Meme Signal Evaluator

Meme Signal Evaluator

Overview

A systematic scoring engine that evaluates meme tokens across 6 dimensions, matches them against configurable trading strategies, and simulates paper trades. Designed to turn raw market data into actionable buy/sell signals.

Use Cases

1. Token Scoring: Evaluate any meme token with a 0-100 composite score 2. Strategy Matching: Define multiple strategies with different thresholds and entry modes 3. Paper Trading: Simulate buy/sell with configurable take-profit and stop-loss 4. Watchlist Management: Lifecycle tracking (watching → buy_signal → bought → sold/dismissed) 5. Performance Tracking: Win rate, average P&L, and per-strategy statistics

6-Dimension Scoring Algorithm

Each dimension scores 0-100 independently. Final score = weighted sum + negative penalty.

Dimension 1: Smart Money (SM) Score

Weight: 20% (default, configurable)

Data Sources:

Scoring Logic:

SM buy signal count:
  ≥5 SM addresses buying → 80pts
  ≥3 SM addresses buying → 60pts
  ≥1 SM address buying  → 40pts
SM inflow amount:
  >$50K inflow → +20pts
  >$10K inflow → +10pts
Dynamic SM holders (fallback when no signals):
  ≥5 holders → 60pts
  ≥3 holders → 45pts
  ≥1 holder  → 25pts
Cap: 100

Dimension 2: Social Score

Weight: 10% (default)

Data Sources:

Scoring Logic:

Social Hype ranking:
  Top 10  → 90pts
  Top 30  → 70pts
  Listed   → 40pts
  Positive sentiment → +10pts
Topic Rush association:
  Found in trending topic → +25pts
  Topic net inflow >$10K  → +10pts
Fallback: present in Unified Rank → 30pts
Cap: 100

Dimension 3: Trend Score

Weight: 20% (default)

Data Source: Token Dynamic API real-time price changes

Scoring Logic:

1h price change:
  >20% → +40pts (strong trend)
  >10% → +30pts
  >5%  → +20pts
  >0%  → +10pts
5m momentum:
  >5%  → +20pts
  >2%  → +10pts
4h trend confirmation:
  >10% → +15pts
  >5%  → +8pts
Multi-timeframe resonance (5m+1h+4h all positive): +10pts
1h drop <-10%: -20pts penalty
Cap: 100

Dimension 4: Inflow/Volume Score

Weight: 20% (default)

Data Source: Token Dynamic API volume data

Scoring Logic:

5m volume:
  >$100K → 60pts
  >$50K  → 45pts
  >$10K  → 30pts
  >$5K   → 15pts
Buy/sell ratio (24h):
  Buy% ≥60% → +20pts (strong buy pressure)
  Buy% ≥55% → +10pts
1h volume:
  >$500K → +15pts
  >$100K → +8pts
Cap: 100

Dimension 5: KOL/Whale Score

Weight: 15% (default)

Data Source: Token Dynamic API holder data

Scoring Logic:

KOL holders:
  ≥10 → 50pts
  ≥5  → 35pts
  ≥2  → 20pts
Pro holders:
  ≥5  → +25pts
  ≥2  → +15pts
  ≥1  → +8pts
KOL holding percentage:
  >5% → +15pts
Cap: 100

Dimension 6: Hype Score

Weight: 15% (default)

Data Sources: Topic Rush data, Meme Exclusive ranking

Scoring Logic:

Topic Rush (Viral topics):
  Found in viral topic → 70pts
  Topic inflow >$10K   → +15pts
Meme Exclusive ranking:
  Score ≥4.0 → 80pts
  Score ≥3.0 → 60pts
  Score ≥2.0 → 40pts
  Listed     → 20pts
Cap: 100

Negative Signals (Penalty)

Applied after positive scoring. Can reduce total score.

Token audit risk (honeypot, rug pull):
  High risk detected  → -30pts + force dismiss
High tax (>10%):
  → -20pts
DEX screener paid without real traction:
  → -10pts

Final Score Calculation

rawScore = SM × w_sm + Social × w_social + Trend × w_trend + 
           Inflow × w_inflow + KOL × w_kol + Hype × w_hype
totalScore = max(0, rawScore + negativePenalty)

Default weights: SM=20, Social=10, Trend=20, Inflow=20, KOL=15, Hype=15

Strategy Configuration

Multiple strategies can be defined with different entry modes and thresholds.

| Field | Type | Description | |-------|------|-------------| | name | string | Strategy name (e.g., volume_5m_50k) | | entryMode | string | Entry trigger (volume_driven, sm_driven) | | buyThreshold | number | Minimum total score to trigger buy (e.g., 20, 30, 40) | | enabled | boolean | Whether strategy is active | | weightSm/Social/Trend/Inflow/Kol/Hype | number | Dimension weights (should sum to 100) |

Strategy Matching

When a token's totalScore reaches a strategy's buyThreshold: 1. Sort matching strategies by threshold (highest first) 2. Pick the first strategy where totalScore >= buyThreshold 3. This ensures higher-threshold strategies get priority

Paper Trading Simulation

Entry Logic

Exit Logic (checked on each evaluation cycle)

Take Profit: price ≥ entry × (1 + takeProfitPct/100)  → sell, mark "tp"
Stop Loss:   price ≤ entry × (1 - stopLossPct/100)    → sell, mark "sl"
Timeout:     holdTime > maxHoldMinutes                  → sell, mark "timeout"

Default: Take Profit = 50%, Stop Loss = 20%, Max Hold = 1440 minutes (24h)

Trade Record Fields

| Field | Description | |-------|-------------| | entryPrice | Price at buy | | exitPrice | Price at sell | | pnlPercent | (exitPrice - entryPrice) / entryPrice × 100 | | strategyUsed | Which strategy triggered the buy | | exitReason | tp (take profit) / sl (stop loss) / timeout |

Pipeline Workflow

The complete pipeline runs on a scheduler (default: every 5 minutes):

1. Collect Data    → Run all collectors (unified-rank, meme-rush, smart-money, social-hype)
2. Scan Watchlist  → Filter new tokens into watchlist based on global filters
3. Evaluate        → Score all watching tokens using 6-dimension algorithm
4. Paper Trade     → Execute simulated buys for buy_signal tokens
5. Monitor         → Check existing positions for TP/SL/timeout exits

Global Filters for Watchlist Entry

| Filter | Default | Description | |--------|---------|-------------| | minMarketCap | $10K | Minimum market cap | | maxMarketCap | $50M | Maximum market cap | | minLiquidity | $5K | Minimum liquidity | | minHolders | 50 | Minimum holder count | | minVolume5m | $1K | Minimum 5-minute volume | | maxTokenAgeHours | 72 | Maximum token age |

Notes

1. All scores are 0-100. Higher = more bullish. 2. Weights are percentages and should sum to 100 for proper normalization. 3. The evaluator fetches fresh Token Dynamic data before each evaluation for accuracy. 4. Strategy matching uses the highest-threshold-first approach for conviction grading. 5. Paper trading tracks simulated P&L for strategy backtesting without risk.

1. All scores are 0-100. Higher = more bullish. 2. Weights are percentages and should sum to 100 for proper normalization. 3. The evaluator fetches fresh Token Dynamic data before each evaluation for accuracy. 4. Strategy matching uses the highest-threshold-first approach for conviction grading. 5. Paper trading tracks simulated P&L for strategy backtesting without risk.