wallet-profiling

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Wallet Profiling

钱包分析

Behavioral classification, performance analysis, and trading style detection for Solana wallets. Profile any wallet to understand how it trades, how well it performs, and whether it is worth following.

针对Solana钱包的行为分类、表现分析及交易风格检测。分析任意钱包以了解其交易方式、表现水平及是否值得跟进。

Why Wallet Profiling Matters

钱包分析的重要性

Copy-Trade Evaluation

跟单交易评估

Before mirroring another wallet's trades, you need evidence that its historical performance is genuine, consistent, and not the result of a single lucky hit. Profiling quantifies win rate, profit factor, hold time, and consistency so you can make informed decisions about which wallets merit attention.

在镜像其他钱包的交易之前，你需要证据证明其历史表现真实、稳定，而非单次运气爆棚的结果。分析会量化胜率、利润因子、持有时间和稳定性，让你能够明智地决定哪些钱包值得关注。

Smart Money Identification

聪明资金识别

Wallets that consistently buy tokens early and exit profitably are signal sources. Profiling separates genuinely skilled traders from lucky gamblers and wash-trading bots. Key differentiators: sustained profit factor above 2.0, win rates above 45% across 100+ trades, and diversified token selection.

持续早期买入代币并盈利退出的钱包是信号来源。分析能够将真正熟练的交易者与运气好的赌徒和洗盘交易机器人区分开来。关键差异点：利润因子持续高于2.0，100+笔交易的胜率高于45%，以及多元化的代币选择。

Counterparty Analysis

对手方分析

When a large wallet enters a position you hold, understanding its historical behavior (sniper vs. holder, bot vs. human) helps you anticipate what will happen next. A sniper wallet buying suggests a quick dump is coming; a swing trader buying suggests multi-day conviction.

当一个大额钱包进入你持有的头寸时，了解其历史行为（狙击者 vs 持有者，机器人 vs 人类）有助于你预测后续走势。狙击者钱包买入意味着很快会抛售；波段交易者买入则表明其有多天的持仓信心。

Risk Assessment

风险评估

Token holder analysis benefits from knowing whether top holders are bots, snipers, or genuine investors. A token where 60% of holders are classified as snipers has very different risk characteristics than one held primarily by swing traders.

代币持有者分析需要了解顶级持有者是机器人、狙击者还是真正的投资者。一个60%持有者被归类为狙击者的代币，与主要由波段交易者持有的代币相比，风险特征截然不同。

Wallet Classification

钱包分类

By Trading Style

按交易风格分类

Classification is based on the median hold time across all closed trades:

Style	Median Hold Time	Characteristics
Sniper	< 5 minutes	First-block buyers, MEV-adjacent, extremely fast exits
Scalper	5 min – 1 hour	Quick momentum trades, high frequency
Day Trader	1 – 24 hours	Intraday positions, moderate frequency
Swing Trader	1 – 7 days	Multi-day conviction holds
Position Holder	> 7 days	Long-term accumulation, low frequency

See

references/classification_methods.md

for the full classification algorithm.

分类基于所有已平仓交易的中位持有时间：

风格	中位持有时间	特征
狙击交易者	< 5分钟	首块买入者，接近MEV，极快退出
剥头皮交易者	5分钟 – 1小时	快速动量交易，高频率
日内交易者	1 – 24小时	日内头寸，中等频率
波段交易者	1 – 7天	多天信心持仓
长期持仓者	> 7天	长期积累，低频率

查看

references/classification_methods.md

获取完整分类算法。

By Trade Size

按交易规模分类

Based on median trade size in SOL:

Tier	Median Trade Size	Typical Behavior
Whale	> 100 SOL	Market-moving entries, often front-run
Large	10 – 100 SOL	Significant but not dominant
Medium	1 – 10 SOL	Active retail traders
Small	< 1 SOL	Micro-cap gamblers, new wallets

基于以SOL计价的中位交易规模：

层级	中位交易规模	典型行为
巨鲸	> 100 SOL	影响市场的入场，常被抢先交易
大额	10 – 100 SOL	规模可观但非主导
中等	1 – 10 SOL	活跃的零售交易者
小额	< 1 SOL	微市值赌徒，新钱包

