copy-trading

name	copy-trading
description	Wallet evaluation, monitoring, and copy-trade strategy design for Solana DEX trading

Copy Trading

Wallet evaluation, monitoring, and copy-trade strategy design for Solana DEX trading. Identify profitable wallets on-chain, evaluate whether their edge is real and replicable, monitor their activity in real time, and execute proportionally-sized trades with independent risk controls.

What Copy Trading Means on Solana

Copy trading is the practice of monitoring one or more wallets that have demonstrated consistent profitability and replicating their trades in your own wallet. On Solana, every DEX swap is publicly visible on-chain within seconds, making it technically feasible to detect and follow any wallet's activity.

How It Differs from TradFi Copy Trading

Dimension	TradFi (eToro, etc.)	Solana On-Chain
Data source	Platform-reported P&L	Verifiable on-chain transactions
Latency	Minutes to hours	Seconds (websocket) to sub-second (gRPC)
Front-running risk	Low	High (MEV bots, sandwich attacks)
Trade cost	Commissions + spread	Gas + slippage + priority fees
Capacity	High (large-cap equities)	Low (micro-cap tokens have thin liquidity)
Signal decay	Slow	Fast (PumpFun tokens move in minutes)
Transparency	Partial (delayed reporting)	Full (every transaction is public)

The core tradeoff: Solana provides perfect transparency but introduces execution risk. The wallet you copy got a price that no longer exists by the time you trade.

The Copy-Trade Pipeline

Stage 1 — Discovery

Find wallets with strong track records. Sources include:

• SolanaTracker Top Traders: GET /top-traders/{token} returns the highest-PnL wallets for any token
• Birdeye Trader Rankings: wallet-level P&L leaderboards by token or globally
• On-chain leaderboards: community-built dashboards (GMGN, Cielo, Arkham)
• Social signals: wallets shared on Twitter/X or Telegram alpha groups
• Your own analysis: run token-holder-analysis on a token that performed well, then profile the top holders

See references/wallet_discovery.md for detailed source documentation and scoring methodology.

Stage 2 — Evaluation

Every discovered wallet must pass quantitative evaluation before it enters a copy list. Use the wallet-profiling skill for deep behavioral analysis, then apply copy-trade-specific criteria.

Minimum thresholds:

Metric	Minimum	Why
Trade count (30d)	>= 50	Statistical significance
Win rate	>= 55%	Edge above random
Profit factor	>= 1.5	Wins meaningfully exceed losses
Last active	Within 7 days	Still trading, not abandoned
Distinct tokens traded	>= 10	Not a one-token wonder
Max single-trade % of total PnL	< 40%	Not reliant on one lucky hit
Bot probability	< 30%	Human-like timing patterns

Run scripts/evaluate_wallet.py for a comprehensive copy-trade suitability assessment.

Stage 3 — Filtering

After evaluation, apply additional filters:

• Consistency check: rolling 7-day win rate should not swing below 40% in any period
• Style compatibility: understand whether the wallet is a sniper, scalper, or swing trader — your infrastructure must match their speed
• Size compatibility: if they trade 500 SOL per position and you have 10 SOL total, proportional sizing may be too small to cover fees
• Sybil check: use the sybil-detection skill to verify the wallet is not part of a wash-trading cluster

Stage 4 — Monitoring

Once a wallet passes evaluation and filtering, set up real-time monitoring.

Monitoring approaches (fastest to simplest):

1. Yellowstone gRPC: sub-second latency, streams all transactions for subscribed wallets
2. Helius Enhanced WebSocket: near-real-time with parsed transaction data
3. Polling via RPC: getSignaturesForAddress every 5-10 seconds — simple but slower

See references/execution_strategy.md for implementation details on each approach.

Stage 5 — Execution

When a monitored wallet executes a swap:

1. Detect the transaction (via monitoring infrastructure)
2. Parse the trade: token address, direction (buy/sell), size
3. Validate the token: check liquidity, holder distribution, honeypot risk
4. Size the position: proportional to your portfolio, not theirs
5. Execute via Jupiter aggregator with appropriate slippage tolerance
6. Record the copy trade with attribution to the source wallet

Stage 6 — Risk Management

Copy trades require independent risk controls that do not depend on the copied wallet's behavior.

See references/risk_framework.md for the complete framework.

