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

Token Scoring: Evaluate any meme token with a 0-100 composite score
Strategy Matching: Define multiple strategies with different thresholds and entry modes
Paper Trading: Simulate buy/sell with configurable take-profit and stop-loss
Watchlist Management: Lifecycle tracking (watching → buy_signal → bought → sold/dismissed)
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:

Smart Money trading signals (buy direction, 24h window)
Smart Money inflow data
Token Dynamic API smartMoneyHolders field

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:

Social Hype Leaderboard ranking
Topic Rush association
Unified Rank presence

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:

Sort matching strategies by threshold (highest first)
Pick the first strategy where totalScore >= buyThreshold
This ensures higher-threshold strategies get priority

Paper Trading Simulation

Entry Logic

When evaluator sets status to buy_signal, paper trader:

Records entry price from Token Dynamic API
Creates a paper trade record with entry timestamp
Sets watchlist status to bought

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

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