Nadfunagent

CRITICAL COMMUNICATION RULES:

1. Language: Always respond in the SAME language as the user's question. If in English, respond in English.
2. Data Loading: BEFORE executing any operations, FIRST request and load ALL required data from user or configuration files. Use OpenClaw's memory/session storage to save loaded data so you don't need to ask again.
3. Telegram Integration:
   • Send detailed reports to Telegram after each trading cycle
   • If Telegram bot connection is not established, request user to start conversation with bot first
   • Include position status, P&L, new opportunities found, trades executed
4. Initial Setup: When first invoked, ask user for:
   • MMIND_TOKEN_ADDRESS (or load from .env)
   • MONAD_PRIVATE_KEY (or load from .env)
   • MONAD_RPC_URL (or load from .env)
   • MONAD_NETWORK (or load from .env)
   • Telegram user ID for notifications Save all this data in OpenClaw memory for future use.
5. Missing variables: If .env is missing or any required variable (MONAD_PRIVATE_KEY, MONAD_RPC_URL, MMIND_TOKEN_ADDRESS, MONAD_NETWORK) is not set, ask the user to provide it before running trading or scripts. Do not proceed with buy/sell or execute-bonding-v2 until config is complete.

Nad.fun Autonomous Trading Agent

Autonomous trading agent that scans Nad.fun markets, analyzes tokens using momentum strategies, executes trades, and distributes profits to MMIND token holders.

Prerequisites

• monad-development skill installed (for wallet and RPC setup)
• nadfun-trading skill installed (for buy/sell operations), or use the trading/ folder from this repo
• nadfun-indexer skill installed (for querying events)
• nadfun-agent-api skill installed (for market data)
• Network configured (mainnet only for this skill)
• MMIND token address configured

Paths (clean install): Config is read from NADFUN_ENV_PATH if set, else $HOME/nadfunagent/.env. Positions report: POSITIONS_REPORT_PATH or $HOME/nadfunagent/positions_report.json. Scripts from nadfun-trading may be at ~/.openclaw/workspace/skills/nadfun-trading or at <this-repo>/trading. See DEPENDENCIES.md.

Configuration

CRITICAL: Load environment variables from .env file (default path: $HOME/nadfunagent/.env; override with NADFUN_ENV_PATH):

• MMIND_TOKEN_ADDRESS: Address of MMIND token for profit distribution (required)
• MONAD_PRIVATE_KEY: Private key for trading wallet (required)
• MONAD_RPC_URL: RPC endpoint URL (required)
• MONAD_NETWORK: Network type - "mainnet" or "testnet" (required)
• MAX_POSITION_SIZE: Maximum position size in MON (default: 0.1)
• PROFIT_TARGET_PERCENT: Take-profit threshold (default: 20%)
• STOP_LOSS_PERCENT: Stop-loss threshold (default: 10%)
• PNL_DISTRIBUTION_PERCENT: % of profit to distribute (default: 30%)

Before starting trading cycle:

1. Read .env file (path: NADFUN_ENV_PATH or $HOME/nadfunagent/.env). If the file is missing or any required variable is empty, ask the user to provide MMIND_TOKEN_ADDRESS, MONAD_PRIVATE_KEY, MONAD_RPC_URL, MONAD_NETWORK — do not run trading until config is complete.
2. Load MMIND_TOKEN_ADDRESS - use this for profit distribution
3. Load MONAD_PRIVATE_KEY - use this for wallet operations
4. Load MONAD_RPC_URL - use this for blockchain queries
5. Load MONAD_NETWORK - determines API endpoints (mainnet: api.nadapp.net, testnet: dev-api.nad.fun)

Workflow

EXECUTION SUMMARY - READ THIS FIRST:

1. Load config from $HOME/nadfunagent/.env (MMIND_TOKEN_ADDRESS, MONAD_PRIVATE_KEY, MONAD_RPC_URL, MONAD_NETWORK)
2. Execute ONLY 3 methods (from HAR file analysis): Method 5 (New Events API), Method 6 (Market Cap API), Method 7 (Creation Time API)
3. Combine results - merge all token addresses from 3 methods, count frequency (how many methods found each token, max 3)
4. Prioritize - sort tokens by frequency (tokens found in 2-3 methods = higher priority)
5. Save tokens - save ALL found tokens (up to top 50 for storage) to $HOME/nadfunagent/found_tokens.json for manual review
6. Analyze - get token info, market data, metrics via Agent API, calculate scores
7. Filter - keep only tokens with min liquidity 5 MON, min holders 5
8. Trade - execute trades if score >= 60
9. Distribute - distribute profits to MMIND holders if profit >= 0.1 MON

1. Market Scanning (NEW + TRENDING)

CRITICAL INSTRUCTIONS: You MUST scan tokens using ALL 7 methods below. Execute each method step-by-step and collect ALL found token addresses.