Key limits:

Control	Recommended Value	Purpose
Max allocation per wallet	10-20% of portfolio	Diversification across signal sources
Max concurrent copy positions	3-5	Prevent overexposure
Per-trade stop loss	-15% to -25%	Independent downside protection
Daily copy-trade loss limit	-5% of portfolio	Circuit breaker
Weekly copy-trade loss limit	-10% of portfolio	Longer-term circuit breaker

Wallet Scoring for Copy Suitability

Composite score from 0-100 based on weighted criteria:

copy_score = (
    trade_count_score * 0.15 +
    win_rate_score * 0.20 +
    profit_factor_score * 0.25 +
    consistency_score * 0.20 +
    recency_score * 0.10 +
    human_probability_score * 0.10
)

Component calculations:

• Trade count score: min(trade_count / 200, 1.0) * 100 — maxes out at 200 trades
• Win rate score: max((win_rate - 0.40) / 0.30, 0) * 100 — scaled from 40% to 70%
• Profit factor score: min((pf - 1.0) / 3.0, 1.0) * 100 — scaled from 1.0 to 4.0
• Consistency score: (1.0 - std_dev_of_rolling_win_rate) * 100 — lower variance = higher score
• Recency score: max(1.0 - days_since_last_trade / 14, 0) * 100 — decays over 14 days
• Human probability score: (1.0 - bot_probability) * 100

Interpretation:

Score	Rating	Action
80-100	Excellent	Strong copy-trade candidate
60-79	Good	Suitable with monitoring
40-59	Marginal	Proceed with caution, reduce allocation
0-39	Poor	Do not copy

Position Sizing for Copy Trades

Three approaches, from simplest to most nuanced:

Fixed Amount

Use a constant SOL amount per copy trade (e.g., 0.5 SOL). Simple but ignores the wallet's conviction level.

Proportional

Match the copied wallet's allocation as a percentage of their estimated portfolio:

your_size = (their_trade_size / their_estimated_portfolio) * your_portfolio

Requires estimating their total portfolio, which can be imprecise.

Confidence-Scaled

Base amount multiplied by your confidence in the wallet:

your_size = base_amount * (copy_score / 100) * conviction_multiplier

Where conviction_multiplier is higher for wallets with longer track records.

Anti-Patterns to Avoid

Copying Bots

Bots have sub-second execution and often use MEV strategies. You cannot match their latency. By the time you detect their trade, the opportunity is gone or you become the exit liquidity. Use the bot probability score to filter these out.

Survivorship Bias

A wallet with 1000% returns from one PumpFun token is not necessarily skilled. Look for wallets with consistent performance across many tokens, not outlier wins. The max-single-trade-PnL filter catches this.

Blind Following

Never copy a trade without understanding what the token is. At minimum, run basic safety checks: liquidity depth, holder concentration, contract verification. A 2-second check can prevent buying a honeypot.

No Independent Exits

The copied wallet may have information you do not have. They may exit for reasons unrelated to the trade. Always maintain your own stop loss. Never rely solely on mirroring their exit.

Correlation Risk

If you copy 5 wallets and they all buy the same token, you have 5x the intended exposure. Track aggregate position across all copy sources and enforce portfolio-level limits.

Ignoring Capacity

A wallet profiting on tokens with $50K daily volume cannot be copied at scale. If your trade is 10% of daily volume, you will move the price against yourself.

Integration with Other Skills

Skill	How It Integrates
wallet-profiling	Deep behavioral analysis of candidate wallets
sybil-detection	Verify wallet is not part of a wash-trading ring
token-holder-analysis	Safety check tokens before copying a buy
liquidity-analysis	Verify sufficient liquidity to enter/exit
helius-api	WebSocket monitoring and transaction parsing
jupiter-api / jupiter-swap	Trade execution via aggregator
slippage-modeling	Estimate execution cost of the copy trade
position-sizing	Portfolio-aware sizing for copy positions
risk-management	Portfolio-level risk controls

Files

References

• references/wallet_discovery.md — Sources and methods for finding copy-trade candidates
• references/execution_strategy.md — Monitoring infrastructure and execution approaches
• references/risk_framework.md — Portfolio-level risk controls for copy trading

Scripts

• scripts/evaluate_wallet.py — Comprehensive copy-trade suitability scoring for a wallet
• scripts/monitor_wallet.py — Real-time wallet transaction monitoring with trade alerts