By Behavior Type

按行为类型分类

Type	Detection Method
Bot	Low inter-trade timing variance (CV < 0.3), uniform sizing
Human	Variable timing, variable sizing, session-based activity
MEV	Sandwich patterns, consistent small profits, high frequency

类型	检测方法
机器人	交易间隔时间方差低（CV < 0.3），规模统一
人类	时间间隔可变，规模可变，基于时段的活动
MEV	三明治模式，持续小额利润，高频率

By Focus Area

按关注领域分类

Focus	Detection Criteria
PumpFun Specialist	> 70% of trades on PumpFun-launched tokens
DEX Trader	Primarily swaps on Raydium/Orca/Meteora
DeFi Farmer	Frequent LP add/remove, staking operations
NFT Trader	Significant NFT marketplace interactions
Multi-Strategy	No single category exceeds 50%

关注领域	检测标准
PumpFun专精者	>70%的交易针对PumpFun发行的代币
DEX交易者	主要在Raydium/Orca/Meteora进行兑换
DeFi挖矿者	频繁添加/移除流动性，质押操作
NFT交易者	大量NFT市场交互
多策略交易者	无单一类别占比超过50%

Performance Metrics

表现指标

Core Metrics

核心指标

Win Rate — Percentage of trades that are profitable.

win_rate = count(pnl > 0) / count(all_closed_trades)

Minimum 30 trades for statistical significance. A 60% win rate across 200 trades is far more meaningful than 80% across 10 trades.

Average ROI Per Trade — Mean return across all closed positions.

avg_roi = mean((exit_value - entry_value) / entry_value)

Include all fees: platform fees, priority fees, and estimated slippage.

Profit Factor — Ratio of gross profits to gross losses.

profit_factor = sum(winning_pnl) / abs(sum(losing_pnl))

Interpretation: > 2.0 excellent, 1.5–2.0 good, 1.0–1.5 marginal, < 1.0 losing.

Total PnL — Cumulative profit/loss in SOL.

total_pnl = sum(all_trade_pnl)

Maximum Drawdown — Largest peak-to-trough decline in cumulative PnL curve.

drawdown = (peak_equity - trough_equity) / peak_equity

Sharpe-Like Ratio — Risk-adjusted return metric.

sharpe = mean(trade_returns) / std(trade_returns) * sqrt(trades_per_year)

See

references/performance_metrics.md

for detailed formulas, edge cases, and interpretation guidelines.

胜率 — 盈利交易的百分比。

win_rate = count(pnl > 0) / count(all_closed_trades)

最少30笔交易才有统计显著性。200笔交易中60%的胜率远比10笔交易中80%的胜率更有意义。

每笔交易平均ROI — 所有已平仓头寸的平均回报率。

avg_roi = mean((exit_value - entry_value) / entry_value)

包含所有费用：平台费、优先费和预估滑点。

利润因子 — 总盈利与总亏损的比率。

profit_factor = sum(winning_pnl) / abs(sum(losing_pnl))

解读：>2.0为优秀，1.5–2.0为良好，1.0–1.5为边际，<1.0为亏损。

总PnL — 以SOL计价的累计盈利/亏损。

total_pnl = sum(all_trade_pnl)

最大回撤 — 累计PnL曲线中最大的峰值到谷底跌幅。

drawdown = (peak_equity - trough_equity) / peak_equity

类夏普比率 — 风险调整后的回报指标。

sharpe = mean(trade_returns) / std(trade_returns) * sqrt(trades_per_year)

查看

references/performance_metrics.md

获取详细公式、边缘案例及解读指南。

Activity Metrics

活动指标

Metric	Calculation	What It Reveals
Trades per day	total_trades / active_days	Activity level and capacity
Average hold time	mean(exit_time - entry_time)	Trading style confirmation
Token diversity	unique_tokens / total_trades	Specialization vs. diversification
Peak hours	mode(hour_of_trade)	Session patterns, timezone hints
Activity streaks	consecutive active days	Dedication and consistency