EXECUTION ORDER:

1. Method 1: CurveCreate events (Indexer) - NEW tokens
2. Method 2: CurveBuy events (Indexer) - TRENDING by volume
3. Method 3: Swap History (Agent API) - TRENDING by 24h volume
4. Method 4: Holdings (Agent API) - ACTIVE tokens from large traders
5. Method 5: New Events API - REAL-TIME BUY/CREATE events
6. Method 6: Market Cap API - TOP tokens by capitalization
7. Method 7: Creation Time API - NEWEST tokens

STEP-BY-STEP EXECUTION:

CRITICAL LOGGING REQUIREMENT: After executing EACH method, you MUST log:

1. Print: "✅ Method X executed: Found N tokens"
2. Print: "Method X token addresses: ..."
3. If method failed: Print: "❌ Method X failed: "
4. This helps track which methods are working and debug issues

NOTE: Only Methods 5, 6, and 7 are used (from HAR file analysis). Methods 1-4 are disabled.

STATUS CHECK: This method requires RPC access. You MUST execute it, don't skip it!

What to do:

1. Read $HOME/nadfunagent/.env to get MONAD_RPC_URL and MONAD_NETWORK
2. Get current block number using RPC: eth_blockNumber
3. Calculate: safeLatest = currentBlock - 10 (safety margin)
4. Scan last 900 blocks in chunks of 100 blocks (RPC limit)
5. For each chunk, query CurveCreate events using nadfun-indexer skill

How to execute:

• Use nadfun-indexer skill with parameters: event=CurveCreate, fromBlock=<start>, toBlock=<end>
• Extract token addresses from events: event.args.token or event.args[1] (token is second argument)
• Collect all unique token addresses

Example command structure:

Use skill: nadfun-indexer
Parameters: event=CurveCreate, fromBlock=<calculated_start>, toBlock=<calculated_end>
Extract: token address from each event

Expected result: Array of token addresses (0x...) from newly created tokens

Method 2: Trending Tokens via Indexer (CurveBuy volume analysis)

STATUS CHECK: This method requires RPC access. You MUST execute it, don't skip it!

What to do:

1. Query CurveBuy events from last 900 blocks (same pagination as Method 1)
2. For each event, extract: token address and amountIn (MON spent)
3. Sum volume per token address
4. Sort tokens by total volume (descending)
5. Take top 20 tokens with highest volume

How to execute:

1. Use same RPC setup as Method 1: Get publicClient from monad-development skill
2. Query CurveBuy events: For each 100-block chunk:
   • Use nadfun-indexer skill: event=CurveBuy, fromBlock=<start>, toBlock=<end>
   • OR use viem: publicClient.getContractEvents({ address: CURVE_ADDRESS, eventName: "CurveBuy", fromBlock, toBlock })
3. Extract data: From each event:
   • token = event.args.token or event.args[1]
   • amountIn = event.args.amountIn or event.args[2] (amount in MON)
4. Calculate volume: Group by token address, sum all amountIn values
5. Sort and filter: Sort by total volume DESC, take top 20
6. Log results: Print "✅ Method 2: Found top 20 trending tokens by volume"

Example:

1. Use same RPC client from Method 1
2. Query CurveBuy events in chunks of 100 blocks
3. For each event: extract token and amountIn
4. Sum volume per token
5. Sort DESC, take top 20
6. Print: "✅ Method 2: Found 20 trending tokens"

Expected result: Top 20 token addresses sorted by trading volume

Method 3: Trending Tokens via Agent API (Swap History Analysis)

What to do:

1. Take top 10 tokens from Method 2 (highest volume)
2. For each token, query swap history via Agent API
3. Calculate 24h volume from swap history
4. Keep tokens with >= 1 MON volume in last 24 hours

How to execute:

• Determine API URL: if MONAD_NETWORK=mainnet then https://api.nadapp.net, else https://dev-api.nad.fun
• For each token, make GET request: ${API_URL}/agent/swap-history/${tokenAddress}?limit=50&trade_type=ALL
• Parse response JSON
• For each swap in response.swaps:
  • Check swap.swap_info.timestamp - if within last 24 hours
  • Sum swap.swap_info.native_amount (in wei, convert to MON: divide by 1e18)
• Keep tokens with total 24h volume >= 1 MON
• Add 1-2 second delay between API calls to avoid rate limits

Example:

API_URL = MONAD_NETWORK === 'mainnet' ? 'https://api.nadapp.net' : 'https://dev-api.nad.fun'
For each token in top10:
  GET ${API_URL}/agent/swap-history/${token}?limit=50&trade_type=ALL
  Calculate 24h volume
  If volume >= 1 MON: add to trendingViaAPI
  Wait 1-2 seconds

Expected result: List of tokens with high 24h trading volume

Method 4: Get All Active Tokens (via Holdings of Large Accounts)

STATUS CHECK: Don't skip this method! Execute it with rate limit delays.

