# Daily Alpha Scanner > One-click daily alpha scanner that runs a full 8-step on-chain research pipeline using OKX OnchainOS + social sentiment + historical backtesting + trend monitoring & alerts: (1) Hot Token Discovery with narrative categorization, (2) Smart Money / KOL / Whale buy-signal tracking, (3) Social Sentiment Scan via crypto news (opennews), Reddit, and Twitter KOL tracking (xreach), (4) Meme Coin launchpad scanning with dev reputation and bundle/sniper detection, (5) Batch security audit (honeypot, mintable, fake LP, wash trading), (6) Historical Signal Validation via DexPaprika OHLCV — backtest what happened after past smart money entries, (7) Trend Monitoring & Alerts via TrendRadar (Chinese platforms: Zhihu/Weibo/Bilibili) + Google Trends (global search interest) — detect tokens trending BEFORE they pump, plus multi-channel alert push (Telegram/Feishu/Slack/DingTalk/WeChat), (8) Consolidated briefing with 8-dimension composite scoring (0-100) and BUY / WATCH / AVOID verdicts. Supports Solana, Base, Ethereum, BSC, A - Skill: `zhuyansen/daily-alpha-scanner` (Agent Skill, multi-file: 4 files) - Install (CLI): `npx skillmds@latest add zhuyansen/daily-alpha-scanner` - Raw SKILL.md: https://api.skillmd.com/api/skills/zhuyansen/daily-alpha-scanner/raw - Safety review: WARNING - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Product & Planning - License: MIT - Author: zhuyansen (https://skillmd.com/u/zhuyansen) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/zhuyansen/daily-alpha-scanner --- # Daily Alpha Scanner A systematic 8-step pipeline that scans on-chain data + social sentiment + historical backtesting + trend monitoring & alerts across multiple chains and produces a consolidated briefing with purchase recommendations. ## Pipeline ``` Step 1: Hot Tokens → Step 2: Smart Money → Step 3: Social Sentiment ↓ ↓ ↓ Step 4: Meme Scan → Step 5: Security Audit (batch scan all candidates) ↓ Step 6: Signal Backtesting (DexPaprika OHLCV — historical validation) ↓ Step 7: Trend Monitoring & Alerts (TrendRadar Chinese platforms + Google Trends global) ↓ Step 8: Consolidated Briefing + Purchase Recommendations (8D scoring) ``` ## Prerequisites ### Required: OKX OnchainOS ```bash 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) ### Social Sentiment Tools (graceful degradation — all optional, use what's available) Check availability in order of priority: ```bash # 1. opennews MCP — crypto news sentiment (best source, MCP server) # If configured: mcp__opennews__search_news_by_coin tool is available # Install: see https://github.com/6551Team/opennews-mcp # 2. Reddit JSON API — community buzz (no auth needed, works everywhere) curl -s "https://www.reddit.com/r/cryptocurrency/hot.json?limit=1" -H "User-Agent: agent-reach/1.0" | python3 -c "import json,sys; print('Reddit: OK')" 2>/dev/null || echo "Reddit: unavailable" # 3. xreach CLI — Twitter/X KOL tracking (needs proxy in China) # Configure proxy first: agent-reach configure proxy http://127.0.0.1:7890 which xreach && xreach search "test" --json -n 1 --proxy http://127.0.0.1:7890 2>/dev/null && echo "xreach: OK" || echo "xreach: unavailable" # 4. opentwitter-mcp — Twitter MCP server (alternative to xreach) # If configured: opentwitter MCP tools are available ``` **At least one social source must work.** If all fail, Step 3 is skipped and social score defaults to 50 (neutral). ### Historical Data: DexPaprika (free, no API key) ```bash # DexPaprika provides DEX historical OHLCV data by token contract address # Supports Solana, Base, Ethereum, BSC, Arbitrum + 33 networks # REST API — no installation needed, just curl # Test: curl -s "https://api.dexpaprika.com/search?query=TROLL" | python3 -c "import json,sys; print(json.loads(sys.stdin.read()).get('tokens',[])[0])" # Optional: add as MCP server for richer integration # claude mcp add dexpaprika -- npx -y dexpaprika-mcp ``` If DexPaprika API is unreachable, Step 6 is skipped and backtestScore defaults to 50 (neutral). ### Trend Monitoring: TrendRadar + Google Trends (graceful degradation — both optional) ```bash # 1. TrendRadar MCP — Chinese platform trend monitoring (Docker) # Crawls