Daily Alpha Scanner

Daily Alpha Scanner

A systematic 5-step pipeline that scans on-chain data across multiple chains and produces a consolidated briefing with purchase recommendations.

Pipeline

Step 1: Hot Tokens    →  Step 2: Smart Money   →  Step 3: Meme Scan
     ↓                        ↓                        ↓
Step 4: Security Audit (batch scan all candidates)
     ↓
Step 5: Consolidated Briefing + Purchase Recommendations

Prerequisites

Verify before starting:

which onchainos && onchainos --version

If missing: curl -sSL https://raw.githubusercontent.com/okx/onchainos-skills/main/install.sh | sh

Required env vars: OKX_API_KEY, OKX_SECRET_KEY, OKX_PASSPHRASE (Web3 API key from https://web3.okx.com/onchainos/dev-portal)

Execution

User Parameters

Ask the user before starting (or use defaults):

If the user just says "扫一下" or "daily scan" without parameters, use defaults (Solana + Base, all narratives, medium risk).

Step 1: Hot Token Discovery

Goal: Find trending tokens across target chains, categorize by narrative.

1.1 Fetch Trending Tokens

Run in parallel for each target chain:

# Trending by OKX trending score (hot-tokens ranking)
onchainos token hot-tokens --chain <chainId> --limit 20

# Volume-based trending (complementary view)
onchainos token trending --chains <chainIds> --sort-by 5 --time-frame 4 --limit 30

1.2 Extract & Categorize

From the results, extract for each token:

• tokenSymbol, tokenContractAddress, chainIndex
• price, change (24h %), volume, marketCap, liquidity
• holders, top10HoldPercent, bundleHoldPercent, devHoldPercent
• riskLevelControl (1=low, 2=medium, 3=high)

Categorize by narrative based on token name, symbol, and tags:

• AI/Agent: tokens related to AI, agents, virtual, autonomous
• Meme: animal names, cultural references, ironic/funny names
• DeFi: DEX tokens, lending, yield, staking
• Infra: L1/L2, bridges, oracles, tooling
• BTC-eco: BTC wrappers, ordinals, BTC-related
• Political: political figures, events
• Stablecoin/Yield: USD-pegged, yield-bearing stablecoins

1.3 Filter Criteria

Remove tokens that are obvious noise:

• Top10 concentration > 80% (extreme whale control)
• Market cap < $5K (dust)
• Volume/MCap ratio > 100x (likely bot wash trading)
• Zero holders or zero liquidity

Keep a shortlist of top 15-20 interesting tokens for further analysis.

Step 2: Smart Money Signal Scan

Goal: Identify what smart money, KOLs, and whales are actively buying.

2.1 Aggregated Buy Signals

Run in parallel for each target chain:

# Smart money buy signals
onchainos signal list --chain <chainId> --wallet-type 1 --limit 10

# KOL buy signals
onchainos signal list --chain <chainId> --wallet-type 2 --limit 10

# Whale buy signals
onchainos signal list --chain <chainId> --wallet-type 3 --limit 10

2.2 Extract Signal Data

For each signal entry:

• token.symbol, token.tokenAddress, token.marketCapUsd
• triggerWalletCount (number of smart money addresses buying)
• amountUsd (total buy amount)
• soldRatioPercent (how much they've already sold — key metric!)
• token.top10HolderPercent, token.holders

2.3 Signal Quality Scoring

Rate each signal:

• Strong: 5+ wallets buying, soldRatio < 30%, marketCap > $100K
• Medium: 3+ wallets buying, soldRatio < 60%
• Weak: 1-2 wallets, soldRatio > 70% (already exiting)
• Exit signal: soldRatio > 90% (smart money dumping)

2.4 Cross-reference with Hot Tokens

Flag tokens that appear in BOTH Step 1 (trending) AND Step 2 (smart money buying) — these have the strongest alpha signal.

Step 3: Meme Coin Scan

Goal: Scan launchpads for new launches worth tracking.