What to do:

1. From Method 2 CurveBuy events, find buyers who spent >1 MON
2. Extract their addresses (sender field)
3. Query holdings for first 5 large buyers (to avoid rate limits)
4. Extract all token addresses they hold (balance > 0)

How to execute:

1. From Method 2 results: Use CurveBuy events already queried
2. Filter large buyers:
   • Filter events where amountIn > 1000000000000000000 (1 MON in wei = 1e18)
   • Extract sender address: event.args.sender or event.args[0]
   • Get unique addresses, take first 5
3. Query holdings: For each of 5 addresses:
   • GET: ${API_URL}/agent/holdings/${address}?limit=50
   • Headers: Accept: application/json
   • Wait 2 seconds between requests
4. Parse and extract:
   • Parse JSON response
   • For each holding in response.tokens:
     • Check holding.balance_info.balance - if > 0
     • Extract holding.token_info.address or holding.token_info.token_id
5. Log results: Print "✅ Method 4: Found N active tokens from large trader holdings"

Example:

From Method 2 CurveBuy events:
  Filter: amountIn > 1e18 (1 MON)
  Extract: sender addresses
  Take first 5 unique addresses
For each address:
  GET ${API_URL}/agent/holdings/${address}?limit=50
  Extract tokens with balance > 0
  Wait 2 seconds
Print: "✅ Method 4: Found X tokens"

Expected result: Set of token addresses held by active large traders

Method 5: New Events API (Real-time BUY/SELL/CREATE events)

STATUS: ✅ This method is WORKING! Found 7 tokens in last scan.

What to do:

1. Make GET request to /api/token/new-event endpoint
2. Parse JSON response (array of events)
3. Extract token addresses from CREATE and BUY events
4. Add all unique token addresses to collection

How to execute:

1. Determine base URL: Read MONAD_NETWORK from .env:
   • If MONAD_NETWORK=mainnet: baseUrl = 'https://nad.fun'
   • Else: baseUrl = 'https://dev.nad.fun'
2. Make GET request:
   • URL: ${baseUrl}/api/token/new-event
   • Headers: Accept: application/json, User-Agent: OpenClaw-Agent/1.0
3. Parse response: JSON array of event objects
4. Extract tokens:
   • For each event in response:
     • If event.type === 'CREATE': extract event.token_info.token_id
     • If event.type === 'BUY': extract event.token_info.token_id
     • If event.type === 'SELL': optionally extract (indicates active trading)
5. Log results: Print "✅ Method 5: Found N tokens from new events API"

Example:

baseUrl = MONAD_NETWORK === 'mainnet' ? 'https://nad.fun' : 'https://dev.nad.fun'
GET ${baseUrl}/api/token/new-event
Parse JSON response
For each event:
  If event.type === 'CREATE' or 'BUY':
    Add event.token_info.token_id to tokensFromEvents
Print: "✅ Method 5: Found X tokens"

Expected result: Array of token addresses from recent CREATE and BUY events

Response structure:

• type: "BUY" | "SELL" | "CREATE"
• amount: Event amount (string)
• token_info: Complete token information (token_id, name, symbol, description, creator, etc.)
• account_info: Account that performed the action

Method 6: Market Cap API (Top tokens by market cap)

STATUS CHECK: Previous error: base64 decoding failed. Fix: Use proper decoding method.

What to do:

1. GET request to market cap endpoint
2. Response is base64-encoded JSON - decode it PROPERLY
3. Extract token addresses from response
4. Add all token addresses to collection

How to execute:

1. API URL: ${API_URL}/order/market_cap?page=1&limit=50&is_nsfw=false
   • Where API_URL = MONAD_NETWORK === 'mainnet' ? 'https://api.nadapp.net' : 'https://dev-api.nad.fun'
2. GET request:
   • Headers: Accept: application/json, User-Agent: OpenClaw-Agent/1.0
   • Get response as text (not JSON)
3. Decode base64 PROPERLY:
   • Response body is base64 string
   • Use Python: import base64; decoded = base64.b64decode(response_text).decode('utf-8')
   • OR use command: echo "$response_text" | base64 -d
   • CRITICAL: Make sure response_text is the raw base64 string, not wrapped in JSON
4. Parse decoded JSON:
   • Parse decoded string as JSON
   • Structure: { "tokens": [...], "total_count": N }
5. Extract tokens WITH FULL DATA:
   • For each token in data.tokens:
     • Extract token.token_info.token_id (token address)
     • CRITICAL: Store FULL token object with both token_info AND market_info
     • Structure: {token_info: {...}, market_info: {...}, percent: ...}
     • Add to topMarketCapTokens array (store full objects, not just addresses)
6. Log results: Print "✅ Method 6: Found N tokens with market data from market cap API"

Example:

API_URL = MONAD_NETWORK === 'mainnet' ? 'https://api.nadapp.net' : 'https://dev-api.nad.fun'
response = GET ${API_URL}/order/market_cap?page=1&limit=100&is_nsfw=false
decoded = base64.b64decode(response.text).decode('utf-8')
data = json.loads(decoded)

topMarketCapTokens = []  # Array of full token objects
For each token in data.tokens:
  # Store FULL token object with token_info + market_info
  topMarketCapTokens.append({
    'token_info': token.token_info,
    'market_info': token.market_info,
    'percent': token.percent,
    'address': token.token_info.token_id  # For easy access
  })
Print: "✅ Method 6: Found X tokens with market data"

Expected result: Array of full token objects (with token_info + market_info) sorted by market cap

Response structure:

• tokens: Array of token objects with token_info and market_info
• total_count: Total number of tokens
• Each token includes: token_info (token_id, name, symbol, creator, etc.) and market_info (market_type, price, volume, holder_count, etc.)