Zhihu, Weibo, Bilibili, Douyin, Baidu etc. for trending topics # Also provides multi-channel alert push (Telegram/Feishu/Slack/DingTalk/WeChat) # Install: docker run -d --name trendradar-mcp -p 8788:3333 wantcat/trendradar-mcp # Add to Claude: claude mcp add trendradar --transport http -- http://localhost:8788/mcp # GitHub: https://github.com/WantCat/TrendRadar (58K+ stars) # 2. Google Trends MCP — global search interest tracking # Detects rising search interest for token names worldwide # Install: claude mcp add google-trends -- npx -y @andrewlwn77/google-trends-mcp ``` If TrendRadar is unavailable, Chinese platform trend data is skipped. If Google Trends is unavailable, global trend data is skipped. If both are down, Step 7 is skipped and trendScore defaults to 50 (neutral). ## Execution ### User Parameters Ask the user before starting (or use defaults): | Parameter | Default | Options | |-----------|---------|---------| | Chains | Solana + Base | Solana (501), Ethereum (1), Base (8453), BSC (56), Arbitrum (42161) | | Focus | All narratives | AI, Meme, DeFi, Infra, BTC-eco, Political | | Risk tolerance | Medium | Conservative, Medium, Aggressive | | Output | Terminal | Terminal, Markdown file | 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: ```bash # Trending by OKX trending score (hot-tokens ranking) onchainos token hot-tokens --chain --limit 20 # Volume-based trending (complementary view) onchainos token trending --chains --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: ```bash # Smart money buy signals onchainos signal list --chain --wallet-type 1 --limit 10 # KOL buy signals onchainos signal list --chain --wallet-type 2 --limit 10 # Whale buy signals onchainos signal list --chain --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: Social Sentiment Scan **Goal**: Gauge market sentiment from crypto news, Reddit communities, and Twitter/X KOL discussions for candidate tokens from Steps 1-2. ### 3.1 Determine Available Sources At the start of this step, check which social tools are available: ``` Source priority (use all that work): 1. opennews MCP → mcp__opennews__search_news_by_coin (if MCP configured) 2. Reddit API → curl to reddit JSON endpoints (always available) 3. xreach CLI → Twitter search (if installed + authenticated) 4. opentwitter → Twitter MCP (if MCP configured) ``` Record which sources are active. If none work, skip this step entirely and assign `socialScore = 50` (neutral) for all tokens. ### 3.2 Crypto News Sentiment (opennews) For each candidate token (top 10 from Steps 1-2 combined): ``` Tool: mcp__opennews__search_news_by_coin Args: coin = "", limit = 5 ``` From results, extract: - **newsCount**: how many articles in the last 24-48h - **sentiment**: classify each headline as bullish / bearish / neutral - **catalysts**: any major events (listing, partnership, hack, regulation) Scoring: - 5+ bullish articles, no bearish → `newsSentiment = 90` - 3+ bullish, no bearish → `newsSentiment = 75` - Mixed (both bullish & bearish) → `newsSentiment = 50` - Mostly bearish → `newsSentiment = 25` - No news at all → `newsSentiment = 50` (neutral, not penalized) ### 3.3 Reddit Community Buzz For each candidate token, search crypto subreddits: ```bash # Search across crypto Reddit curl -s "https://www.reddit.com/r/cryptocurrency+CryptoMoonShots+solana+defi/search.json?q=&limit=10&sort=new&t=week" \ -H "User-Agent: daily-alpha-scanner/2.0" ``` From results, extract: - **postCount**: number of posts mentioning this token in the past week - **totalScore**: sum of upvotes across all matching posts - **commentCount**: total comments (engagement depth) - **sentimentRatio**: ratio of positive vs negative posts (read titles + top comments) Scoring: - 10+ posts, totalScore > 100, mostly positive → `redditBuzz = 85` - 5+ posts, totalScore > 30, mixed-positive → `redditBuzz = 65` - 1-4 posts, low engagement → `redditBuzz = 45` - Zero mentions → `redditBuzz = 30` (no buzz is slightly bearish for a "hot" token) ### 3.4 Twitter/X KOL Tracking (if available) If xreach CLI is available: ```bash xreach search "$TOKEN_SYMBOL crypto" --json -n 15 --proxy http://127.0.0.1:7890 ``` If opentwitter MCP is available, use its search tool instead. From results, extract: - **tweetCount**: number of tweets in past 24-48h - **kol_mentions**: tweets from accounts with 10K+ followers - **sentiment**: bullish / bearish / neutral ratio - **engagementRate**: avg (likes + retweets + replies) per tweet Scoring: - 3+ KOL mentions, mostly bullish, high engagement → `twitterScore = 90` - 1-2 KOL mentions, positive sentiment → `twitterScore = 70` - Mentions exist but mixed/low engagement → `twitterScore = 50` - No data (tool unavailable) → `twitterScore = null` (excluded from average) ### 3.5 Social Score Calculation Combine available sources into a single `socialScore` (0-100): ``` If all 3 sources available: socialScore = newsSentiment × 0.40 + redditBuzz × 0.30 + twitterScore × 0.30 If 2 sources available: socialScore = weighted average of available sources (equal weights) If 1 source available: socialScore = that source's score If 0 sources: socialScore = 50 (neutral) ``` ### 3.6 Social Signal Flags Flag notable patterns: - 🔥 **VIRAL**: socialScore > 80 — strong positive buzz across multiple platforms - 📰 **NEWS_CATALYST**: major news event detected (listing, partnership, whale buy) - 🤫 **SILENT_ALPHA**: smart money buying (Step 2) but zero social buzz — potential early signal - ⚠️ **HYPE_RISK**: social score > 85 but smart money already selling (soldRatio > 60%) — possible exit liquidity - 🧊 **COLD**: socialScore < 30 — no one is talking about this token --- ## Step 4: Meme Coin Scan **Goal**: Scan launchpads for new launches worth tracking. ### 4.1 New Token Scan ```bash # Latest meme launches on Solana onchainos memepump tokens --chain 501 # Latest meme launches on BSC (if in target chains) onchainos memepump tokens --chain 56 ``` ### 4.2 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) ### 4.3 Developer Reputation Check For promising tokens (survived bonding, has social presence): ```bash onchainos memepump token-dev-info --address --chain ``` Key fields: - `devCreateTokenCount` — how many tokens this dev has created - `devRugPullTokenCount` — how many rugged - `devLaunchedTokenCount` — how many survived past bonding curve ### 4.4 Bundle/Sniper Detection ```bash onchainos memepump token-bundle-info --address --chain ``` ### 4.5 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 5: Security Audit **Goal**: Batch security scan all candidate tokens from Steps 1-4. ### 5.1 Collect Candidates Merge the shortlisted tokens from Steps 1, 2, 3, and 4 (deduplicate by address). Max 10 tokens for batch scan. ### 5.2 Batch Token Security Scan ```bash onchainos security token-scan --tokens ":,:,..." ``` 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 ### 5.3 Advanced Risk Analysis For tokens that pass the security scan, run advanced info: ```bash onchainos token advanced-info --address
--chain ``` 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` ### 5.4 Risk Classification | Risk Level | Criteria | Action | |------------|----------|--------| | SAFE | LOW risk, no flags, LP burned > 90%, dev clean | Can consider | | CAUTION | LOW risk but some flags (high dev token count, moderate concentration) | Small position only | | DANGER | Any honeypot/mintable/fake liquidity flag | REJECT | | CRITICAL | Multiple red flags, dev rug history, active dumping | REJECT + warn | --- ## Step 6: Signal Backtesting (Historical Validation) **Goal**: For BUY/WATCH candidates that passed security audit, validate the signal by checking historical price performance using DexPaprika OHLCV data. ### 6.1 Select Candidates for Backtesting Only backtest tokens that: - Passed Step 5 security audit (Risk = LOW or MEDIUM, no critical flags) - Have a composite score > 45 from Steps 1-5 (skip obvious AVOIDs) - Max 5 tokens to backtest (API rate limiting) ### 6.2 Get Token Pool (Highest Liquidity) For each candidate, find the highest-liquidity DEX pool: ```bash curl -s "https://api.dexpaprika.com/networks//tokens//pools?limit=3" ``` Network mapping: - Solana → `solana` - Base → `base` - Ethereum → `ethereum` - BSC → `bsc` - Arbitrum → `arbitrum-one` From results, pick the pool with highest `volume_usd`. Record `pool_id`. ### 6.3 Pull Historical OHLCV ```bash # 30-day daily candles curl -s "https://api.dexpaprika.com/networks//pools//ohlcv?start=<30d_ago>&end=&interval=24h&limit=30" # 7-day hourly candles (for recent price action) curl -s "https://api.dexpaprika.com/networks//pools//ohlcv?start=<7d_ago>&end=&interval=1h&limit=168" ``` Available intervals: `1m`, `5m`, `10m`, `15m`, `30m`, `1h`, `6h`, `12h`, `24h` ### 6.4 Compute Signal Metrics From OHLCV data, calculate: **Price Trend:** - `price_7d_change`: 7-day price change % - `price_30d_change`: 30-day price change % - `price_from_ath`: distance from 30-day all-time high % - `price_from_atl`: distance from 30-day all-time low % **Volatility:** - `daily_volatility`: average daily (high-low)/open % - `max_drawdown_7d`: worst peak-to-trough decline in 7 days **Volume Trend:** - `volume_trend`: is 7d avg volume higher or lower than 30d avg? (expanding = bullish) - `volume_spike_days`: days with volume > 3x average in last 30d **Support/Resistance:** - `current_vs_7d_vwap`: current price relative to 7-day VWAP (above = bullish, below = bearish) - `consolidation_range`: is price in a tight range (<10% spread) in last 3 days? (potential breakout) ### 6.5 Smart Money Entry Timing Analysis If the token appeared in Step 2 (smart money signals), analyze: - Where is the current price relative to when smart money likely entered? - Did SM enter near recent lows (good timing) or near highs (chasing)? - Is the token still within 20% of SM entry zone? (potential upside remains) - Has SM already captured > 50% of the recent move? (late entry risk) Estimate SM entry price by looking at the OHLCV around the signal detection time. ### 6.6 Backtest Scoring Rate each token's historical performance: | backtestScore | Criteria | |---------------|----------| | 80-100 | Uptrend + expanding volume + near support + SM entered low | | 60-79 | Mixed trend but positive volume + reasonable entry zone | | 40-59 | Sideways/declining + no volume expansion | | 20-39 | Downtrend + shrinking volume + SM entry near highs | | 0-19 | Crash + extreme drawdown + volume collapse | ### 6.7 Backtest Signal Flags - 📈 **UPTREND**: 7d change > +15%, expanding volume → momentum confirmed - 🔄 **CONSOLIDATION**: tight range + volume drying up → potential breakout/breakdown - 📉 **DOWNTREND**: 7d change < -20%, declining volume → avoid or wait for bottom - 💎 **DIAMOND_ENTRY**: price within 10% of 30d low + volume expanding → potential reversal point - 🏔️ **NEAR_ATH**: price within 10% of 30d high → late entry risk, wait for pullback --- ## Step 7: Trend Monitoring & Alerts **Goal**: Detect whether candidate tokens are trending on mainstream platforms BEFORE price pumps, and optionally push alerts via multi-channel notifications. ### 7.1 Determine Available Trend Sources Check which trend monitoring tools are available: ``` Source priority (use all that work): 1. TrendRadar MCP → Chinese platforms (Zhihu, Weibo, Bilibili, Douyin, Baidu, etc.) 2. Google Trends MCP → Global search interest (worldwide + country-specific) ``` If neither is available, skip this step. Assign `trendScore = 50` (neutral) for all tokens. ### 7.2 TrendRadar: Chinese Platform Trend Detection TrendRadar provides trend data from 30+ Chinese platforms. For our use case: **Step A: Trigger a fresh crawl (if data is stale)** ``` Tool: mcp__trendradar__trigger_crawl Args: save_to_local = true ``` If crawl fails (no config), skip to cached data or Google Trends only. **Step B: Check global trending topics** ``` Tool: mcp__trendradar__get_trending_topics Args: top_n = 20, extract_mode = "auto_extract" ``` Scan the trending topic list for any crypto/blockchain/Web3 keywords. Note which narratives are hot on Chinese platforms (AI, meme, DeFi, etc.). **Step C: Search for each candidate token** For each candidate token (top 5 from Steps 1-5): ``` Tool: mcp__trendradar__search_news Args: query = "", limit = 10 ``` From results, extract: - **cnMentionCount**: number of mentions on Chinese platforms - **cnPlatforms**: which platforms mention it (Zhihu = informed community, Weibo = mass awareness, Bilibili = young community) - **cnSentiment**: positive / negative / neutral ratio from titles **Step D: Analyze viral potential (if mentions found)** ``` Tool: mcp__trendradar__analyze_topic_trend Args: topic = "", analysis_type = "viral", spike_threshold = 3.0 ``` Check for: - **spike_detected**: is there a sudden surge in mentions? - **lifecycle_phase**: emerging / growing / peak / declining - **heat_score**: normalized heat score across platforms Scoring: - 5+ mentions across 3+ platforms, spike detected → `cnTrendScore = 90` - 3+ mentions, positive sentiment, growing phase → `cnTrendScore = 70` - 1-2 mentions, neutral sentiment → `cnTrendScore = 50` - Zero mentions → `cnTrendScore = 30` (Chinese community hasn't noticed yet) ### 7.3 Google Trends: Global Search Interest **Step A: Get currently trending topics** ``` Tool: mcp__google-trends__google_trends_trending Args: language = "en", country = "US" ``` **Step B: Cross-check with candidate tokens** Scan the global trending topics list for: - Exact token symbol matches (e.g., "TROLL", "PEPE") - Related narrative keywords (e.g., "solana meme coin", "AI crypto") - Broader crypto market trends (e.g., "bitcoin ATH", "crypto bull run") Scoring: - Token name appears in trending topics → `globalTrendScore = 95` (extremely bullish — mainstream attention) - Related narrative is trending → `globalTrendScore = 70` (narrative tailwind) - Crypto/blockchain terms trending → `globalTrendScore = 55` (general market interest) - No crypto-related trends → `globalTrendScore = 40` (no mainstream tailwind) ### 7.4 Trend Score Calculation Combine available trend sources: ``` If both TrendRadar + Google Trends available: trendScore = cnTrendScore × 0.50 + globalTrendScore × 0.50 If only TrendRadar available: trendScore = cnTrendScore If only Google Trends available: trendScore = globalTrendScore If neither available: trendScore = 50 (neutral) ``` ### 7.5 Trend Signal Flags - 🚀 **TREND_SURGE**: trendScore > 80, token found on BOTH Chinese platforms AND Google Trends — massive cross-market attention, move fast - 📊 **CN_BUZZ**: cnTrendScore > 70 but globalTrendScore < 50 — hot on Chinese platforms, not yet global — potential early signal for Western markets - 🌐 **GLOBAL_TREND**: globalTrendScore > 70 but cnTrendScore < 50 — global mainstream attention, Chinese community hasn't caught up yet - 📡 **EARLY_SIGNAL**: 1-2 mentions found in trend analysis but not mainstream trending — very early, needs more confirmation - 💤 **NO_TREND**: trendScore < 30 — not trending anywhere, may lack the catalyst for price action ### 7.6 Multi-Channel Alert Push (Optional — Post-Scan) If BUY recommendations are found AND TrendRadar notification channels are configured: **Step A: Check available channels** ``` Tool: mcp__trendradar__get_notification_channels ``` **Step B: Format and send alert** ``` Tool: mcp__trendradar__send_notification Args: title = "🔔 Daily Alpha Scanner — BUY Signal" message = "" channels = [configured channels] ``` Alert content format: ```markdown **Daily Alpha Scanner v4.0 — BUY Alert** 🟢 **TOKEN_NAME** (Chain) — Score: XX/100 · MCap: $XXM | SM: X wallets | Social: XX | Backtest: 📈 · Trend: 🚀 TREND_SURGE — trending on Google + Chinese platforms · Why: [1-line reason] ⏰ Scanned at: YYYY-MM-DD HH:MM UTC ``` Supported channels: Telegram, Feishu (飞书), Slack, DingTalk (钉钉), WeChat Work (企业微信), Email, ntfy, Bark If no notification channels are configured, skip alerts. The report still contains all trend data. --- ## Step 8: Consolidated Briefing **Goal**: Synthesize all data into a single actionable report. ### 8.1 Report Structure Use the template at `templates/daily-briefing.md` to generate the output. ### 8.2 Scoring Model (8 Dimensions) Each candidate token gets a composite score (0-100): | Factor | Weight | Data Source | |--------|--------|-------------| | Narrative strength | 8% | Step 1 categorization + current market meta | | On-chain momentum | 8% | Price change, volume, tx count, holder growth | | Smart money conviction | 25% | Step 2: signal count, wallet count, sold ratio | | Social sentiment | 8% | Step 3: news + Reddit + Twitter combined socialScore | | Security score | 25% | Step 