指标	计算方式	揭示信息
每日交易数	total_trades / active_days	活动水平和容量
平均持有时间	mean(exit_time - entry_time)	交易风格确认
代币多样性	unique_tokens / total_trades	专精 vs 多元化
峰值时段	mode(hour_of_trade)	时段模式，时区线索
活动连续天数	consecutive active days	投入度和稳定性

Data Sources

数据源

SolanaTracker PnL API (Primary)

SolanaTracker PnL API（主要）

The SolanaTracker API provides pre-computed PnL data per wallet per token.

python

import httpx

url = f"https://data.solanatracker.io/pnl/{wallet_address}"
headers = {"x-api-key": os.getenv("ST_API_KEY")}
resp = httpx.get(url, headers=headers)
pnl_data = resp.json()

Response includes per-token:

realized

unrealized

total_invested

total_sold

num_buys

num_sells

last_trade_time

SolanaTracker API提供每个钱包每个代币的预计算PnL数据。

python

import httpx

url = f"https://data.solanatracker.io/pnl/{wallet_address}"
headers = {"x-api-key": os.getenv("ST_API_KEY")}
resp = httpx.get(url, headers=headers)
pnl_data = resp.json()

响应包含每个代币的：

realized

、

unrealized

、

total_invested

、

total_sold

、

num_buys

、

num_sells

、

last_trade_time

。

Helius Parsed Transactions (Detailed)

Helius解析交易（详细）

For granular transaction-level analysis, use the

helius-api

skill to fetch parsed transaction history. This provides exact timestamps, amounts, and program interactions.

如需粒度级交易分析，使用

helius-api

技能获取解析后的交易历史。这提供了精确的时间戳、金额和程序交互信息。

Birdeye Trader Data

Birdeye交易者数据

Birdeye's trader endpoints provide wallet-level analytics. See the

birdeye-api

skill for endpoint details.

Birdeye的交易者端点提供钱包级分析。查看

birdeye-api

技能获取端点详情。

DexScreener (Free Fallback)

DexScreener（免费备选）

DexScreener does not provide wallet-level PnL but can be used to validate token prices at trade timestamps.

DexScreener不提供钱包级PnL，但可用于验证交易时点的代币价格。

Copy-Trade Evaluation Framework

跟单交易评估框架

Before following a wallet's trades, verify these criteria:

在跟进钱包交易之前，验证以下标准：

Minimum Requirements

最低要求

Trade history: At least 50 closed trades (100+ preferred)
Time span: Active for at least 30 days
Consistent performance: Rolling 7-day win rate standard deviation < 15%
Reasonable sizing: No single trade > 20% of observed portfolio
Diverse tokens: At least 10 unique tokens traded

交易历史：至少50笔已平仓交易（推荐100+笔）
时间跨度：活跃至少30天
稳定表现：滚动7天胜率标准差 <15%
合理规模：无单笔交易占观察到的投资组合的20%以上
多元化代币：至少交易10种独特代币

Green Flags

积极信号

Profit factor > 1.8 sustained over 60+ days
Win rate 45–65% (unrealistically high rates suggest wash trading)
Moderate trade frequency (2–20 trades/day)
Mixed hold times indicating adaptive strategy
Gradual equity curve growth (not step-function jumps)

60+天内利润因子持续>1.8
胜率45–65%（过高的胜率可能暗示洗盘交易）
中等交易频率（每天2–20笔交易）
混合持有时间表明策略适应性强
权益曲线逐步增长（而非阶跃式跳跃）

Red Flags

负面信号

New wallet (< 14 days old): Possible sybil or one-hit-wonder
Single big win: One trade accounts for > 50% of total PnL
Declining performance: Last-30-day metrics significantly below all-time
Bot-like patterns: Uniform timing/sizing without proportional edge
Extreme win rate: > 80% often indicates small wins with catastrophic losses
Concentration: > 50% of PnL from a single token
Wash trading signals: Repeated buy/sell of same token with minimal price movement

新钱包（<14天）：可能是女巫攻击或单次幸运者
单次大额盈利：单笔交易占总PnL的50%以上
表现下滑：最近30天指标显著低于历史水平
机器人模式：统一时间/规模但无相应优势
极高胜率：>80%通常表明小额盈利伴随灾难性亏损
集中度高：>50%的PnL来自单一代币
洗盘交易信号：重复买卖同一代币且价格波动极小