Method 7: Creation Time API (Newest tokens)

CRITICAL: Include BOTH bonding curve AND DEX tokens! Do NOT filter by is_graduated.

What to do:

1. GET request to creation time endpoint (newest first)
2. Decode base64 response PROPERLY (same as Method 6)
3. Extract token addresses - include ALL tokens (both bonding curve AND DEX)
4. Add to collection WITH FULL DATA

How to execute:

• API URL: ${API_URL}/order/creation_time?page=1&limit=50&is_nsfw=false&direction=DESC
• GET request, decode base64 response (same as Method 6)
• Parse JSON: data.tokens array
• For each token:
  • Extract token.token_info.token_id (token address)
  • CRITICAL: Store FULL token object with both token_info AND market_info
  • DO NOT filter by is_graduated - include both bonding curve (is_graduated=false) AND DEX (is_graduated=true) tokens
  • Add to newestTokens array (store full objects, not just addresses)

Example:

API_URL = MONAD_NETWORK === 'mainnet' ? 'https://api.nadapp.net' : 'https://dev-api.nad.fun'
response = GET ${API_URL}/order/creation_time?page=1&limit=50&is_nsfw=false&direction=DESC
decoded = base64.b64decode(response.text).decode('utf-8')
data = json.loads(decoded)

newestTokens = []  # Array of full token objects
For each token in data.tokens:
  # Include ALL tokens - both bonding curve AND DEX
  # Store FULL token object with token_info + market_info
  newestTokens.append({
    'token_info': token.token_info,
    'market_info': token.market_info,
    'percent': token.percent,
    'address': token.token_info.token_id  # For easy access
  })
Print: "✅ Method 7: Found X tokens with market data (bonding curve + DEX)"

Expected result: Array of full token objects (with token_info + market_info) - newest tokens including both bonding curve AND DEX

Response structure:

• Same as Method 6: tokens array with token_info and market_info
• direction=DESC returns newest first
• CRITICAL: Include ALL tokens (both bonding curve AND DEX) - do NOT filter by is_graduated

Combine All Methods - CRITICAL STEP

What to do:

1. Collect ALL token addresses from Methods 5, 6, and 7 into one set (removes duplicates)
2. Count how many times each token appears across methods
3. Sort tokens by frequency (tokens found in more methods = higher priority/confidence)
4. Log summary of combination results

How to execute:

1. Create collections:

   • allTokens = new Set() or allTokens = [] (use Set to avoid duplicates)
   • tokenFrequency = new Map() or {} to count occurrences

2. Add tokens from each method:

   • Method 5: Add all token addresses from tokensFromEvents (New Events API) - these are just addresses
   • Method 6: Add all tokens from topMarketCapTokens (Market Cap API) - these are FULL objects with token_info + market_info
   • Method 7: Add all tokens from newestTokens (Creation Time API) - these are FULL objects with token_info + market_info

   IMPORTANT: For Methods 6 & 7, store the FULL token objects (not just addresses) so you can use market_info directly for analysis!

3. Count frequency and preserve full data:

   • Use a Map/object: allTokensMap = {} where key = token address, value = token object with metadata
   • For Method 5 (addresses only): allTokensMap[address] = {address, source: 'method5', data: null}
   • For Method 6 (full objects): allTokensMap[address] = {address, source: 'method6', data: fullTokenObject}
   • For Method 7 (full objects): allTokensMap[address] = {address, source: 'method7', data: fullTokenObject}
   • Count frequency: tokenFrequency[address] = (tokenFrequency[address] || 0) + 1
   • CRITICAL: Preserve full token objects from Methods 6 & 7 - they contain market_info!

