Large wallet monitoring, accumulation and distribution detection, and smart money signal generation for Solana tokens
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill whale-tracking
Whale tracking monitors the on-chain behavior of large wallets to detect accumulation, distribution, and smart money movements before they become visible in price action. On Solana, where token ownership is highly concentrated and whale transactions can move markets instantly, tracking large holders is one of the highest-signal alpha sources available.
A single large wallet selling 5% of a token's supply can crash the price 30-50% on thin Solana DEX liquidity. Conversely, a known profitable wallet accumulating a new token often precedes major price runs. Whale tracking converts on-chain transparency into actionable intelligence.
Key use cases:
Whale classification depends on context. A wallet holding $50K of a $1M market cap token is a whale; the same $50K in SOL is not. Use relative and absolute thresholds:
| Category | Trade Size | Portfolio Size | Typical Behavior |
|----------|-----------|---------------|-----------------|
| Retail | < 10 SOL | < 100 SOL | Reactive, follows trends |
| Mid-size | 10-100 SOL | 100-1,000 SOL | Mixed strategies |
| Whale | 100-1,000 SOL | 1,000-10,000 SOL | Informed, moves markets |
| Mega-whale | > 1,000 SOL | > 10,000 SOL | Market makers, funds, insiders |
| Metric | Threshold | Significance |
|--------|-----------|-------------|
| % of supply held | > 2% | Significant holder |
| % of daily volume | > 5% single trade | Market-moving transaction |
| Top N holders | Top 20 | Core holder group |
| Concentration ratio | Top 10 hold > 50% | High concentration risk |
Use both absolute and relative metrics. A 50 SOL trade in a $200K market cap token is whale-level; the same trade in a $50M token is retail.
Accumulation is when a whale builds a position over time, often trying to minimize price impact.
DCA pattern (Dollar-Cost Averaging):
Dip buying:
Multi-wallet accumulation:
Detection heuristics:
accumulation_score = 0
if buy_count > sell_count * 2: accumulation_score += 2
if avg_buy_size > avg_sell_size: accumulation_score += 1
if position_change_7d > 0: accumulation_score += 1
if buys_during_dips > buys_on_pump: accumulation_score += 2
if dca_pattern_detected: accumulation_score += 2
# Score >= 4 = likely accumulating
Distribution is when a whale reduces or exits a position, often gradually to avoid crashing the price.
Gradual selling:
Transfer to exchange:
Rapid exit:
Detection heuristics:
distribution_score = 0
if sell_count > buy_count * 2: distribution_score += 2
if position_change_7d < 0: distribution_score += 1
if transfers_to_exchanges > 0: distribution_score += 3
if sell_frequency_increasing: distribution_score += 2
if position_pct_remaining < 50: distribution_score += 1
# Score >= 4 = likely distributing
Maintain a watchlist of wallets worth tracking. Sources for discovering whale wallets:
getTokenLargestAccounts for any token of interestEach watchlist entry should track:
whale_entry = {
"address": "WhaLe...",
"label": "Smart money #47", # Human-readable label
"discovered": "2026-01-15", # When added to watchlist
"discovery_reason": "top_trader", # How they were found
"win_rate": 0.72, # Historical trade win rate
"avg_pnl": 3.4, # Average PnL multiplier
"tokens_tracked": 12, # Number of tokens held
"last_active": "2026-03-09", # Last on-chain activity
"tags": ["dex_trader", "sniper"], # Classification tags
}
| Tag | Description |
|-----|-------------|
| sniper | Buys tokens within minutes of launch |
| dex_trader | Primarily trades on DEXes |
| accumulator | Builds positions gradually |
| flipper | Short hold times, quick profit-taking |
| insider | Connected to token teams (funded by deployer) |
| fund | Institutional or fund wallet |
| market_maker | Provides liquidity, two-sided flow |
Whale tracking becomes most powerful when you analyze whale behavior across multiple tokens simultaneously.
When multiple tracked whales buy the same token independently, the signal strength compounds:
| Whale Count | Signal | Confidence |
|-------------|--------|------------|
| 1 whale buying | Informational | Low |
| 2-3 whales buying | Notable | Medium |
| 4+ whales buying | Strong convergence | High |
| Whales + volume spike | Confirmed momentum | Very high |
Track where whale capital flows:
Token A (selling) → SOL → Token B (buying)
If multiple whales rotate from A to B:
- Bearish for Token A (smart money exiting)
- Bullish for Token B (smart money entering)
Whale tracking on Solana uses multiple data sources. See references/data_sources.md for complete details.
| Source | Use Case | Auth Required |
|--------|----------|---------------|
| Helius | Transaction history, webhooks | Yes (free tier available) |
| SolanaTracker | Top traders, wallet PnL | Yes |
| Birdeye | Token holder rankings | Yes (free tier available) |
| Solana RPC | Token accounts, signatures | No (public endpoints) |
Helius webhooks enable real-time whale alerts without polling:
# Webhook configuration for whale wallet monitoring
webhook_config = {
"webhookURL": "https://your-server.com/whale-alerts",
"transactionTypes": ["SWAP", "TRANSFER"],
"accountAddresses": [
"WhaLe1...", # Tracked whale wallets
"WhaLe2...",
],
"webhookType": "enhanced", # Parsed transaction data
}
Convert whale activity into trading signals. Whale signals are one input to a broader decision framework, not standalone trading triggers.
Whale Buy Signal:
Whale Sell Signal:
Accumulation Signal:
Distribution Signal:
def compute_whale_signal(whale_activity: dict) -> dict:
"""Combine whale activity indicators into a composite signal."""
score = 0.0
# Trade direction: +1 for buy, -1 for sell
direction = 1 if whale_activity["is_buy"] else -1
# Size factor: larger trades = stronger signal
size_sol = whale_activity["size_sol"]
if size_sol > 500:
score += direction * 3
elif size_sol > 100:
score += direction * 2
else:
score += direction * 1
# Whale quality: better track record = stronger signal
win_rate = whale_activity["whale_win_rate"]
score *= (0.5 + win_rate) # 0.5x to 1.5x multiplier
# Convergence: multiple whales = stronger signal
whale_count = whale_activity["concurrent_whale_count"]
score *= (1 + 0.3 * (whale_count - 1))
return {
"score": round(score, 2),
"direction": "bullish" if score > 0 else "bearish",
"confidence": "high" if abs(score) > 5 else "medium" if abs(score) > 2 else "low",
}
Whale tracking works best when combined with other analysis:
| Skill | Integration |
|-------|-------------|
| token-holder-analysis | Identify concentration risk, insider wallets |
| liquidity-analysis | Estimate price impact of whale trades |
| solana-onchain | Wallet profiling, transaction history |
| slippage-modeling | Predict slippage for whale-sized trades |
| risk-management | Factor whale concentration into position sizing |
| helius-api | Transaction fetching, webhook setup |
references/detection_methods.md — Accumulation/distribution detection algorithms, whale classification, alert thresholds and scoring systemsreferences/data_sources.md — Complete guide to Helius, SolanaTracker, Birdeye, and on-chain data sources for whale trackingscripts/track_whales.py — Fetches top holders for a token, classifies whale activity as accumulating/distributing/holding, prints a whale report. Run with --demo for synthetic data mode.scripts/whale_alerts.py — Monitors a watchlist of whale wallets for new large transactions, classifies trades, and prints alerts. Run with --demo for simulated whale trades.Automatically organizes invoices and receipts for tax preparation by reading messy files, extracting key information, renaming them consistently, and sorting them into logical folders. Turns hours of manual bookkeeping into minutes of automated organization.
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