gmgn-skills/docs/workflow-smart-money-profile.md

Smart Money Profile — Behavior Analysis Workflow

When a user wants to understand a wallet's trading behavior in depth: what style they trade, when they take profit, when they cut losses, and whether copying them would be profitable.

Use this workflow when:

• "is this wallet a long-term holder or a short-term trader?"
• "what is this wallet's win rate, when does it take profit or cut losses?"
• "if I copied this wallet, what would my return be?"
• "smart money leaderboard, which wallets are most worth following?"
• User provides a wallet address and asks about trading style or copy-trade potential

For basic "is this wallet worth following" analysis, see workflow-wallet-analysis.md. This workflow goes deeper into behavior patterns and copy-trade estimation.

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Step 1 — Trading Stats (Both Periods)

Run stats for both 7d and 30d to detect performance trends:

gmgn-cli portfolio stats --chain <chain> --wallet <address> --period 7d
gmgn-cli portfolio stats --chain <chain> --wallet <address> --period 30d

Key metrics:

Field	Meaning	Threshold
winrate	% of profitable trades (0–1)	> 0.6 strong, > 0.5 acceptable
pnl	realized_profit / total_cost multiplier	> 1.0 = net positive
realized_profit	USD profit locked in	context-dependent
buy_count / sell_count	trading frequency	high = active trader
token_num	number of distinct tokens traded	high = diversified

Trend signal: If 7d winrate is significantly higher than 30d, performance is improving. If lower, recent form is declining.

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Step 2 — Activity Analysis (Style Inference)

gmgn-cli portfolio activity --chain <chain> --wallet <address> --limit 100

For each token that appears in both a buy and a sell event, compute holding duration:

• sell.timestamp - buy.timestamp in hours

Style classification:

Holding Duration	Style Label
< 1 hour	Scalper
1h – 24h	Day trader
1d – 7d	Swing trader
> 7d	Position / long-term holder

Also check:

• Position sizing consistency — are buy amounts roughly similar (disciplined) or highly variable?
• Token concentration — does the wallet repeatedly trade the same tokens (specialist) or always new ones (trend chaser)?
• Sell behavior — do sells follow a pattern (e.g., always sells after 2–3x, or cuts at -30%)?

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Step 3 — Take-Profit and Stop-Loss Pattern

From portfolio activity, cross-reference buy price vs sell price for completed round trips:

• For each token: find a buy event followed by a sell event
• Compute approximate return: (sell_total_usd - buy_total_usd) / buy_total_usd
• Group outcomes: wins vs losses

Look for:

• Typical gain at exit — does the wallet consistently take profit at ~2x, ~5x, or higher?
• Typical loss at cut — does the wallet cut quickly at -20% or hold through large drawdowns?
• Asymmetry — wins larger than losses = positive expected value. Reverse = risk.

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Step 4 — Copy-Trade ROI Estimation (Approximate)

Note: This is an approximation based on historical activity data, not a precise backtest.

For the wallet's last 20–30 completed trades (round-trip buys + sells):

1. List all buy events: token, amount_usd, timestamp
2. List all sell events for the same tokens
3. Compute per-trade return: (sell_usd - buy_usd) / buy_usd
4. Average the returns

If you want to estimate "if I had followed today": For still-open positions (buy with no matching sell), use portfolio holdings to get current usd_value vs cost, computing unrealized return.

Present as:

Copy-trade estimate (last 30d completed trades):
  Avg return per trade: +X%
  Win rate:             X / Y trades profitable
  Best trade:           +X% on TOKEN
  Worst trade:          -X% on TOKEN
  Approximate 30d return if equal-weight copy: ~X%
⚠️ This is an approximation. Actual results depend on entry timing, slippage, and fees.

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Step 5 — Smart Money Leaderboard (Multi-Wallet Comparison)

When the user wants to compare multiple smart money wallets:

# Batch stats — compare up to 10 wallets at once
gmgn-cli portfolio stats --chain <chain> \
  --wallet <addr1> --wallet <addr2> --wallet <addr3> \
  --period 30d

Rank wallets by composite score. Suggested weights:

• winrate × 40%
• pnl × 40%
• token_num (diversity) × 10%
• Recency (7d winrate vs 30d winrate improvement) × 10%

To discover active smart money wallets to compare, first run:

gmgn-cli track smartmoney --chain <chain>

Extract unique wallet addresses from the results, then batch-query their stats.

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Output Template

Smart Money Profile: {short_address}
Chain: {chain} | Data: 7d + 30d

─── Performance ────────────────────────────
Win Rate (7d / 30d):  {X}% / {X}%     [trend: ↑ improving / ↓ declining / → stable]
PnL Ratio (30d):      {X}x
Realized Profit (30d): ${X}

─── Trading Style ──────────────────────────
Style:          Scalper / Day trader / Swing trader / Long-term holder
Avg Hold Time:  ~{X} hours / days
Position Size:  Consistent (disciplined) / Variable (opportunistic)
Token Focus:    Specialist (repeats tokens) / Trend chaser (always new)

─── Exit Behavior ──────────────────────────
Typical take-profit: ~+{X}% gain
Typical stop-loss:   ~-{X}% loss
Win/loss ratio:      {avg_win}x / {avg_loss}x

─── Copy-Trade Estimate ────────────────────
Approx. 30d return if copied: ~{X}%
Based on {N} completed trades
⚠️ Approximation only

─── Verdict ────────────────────────────────
🟢 High-conviction follow — strong stats, consistent style, favorable exit pattern
🟡 Selective follow — good stats but inconsistent or high-risk behavior
🔴 Avoid copying — low win rate, poor exit discipline, or declining form

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Related Workflows

• workflow-wallet-analysis.md — general wallet quality assessment
• workflow-token-research.md — deep dive on tokens this wallet holds