4. Convert and sort:

   • candidateTokens = Object.values(allTokensMap) or Array.from(allTokensMap.values())
   • Sort by frequency DESC, then by data availability:

     candidateTokens.sort((a, b) => {
       const freqA = tokenFrequency[a.address] || 0
       const freqB = tokenFrequency[b.address] || 0
       if (freqB !== freqA) return freqB - freqA  // Higher frequency first
       // Prefer tokens with full data (market_info)
       const hasDataA = a.data && a.data.market_info ? 1 : 0
       const hasDataB = b.data && b.data.market_info ? 1 : 0
       return hasDataB - hasDataA
     })
     

   • prioritizedTokens = candidateTokens

5. Log summary:

   • Print: "📊 Combined results: Total unique tokens: N"
   • Print: "Tokens found in 2+ methods: X"
   • Print: "Tokens found in all 3 methods: Y"
   • Print: "Tokens with full market data: Z"
   • Print: "Top 10 prioritized tokens: "

Example:

allTokensMap = {}  # Map: address -> {address, source, data}
tokenFrequency = {}

# Method 5: addresses only
for address in tokensFromEvents:
  allTokensMap[address] = {address: address, source: 'method5', data: null}
  tokenFrequency[address] = (tokenFrequency[address] || 0) + 1

# Method 6: full objects with market_info
for token in topMarketCapTokens:
  address = token.token_info.token_id
  allTokensMap[address] = {address: address, source: 'method6', data: token}
  tokenFrequency[address] = (tokenFrequency[address] || 0) + 1

# Method 7: full objects with market_info
for token in newestTokens:
  address = token.token_info.token_id
  allTokensMap[address] = {address: address, source: 'method7', data: token}
  tokenFrequency[address] = (tokenFrequency[address] || 0) + 1

# Sort by frequency and data availability
candidateTokens = Object.values(allTokensMap)
prioritizedTokens = candidateTokens.sort((a, b) => {
  freqDiff = tokenFrequency[b.address] - tokenFrequency[a.address]
  if (freqDiff !== 0) return freqDiff
  return (b.data && b.data.market_info ? 1 : 0) - (a.data && a.data.market_info ? 1 : 0)
})

Print: "📊 Combined: N unique tokens, X found in 2+ methods, Y found in all 3 methods, Z with full data"

Expected result: Prioritized array of token objects, each containing:

• address: token address
• source: which method(s) found it
• data: full token object with token_info + market_info (if available from Methods 6 or 7)
• foundInMethods: count from tokenFrequency

Save Found Tokens - CRITICAL STEP

What to do: After combining all methods and getting prioritizedTokens, you MUST save them to file $HOME/nadfunagent/found_tokens.json for manual review.

How to execute:

1. Prepare token list:

   • Take top 50 tokens from prioritizedTokens
   • For each token, create object: {address: token, foundInMethods: frequency_count}
   • Create scan entry with timestamp, totalFound count, and tokens array

2. Read existing file:

   • File: $HOME/nadfunagent/found_tokens.json
   • If file doesn't exist or is empty: start with []
   • If exists: parse JSON, get array

3. Append and save:

   • Add new scan entry to array
   • Keep only last 100 scans (remove oldest if > 100)
   • Write back as formatted JSON

Example using Node.js script (RECOMMENDED):

# From skill root or repo root: print token addresses (one per line) and pipe to script
# prioritizedTokens is the array of token addresses; take first 50 and pass to script
echo -e "0x...\n0x..." | node scripts/save_tokens.js

Or from the agent: write the list of addresses to a temp file or stdin and run node scripts/save_tokens.js (script reads from stdin). Data dir: NADFUNAGENT_DATA_DIR or $HOME/nadfunagent.

Expected result: File $HOME/nadfunagent/found_tokens.json updated with latest scan results

2. Token Analysis - CRITICAL: Use Data from Methods 6 & 7 Directly

IMPORTANT: Methods 6 (Market Cap API) and Method 7 (Creation Time API) already return COMPLETE token data with market_info. Use this data directly for analysis - DO NOT make additional API calls unless absolutely necessary!

Data Structure from Methods 6 & 7: Each token in the response has this structure:

{
  "token_info": {
    "token_id": "0x...",
    "name": "...",
    "symbol": "...",
    "is_graduated": true/false,
    "created_at": timestamp,
    ...
  },
  "market_info": {
    "market_type": "DEX" or "BONDING_CURVE",
    "reserve_native": "1625513352353765005672133",  // Liquidity in MON (wei format)
    "reserve_token": "51582894169959918089536846",
    "token_price": "0.000886889894056452",
    "price": "0.0415501242",
    "price_usd": "0.000886889894056452",
    "volume": "761848094037806233587694495",  // Total volume (wei format)
    "holder_count": 31580,  // Number of holders
    "ath_price": "0.0128807779",
    "ath_price_usd": "0.0128807779",
    ...
  },
  "percent": 8.797  // Market cap change percentage
}

Analysis Steps:

1. Extract data from Methods 6 & 7:

   • For each token in data.tokens array from Methods 6 & 7:
     • Extract token_info.token_id (token address)
     • Extract market_info.reserve_native (liquidity in wei, convert to MON: divide by 1e18)
     • Extract market_info.holder_count (number of holders)
     • Extract market_info.volume (total volume in wei, convert to MON: divide by 1e18)
     • Extract market_info.market_type ("DEX" or "BONDING_CURVE")
     • Extract token_info.is_graduated (true if graduated to DEX)
     • Extract percent (market cap change percentage)