5: security scan + advanced info | | Historical validation | 8% | Step 6: DexPaprika OHLCV backtest — price trend, volume, entry timing | | **Trend monitoring** | **10%** | **Step 7: TrendRadar Chinese platforms + Google Trends global search interest** | | Liquidity depth | 8% | Liquidity USD, liquidity/mcap ratio | **Signal Bonus/Penalty (Social + Backtest + Trend combined):** - If social signal = 🔥 VIRAL → add +5 bonus to final score - If social signal = 🤫 SILENT_ALPHA → no change (early signal, not penalized) - If social signal = ⚠️ HYPE_RISK → subtract -10 from final score (exit liquidity warning) - If social signal = 🧊 COLD → subtract -3 from final score - If backtest signal = 📈 UPTREND → add +3 bonus (momentum confirmed by history) - If backtest signal = 💎 DIAMOND_ENTRY → add +5 bonus (price near bottom + volume expanding) - If backtest signal = 📉 DOWNTREND → subtract -5 from final score - If backtest signal = 🏔️ NEAR_ATH → subtract -3 from final score (late entry risk) - If trend signal = 🚀 TREND_SURGE → add +5 bonus (cross-market trending) - If trend signal = 📊 CN_BUZZ → add +3 bonus (Chinese community early signal) - If trend signal = 🌐 GLOBAL_TREND → add +3 bonus (global mainstream interest) - If trend signal = 📡 EARLY_SIGNAL → add +1 bonus (very early, minimal weight) - If trend signal = 💤 NO_TREND → subtract -2 from final score (no catalyst) ### 8.3 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 ### 8.4 Output Format Generate the briefing with: 1. Executive summary (3-5 bullets) 2. Hot tokens by narrative table 3. Smart money signal table 4. Social sentiment overview (news + Reddit + Twitter summary per token) 5. Meme scan highlights 6. Security audit results 7. Historical validation (price chart summary, backtest scores, entry timing) 8. **Trend monitoring (Chinese platform buzz + Google Trends + trend signals)** 9. Final recommendation table with 8D score breakdown and risk rating 10. Alert push status (if notifications sent) 11. Disclaimer ### 8.5 Save Report ```bash # 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 - **Social sentiment graceful degradation**: if opennews MCP unavailable → use Reddit + Twitter only; if Reddit blocked → use opennews + Twitter; if all social tools fail → assign socialScore = 50 (neutral) and note "Social data unavailable" in report - If xreach times out (common without proxy): fall back to opentwitter-mcp or skip Twitter entirely - **DexPaprika graceful degradation**: if API returns error or empty data → skip backtesting, assign backtestScore = 50 (neutral) and note "Historical data unavailable" in report - If pool not found for a token (new token, no DEX history) → backtestScore = 50, note "No trading history" - **TrendRadar graceful degradation**: if Docker container not running or MCP unreachable → skip Chinese platform trend check, use only Google Trends; if crawl fails (no config.yaml) → try using cached data via search_news; if no cached data → cnTrendScore = 50 (neutral) - **Google Trends graceful degradation**: if MCP connection fails or timeout → skip global trend check, use only TrendRadar; if both fail → trendScore = 50 (neutral) and note "Trend data unavailable" - **Alert push graceful degradation**: if send_notification fails or no channels configured → skip alerts, report still generated normally - Always run all 8 steps even if some data is partial — the report should indicate data completeness per step ## 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. Quick social scan: opennews only (skip Reddit/Twitter), top 5 tokens 4. Skip meme scan 5. Security scan top 5 candidates only 6. Skip backtesting (quick mode does not run DexPaprika) 7. Skip trend monitoring (quick mode does not run TrendRadar/Google Trends) 8. Abbreviated 1-page briefing with 8D scores (backtest + trend = neutral) If notification channels are configured, still push BUY alerts in quick mode ## Chain Reference | Chain | ID | Meme Support | |-------|----|-------------| | Solana | 501 | pumpfun, believe, launchlab, moonshot, etc. | | BSC | 56 | fourmeme, flap | | Base | 8453 | clanker, bankr | | Ethereum | 1 | Limited | | TRON | 195 | sunpump |