3.1 New Token Scan

# Latest meme launches on Solana
onchainos memepump tokens --chain 501

# Latest meme launches on BSC (if in target chains)
onchainos memepump tokens --chain 56

3.2 Extract Key Metrics

For each new meme token:

• symbol, tokenAddress, bondingPercent (bonding curve progress)
• market.marketCapUsd, market.buyTxCount1h, market.sellTxCount1h
• tags.snipersPercent, tags.bundlersPercent
• tags.top10HoldingsPercent, tags.devHoldingsPercent, tags.freshWalletsPercent
• tags.totalHolders
• social.x (has Twitter?), social.website, social.telegram
• creatorAddress (for dev reputation lookup)

3.3 Developer Reputation Check

For promising tokens (survived bonding, has social presence):

onchainos memepump token-dev-info --address <tokenAddress> --chain <chainId>

Key fields:

• devCreateTokenCount — how many tokens this dev has created
• devRugPullTokenCount — how many rugged
• devLaunchedTokenCount — how many survived past bonding curve

3.4 Bundle/Sniper Detection

onchainos memepump token-bundle-info --address <tokenAddress> --chain <chainId>

3.5 Meme Scoring

Filter out garbage:

• Sniper % > 50% → likely bot-controlled → skip
• Top10 holdings > 60% → extreme concentration → skip
• 0 holders or < 3 holders → too early → skip
• No social presence at all → skip
• Dev has > 5 rug pulls → skip

Keep tokens that have:

• Survived bonding curve (migrated)
• Sniper < 20%, bundle < 10%
• Has at least Twitter presence
• Dev rug pull rate < 20%
• Growing holder count

Step 4: Security Audit

Goal: Batch security scan all candidate tokens from Steps 1-3.

4.1 Collect Candidates

Merge the shortlisted tokens from Steps 1, 2, and 3 (deduplicate by address). Max 10 tokens for batch scan.

4.2 Batch Token Security Scan

onchainos security token-scan --tokens "<chainId1>:<addr1>,<chainId2>:<addr2>,..."

Up to 10 tokens per batch. Key fields to check:

• riskLevel: LOW / MEDIUM / HIGH
• isHoneypot: true = INSTANT REJECT
• isMintable: true = can create infinite tokens
• isDumping: true = active sell pressure
• isFakeLiquidity: true = liquidity is fake
• isLiquidityRemoval: true = LP being pulled
• isCounterfeit: true = fake/copycat token
• isWash: true = wash trading detected

4.3 Advanced Risk Analysis

For tokens that pass the security scan, run advanced info:

onchainos token advanced-info --address <address> --chain <chainId>

Key fields:

• devCreateTokenCount — serial deployer?
• devRugPullTokenCount — rug history
• lpBurnedPercent — LP lock safety (higher = safer)
• sniperHoldingPercent — sniper concentration
• bundleHoldingPercent — bundle bot concentration
• suspiciousHoldingPercent — flagged wallets
• tokenTags — look for: communityRecognized, smartMoneyBuy, dsPaid, CTO

4.4 Risk Classification

Step 5: Consolidated Briefing

Goal: Synthesize all data into a single actionable report.

5.1 Report Structure

Use the template at templates/daily-briefing.md to generate the output.

5.2 Scoring Model

Each candidate token gets a composite score (0-100):

5.3 Purchase Recommendation Tiers

Based on composite score and risk tolerance:

BUY (Score > 70, Security = SAFE)

• Strong narrative + smart money backing + clean security
• Suggested position: 2-5% of portfolio

WATCH (Score 50-70, Security = SAFE/CAUTION)

• Promising but needs more confirmation
• Set price alerts, monitor daily

AVOID (Score < 50 or Security = DANGER/CRITICAL)

• Too risky or no clear edge
• Explain specific red flags

5.4 Output Format

Generate the briefing with:

1. Executive summary (3-5 bullets)
2. Hot tokens by narrative table
3. Smart money signal table
4. Meme scan highlights
5. Security audit results
6. Final recommendation table with risk rating
7. Disclaimer

5.5 Save Report

# Save to reports directory
mkdir -p reports
# Filename: daily-alpha-YYYY-MM-DD.md

Error Handling

• If onchainos returns error 50125 (region restriction): inform user to check API key region or use VPN
• If any step returns empty data: skip that step, note it in the report, continue with available data
• If security scan returns empty for a token: mark as "UNSCANNED" in the report, do NOT recommend
• Always run all 5 steps even if some data is partial — the report should indicate data completeness

Quick Mode

If the user says "快速扫描" / "quick scan", run abbreviated version:

1. hot-tokens top 10 only (single chain)
2. signal list smart money only (single chain)
3. Skip meme scan
4. Security scan top 5 candidates only
5. Abbreviated 1-page briefing

Chain Reference