2. For Method 5 tokens (new-event API):

   • This API returns only basic event data without full market_info
   • For tokens from Method 5, you can optionally query /agent/market/:token_id to get market_info
   • BUT prioritize tokens from Methods 6 & 7 first (they already have complete data)

3. Calculate Liquidity Score (30% weight):

   liquidityMON = parseFloat(market_info.reserve_native) / 1e18
   
   // Score based on liquidity tiers
   if (liquidityMON >= 1000) liquidityScore = 100      // Excellent
   else if (liquidityMON >= 500) liquidityScore = 80     // Very good
   else if (liquidityMON >= 100) liquidityScore = 60    // Good
   else if (liquidityMON >= 50) liquidityScore = 40      // Fair
   else if (liquidityMON >= 10) liquidityScore = 20      // Low
   else liquidityScore = 0                              // Too low
   
   weightedLiquidity = liquidityScore * 0.30
   

4. Calculate Momentum Score (25% weight):

   // Use percent (market cap change) as momentum indicator
   percentChange = parseFloat(token.percent) || 0
   
   // Score based on positive momentum
   if (percentChange >= 50) momentumScore = 100      // Strong growth
   else if (percentChange >= 20) momentumScore = 80
   else if (percentChange >= 10) momentumScore = 60
   else if (percentChange >= 5) momentumScore = 40
   else if (percentChange >= 0) momentumScore = 20
   else momentumScore = 0                           // Negative momentum
   
   weightedMomentum = momentumScore * 0.25
   

5. Calculate Volume Score (20% weight):

   volumeMON = parseFloat(market_info.volume) / 1e18
   
   // Score based on volume tiers
   if (volumeMON >= 1000000) volumeScore = 100      // 1M+ MON volume
   else if (volumeMON >= 500000) volumeScore = 80
   else if (volumeMON >= 100000) volumeScore = 60
   else if (volumeMON >= 50000) volumeScore = 40
   else if (volumeMON >= 10000) volumeScore = 20
   else volumeScore = 0
   
   weightedVolume = volumeScore * 0.20
   

6. Calculate Holder Score (15% weight):

   holderCount = parseInt(market_info.holder_count) || 0
   
   // Score based on holder count
   if (holderCount >= 10000) holderScore = 100      // Excellent distribution
   else if (holderCount >= 5000) holderScore = 80
   else if (holderCount >= 1000) holderScore = 60
   else if (holderCount >= 500) holderScore = 40
   else if (holderCount >= 100) holderScore = 20
   else holderScore = 0
   
   weightedHolders = holderScore * 0.15
   

7. Calculate Progress Score (10% weight):

   isGraduated = token_info.is_graduated === true
   marketType = market_info.market_type
   
   // Score based on market stage
   if (isGraduated || marketType === "DEX") {
     progressScore = 100  // Fully graduated to DEX
   } else {
     // Still on bonding curve - use percent as progress indicator
     // Higher percent = closer to graduation
     percent = parseFloat(token.percent) || 0
     if (percent >= 80) progressScore = 80
     else if (percent >= 50) progressScore = 60
     else if (percent >= 30) progressScore = 40
     else progressScore = 20
   }
   
   weightedProgress = progressScore * 0.10
   

8. Calculate Authority Score (Bonus - up to +10 points):

   // Check for social media presence and website (indicates legitimacy)
   hasTwitter = token_info.twitter && token_info.twitter.length > 0
   hasTelegram = token_info.telegram && token_info.telegram.length > 0
   hasWebsite = token_info.website && token_info.website.length > 0
   
   authorityScore = 0
   if (hasTwitter) authorityScore += 3
   if (hasTelegram) authorityScore += 3
   if (hasWebsite) authorityScore += 4
   
   // Maximum +10 bonus points for full social presence
   authorityBonus = Math.min(authorityScore, 10)
   

9. Calculate Total Score:

   totalScore = weightedLiquidity + weightedMomentum + weightedVolume + weightedHolders + weightedProgress + authorityBonus
   
   // Round to 2 decimal places
   totalScore = Math.round(totalScore * 100) / 100
   

10. Store Analysis Results: For each token, store:

    {
      address: token_info.token_id,
      name: token_info.name,
      symbol: token_info.symbol,
      liquidity: liquidityMON,
      holders: holderCount,
      volume: volumeMON,
      marketType: marketType,
      isGraduated: isGraduated,
      percentChange: percentChange,
      scores: {
        liquidity: liquidityScore,
        momentum: momentumScore,
        volume: volumeScore,
        holders: holderScore,
        progress: progressScore,
        total: totalScore
      },
      foundInMethods: tokenFrequency[token_info.token_id] || 1
    }
    

CRITICAL INSTRUCTIONS:

• Use data from Methods 6 & 7 DIRECTLY - they already contain all needed information
• DO NOT make additional API calls to /agent/market/:token_id for tokens from Methods 6 & 7
• Only for Method 5 tokens (if needed) make additional API calls
• Calculate scores immediately after combining methods, before filtering
• Check authority (social media presence): Check token_info.twitter, token_info.telegram, token_info.website - add bonus points
• Log analysis results: Print "📊 Analysis: Token X has score Y (liquidity: A, momentum: B, volume: C, holders: D, progress: E, authority: +F)"

3. Filtering Criteria

Filter tokens based on:

• Minimum liquidity: 5 MON
• Minimum holders: 5
• Bonding curve progress: >= 10%
• Score calculation:
  • Liquidity: 30% weight
  • Momentum: 25% weight
  • Volume trend: 20% weight
  • Holder distribution: 15% weight
  • Bonding curve progress: 10% weight

4. Position Management - CRITICAL: Check Existing Positions FIRST

BEFORE analyzing new tokens, you MUST check and manage existing positions!

Step 1: Get Current Holdings

What to do:

1. Get trading wallet address from MONAD_PRIVATE_KEY in .env (derive address from private key)
2. Query current token holdings using Agent API: /agent/holdings/${walletAddress}?limit=100
3. Filter tokens where balance > 0 (exclude zero balances)
4. For each token with balance > 0, get current market data

How to execute:

// 1. Get wallet address from private key
const walletAddress = getAddressFromPrivateKey(MONAD_PRIVATE_KEY)

// 2. Query holdings
const holdingsResponse = await fetch(`${API_URL}/agent/holdings/${walletAddress}?limit=100`)
const holdings = await holdingsResponse.json()

// 3. Filter tokens with balance > 0
const activePositions = holdings.tokens.filter(token => {
  const balance = parseFloat(token.balance_info.balance) || 0
  return balance > 0
})

Print: "📊 Current positions: Found N tokens with balance > 0"

Step 2: Calculate P&L for Each Position

CRITICAL: Always use the check-pnl.js script from nadfun-trading skill for proper P&L calculation. This script:

• Reads entry price (entryValueMON) from $HOME/nadfunagent/positions_report.json (automatically recorded by buy-token.js when you purchase)
• Gets current value on-chain via nad.fun quote contract (or falls back to Agent API)
• Calculates P&L: (currentValueMON - entryValueMON) / entryValueMON * 100

What to do:

1. Use the script: Run check-pnl.js from nadfun-trading skill directory
2. The script reads entry prices from JSON (set by buy-token.js after purchases)
3. Gets current value on-chain via nad.fun quote contract
4. Calculates real P&L based on actual entry price

How to execute:

# Check P&L for all positions
cd $HOME/.openclaw/workspace/skills/nadfun-trading
node check-pnl.js

# Or with auto-sell (sells if P&L >= +5% or <= -10%)
node check-pnl.js --auto-sell

Manual calculation (if script unavailable):

// Load entry prices from positions_report.json
const report = JSON.parse(await fs.readFile('$HOME/nadfunagent/positions_report.json', 'utf-8'))

for (const position of activePositions) {
  const tokenAddress = position.token_info.token_id || position.token_info.address
  const tokenBalance = parseFloat(position.balance_info.balance) || 0
  
  // Get entry price from JSON (recorded by buy-token.js)
  const prev = report.positions?.find(p => 
    (p.address || '').toLowerCase() === tokenAddress.toLowerCase()
  )
  const entryValueMON = prev?.entryValueMON || 0
  
  // Get current value on-chain via nad.fun quote contract
  const [router, amountOutWei] = await publicClient.readContract({
    address: '0x7e78A8DE94f21804F7a17F4E8BF9EC2c872187ea', // nad.fun quote contract
    abi: lensAbi,
    functionName: 'getAmountOut',
    args: [tokenAddress, parseEther(tokenBalance.toString()), false] // false = selling
  })
  const currentValueMON = Number(amountOutWei) / 1e18
  
  // Calculate P&L
  const pnlPercent = entryValueMON > 0
    ? ((currentValueMON - entryValueMON) / entryValueMON) * 100
    : 0
  
  positions.push({
    address: tokenAddress,
    balance: tokenBalance,
    entryValueMON: entryValueMON,
    currentValueMON: currentValueMON,
    pnlPercent: pnlPercent
  })
}

Print: "💰 Position P&L calculated for N positions (source: on-chain nad.fun quote + positions_report.json)"

Entry Price Tracking:

• Entry price is automatically recorded by buy-token.js after successful purchase
• Stored in $HOME/nadfunagent/positions_report.json as entryValueMON
• If entry price not found, check-pnl.js uses current value as fallback (P&L = 0%)

Step 3: Make Sell Decisions

What to do: For each position, check sell conditions:

1. Stop-Loss Check:

   if (pnlPercent <= -10) {  // STOP_LOSS_PERCENT = -10%
     // SELL ALL - stop loss triggered
     sellDecision = {
       action: 'SELL_ALL',
       reason: 'Stop-loss triggered',
       tokenAddress: position.address,
       amount: 'all'
     }
   }
   