Risk Score Calculation

风险评分计算

python

risk_score = 0  # 0 = low risk, 100 = high risk

if wallet_age_days < 14:
    risk_score += 25
if top_trade_pnl_pct > 0.5:
    risk_score += 20
if recent_pf < historical_pf * 0.7:
    risk_score += 15
if bot_probability > 0.7:
    risk_score += 15
if win_rate > 0.8:
    risk_score += 10
if unique_tokens < 5:
    risk_score += 15

python

risk_score = 0  # 0 = 低风险，100 = 高风险

if wallet_age_days < 14:
    risk_score += 25
if top_trade_pnl_pct > 0.5:
    risk_score += 20
if recent_pf < historical_pf * 0.7:
    risk_score += 15
if bot_probability > 0.7:
    risk_score += 15
if win_rate > 0.8:
    risk_score += 10
if unique_tokens < 5:
    risk_score += 15

Integration with Other Skills

与其他技能集成

whale-tracking
: Identify large wallets, then profile them here for behavioral context
token-holder-analysis
: Profile top holders of a token to assess holder quality
solana-onchain
: Fetch raw transaction data for deep-dive analysis
helius-api
: Parsed transaction history for granular trade reconstruction
birdeye-api
: Token price data for PnL validation

whale-tracking
：识别大额钱包，然后在此处分析其行为背景
token-holder-analysis
：分析代币的顶级持有者以评估持有者质量
solana-onchain
：获取原始交易数据以进行深度分析
helius-api
：解析后的交易历史用于粒度级交易重构
birdeye-api
：代币价格数据用于PnL验证

Quick Start

快速开始

Profile a Single Wallet

分析单个钱包

python

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python

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Set environment variables

设置环境变量

export WALLET_ADDRESS=YourTargetWallet...

export ST_API_KEY=your_solanatracker_key (optional)

export ST_API_KEY=your_solanatracker_key (可选)

python scripts/profile_wallet.py

Or use demo mode:

或使用演示模式:

python scripts/profile_wallet.py --demo

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python scripts/profile_wallet.py --demo

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Compare Multiple Wallets

对比多个钱包

python

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python

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export WALLET_ADDRESSES=Wallet1...,Wallet2...,Wallet3...

export ST_API_KEY=your_solanatracker_key (optional)

export ST_API_KEY=your_solanatracker_key (可选)

python scripts/compare_wallets.py

Or use demo mode:

或使用演示模式:

python scripts/compare_wallets.py --demo

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python scripts/compare_wallets.py --demo

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Files

文件说明

File	Description
`references/classification_methods.md`	Hold time, size, bot detection, and focus classification algorithms
`references/performance_metrics.md`	Detailed metric formulas, interpretation, edge cases, and decay detection
`scripts/profile_wallet.py`	Profile a single wallet: fetch data, compute metrics, classify, report
`scripts/compare_wallets.py`	Compare multiple wallets side-by-side with ranking

文件	描述
`references/classification_methods.md`	持有时间、规模、机器人检测及关注领域分类算法
`references/performance_metrics.md`	详细指标公式、解读、边缘案例及衰退检测
`scripts/profile_wallet.py`	分析单个钱包：获取数据、计算指标、分类、生成报告
`scripts/compare_wallets.py`	对比多个钱包并排名

Dependencies

依赖

bash

uv pip install httpx

bash

uv pip install httpx

Environment Variables

环境变量

Variable	Required	Description
`WALLET_ADDRESS`	For profile_wallet.py	Solana wallet address to profile
`WALLET_ADDRESSES`	For compare_wallets.py	Comma-separated wallet addresses
`ST_API_KEY`	No	SolanaTracker API key for PnL data

变量	是否必填	描述
`WALLET_ADDRESS`	用于profile_wallet.py	要分析的Solana钱包地址
`WALLET_ADDRESSES`	用于compare_wallets.py	逗号分隔的钱包地址
`ST_API_KEY`	否	SolanaTracker API密钥，用于获取PnL数据