. A bot that dominates crypto may not rank highly globally if it lacks multi-market support. Both perspectives are valid; we surface both.
Reviews are the strongest signal. We weight real user experiences above all else. A bot with 50 genuine 4.5-star reviews will outrank one with 1,000 GitHub stars and zero users.
Evolving algorithm. We tune weights based on what produces the most useful rankings. Current version: v0.1.
Last updated: July 2026