2. Take-Profit Check:

   if (pnlPercent >= 20) {  // PROFIT_TARGET_PERCENT = 20%
     // SELL HALF - take profit
     sellDecision = {
       action: 'SELL_HALF',
       reason: 'Take-profit triggered',
       tokenAddress: position.address,
       amount: position.balance / 2
     }
   }
   

3. Trailing Stop (Optional):

   // If profit was > 15% but dropped to < 5%, sell to protect gains
   if (previousPnL > 15 && pnlPercent < 5) {
     sellDecision = {
       action: 'SELL_ALL',
       reason: 'Trailing stop - protecting gains',
       tokenAddress: position.address,
       amount: 'all'
     }
   }
   

How to execute:

const sellDecisions = []

for (const position of positions) {
  // Stop-loss: sell all if down 10%+
  if (position.pnlPercent <= -10) {
    sellDecisions.push({
      tokenAddress: position.address,
      action: 'SELL_ALL',
      reason: `Stop-loss: ${position.pnlPercent.toFixed(2)}%`,
      amount: 'all'
    })
  }
  // Take-profit: sell half if up 20%+
  else if (position.pnlPercent >= 20) {
    sellDecisions.push({
      tokenAddress: position.address,
      action: 'SELL_HALF',
      reason: `Take-profit: ${position.pnlPercent.toFixed(2)}%`,
      amount: position.balance / 2
    })
  }
}

Print: "🔔 Sell decisions: N positions need action"
for (const decision of sellDecisions) {
  Print: `   ${decision.action}: ${decision.tokenAddress} - ${decision.reason}`
}

Step 4: Execute Sell Orders

What to do: For each sell decision:

1. Use nadfun-trading skill with action sell
2. Parameters: token address, amount (from decision)
3. The skill handles: balance check, quote, approve, execution

How to execute:

for (const decision of sellDecisions) {
  try {
    // Use nadfun-trading skill
    await useSkill('nadfun-trading', {
      action: 'sell',
      token: decision.tokenAddress,
      amount: decision.amount  // 'all' or specific amount
    })
    
    Print: `✅ Sold ${decision.amount} of ${decision.tokenAddress}: ${decision.reason}`
  } catch (error) {
    Print: `❌ Failed to sell ${decision.tokenAddress}: ${error.message}`
  }
}

5. Trading Execution - Buy New Tokens

CRITICAL: Only buy new tokens AFTER managing existing positions!

Buy Decision Logic:

1. Prioritize tokens with authority (social media presence):

   • Tokens with Twitter + Telegram + Website get priority
   • These are more legitimate and have better community support

2. Consider both early-stage AND established tokens:

   • Early-stage (5-50 MON liquidity): Higher risk, higher potential
   • Established (50k+ MON liquidity): Lower risk, steady growth
   • Both can be profitable if they meet score criteria

3. Buy Criteria:

   • Score >= 60 (or >= 55 if has social media)
   • Liquidity >= 5 MON
   • Holders >= 5
   • NOT already in portfolio (check existing positions first)

4. Position Sizing:

   • For tokens with authority (social media): up to 0.15 MON
   • For tokens without authority: up to 0.1 MON
   • Maximum total position per token: 0.15 MON

CRITICAL: Always use the buy-token.js script from nadfun-trading skill. This script:

• Automatically detects bonding curve vs DEX via nad.fun quote contract
• Handles DEX: wraps MON→WMON, approves, swaps
• Automatically records entry price in $HOME/nadfunagent/positions_report.json after successful purchase
• Works on bonding curve (MON) or DEX (MON) - all trading uses MON

Buy tokens:

cd $HOME/.openclaw/workspace/skills/nadfun-trading
NAD_PRIVATE_KEY=$MONAD_PRIVATE_KEY node buy-token.js <token-address> <MON-amount> [--slippage=300]

Example:

NAD_PRIVATE_KEY=0x... node buy-token.js 0x123...abc 0.15 --slippage=300

After purchase:

• Entry price (entryValueMON) is automatically recorded in $HOME/nadfunagent/positions_report.json
• This entry price is used by check-pnl.js for P&L calculation
• No manual tracking needed - everything is automated

Risk management:

• CRITICAL: Buy tokens on BOTH bonding curve AND DEX (don't filter by market_type)
• The nadfun-trading skill automatically detects market type and uses correct contract
• For all trades, use MON balance (no wrapping needed - all trading uses MON)
• Set slippage tolerance: 2-3% (increased for better execution, especially on DEX)
• Set deadline: 5 minutes from now
• Don't exceed MAX_POSITION_SIZE per token

6. Profit Distribution

CRITICAL: Use MMIND_TOKEN_ADDRESS from $HOME/nadfunagent/.env file.

When profit >= 0.1 MON:

// Step 1: Load MMIND_TOKEN_ADDRESS from .env file
const envFile = await readFile('$HOME/nadfunagent/.env', 'utf-8')
const mmindMatch = env

…(truncated)