ChipaTrade - Solana Trading Platform

Overview

The get_dev_tokens() method retrieves all tokens created by a specific developer address on the Solana blockchain. This is essential for analyzing developer track records, identifying patterns in token launches, and performing due diligence before investing in tokens from a particular developer.

Critical for Risk Assessment

Always check developer history before investing. High rug counts and low success rates are major red flags.

Due Diligence

Research developer history before investing

Risk Assessment

Check how many tokens a dev has created

Rug Pull Detection

Identify developers with suspicious patterns

Track Record Analysis

See successful vs failed token launches

Method Signature

python

def get_dev_tokens(self, dev_address: str) -> Dict

Parameters

dev_address (str, required)

Description: The Solana wallet address of the token developer/creator
Format: Base58-encoded Solana public key (32-44 characters)
Example: "A3xbhvsma7XYmcouyFBCfzKot5dShxHtTrhyrSfBzyZV"

Where to find:

• Token metadata on explorers (Solscan, SolanaFM)
• New pair WebSocket events (deployer_address field)
• DEX Screener token information

Return Value

Returns a dictionary containing developer token information:

python

{
    "tokens": [
        {
            "tokenAddress": "TokenMintAddress123...",
            "tokenName": "Example Token",
            "tokenTicker": "EXMPL",
            "initialLiquiditySol": 1.5,
            "createdAt": "2026-01-10T12:30:00Z",
            "currentMarketCap": 25000,
            "priceChange24h": 15.5,
            "status": "active",  # or "rugged", "failed"
            "holders": 150,
            "pairAddress": "PairAddress456..."
        }
    ],
    "counts": {
        "total": 25,
        "active": 10,
        "rugged": 8,
        "failed": 7
    },
    "statistics": {
        "successRate": 40.0,
        "averageInitialLiquidity": 2.5,
        "totalMarketCapCreated": 500000
    }
}

tokens (Array)

List of all tokens created by the developer with details including status, market cap, and holder count

counts (Object)

Token count breakdown: total, active, rugged, and failed launches

statistics (Object)

Developer statistics: success rate, average liquidity, total market cap created

Basic Usage Examples

Example 1: Get All Developer Tokens

Retrieve complete list of tokens created by a developer.

python

from axiomtradeapi import AxiomTradeClient

client = AxiomTradeClient()
client.login("username", "password")

# Developer address to investigate
dev_address = "A3xbhvsma7XYmcouyFBCfzKot5dShxHtTrhyrSfBzyZV"

# Get all tokens created by this developer
result = client.get_dev_tokens(dev_address)

print(f"📊 Developer Token Analysis")
print(f"=" * 50)
print(f"Total tokens: {result['counts']['total']}")
print(f"Active tokens: {result['counts']['active']}")
print(f"Rugged tokens: {result['counts']['rugged']}")
print(f"Failed launches: {result['counts']['failed']}")

print(f"\n📈 Statistics:")
print(f"Success rate: {result['statistics']['successRate']:.1f}%")
print(f"Avg liquidity: {result['statistics']['averageInitialLiquidity']:.2f} SOL")

Example 2: Calculate Risk Score

Assess developer risk based on their token history.

python

from axiomtradeapi import AxiomTradeClient

def calculate_dev_risk_score(dev_address: str) -> dict:
    """Calculate developer risk score based on history"""
    client = AxiomTradeClient()
    client.login("username", "password")
    
    result = client.get_dev_tokens(dev_address)
    counts = result['counts']
    stats = result['statistics']
    
    risk_score = 0
    risk_factors = []
    
    # High rug count
    rug_rate = (counts['rugged'] / counts['total']) * 100
    if rug_rate > 50:
        risk_score += 40
        risk_factors.append(f"High rug rate: {rug_rate:.1f}%")
    
    # Low success rate
    if stats['successRate'] < 30:
        risk_score += 20
        risk_factors.append(f"Low success: {stats['successRate']:.1f}%")
    
    # Too many tokens (pump and dump pattern)
    if counts['total'] > 20:
        risk_score += 15
        risk_factors.append(f"Excessive tokens: {counts['total']}")
    
    # Determine risk level
    if risk_score >= 70:
        risk_level = "🔴 HIGH RISK"
    elif risk_score >= 40:
        risk_level = "🟡 MEDIUM RISK"
    else:
        risk_level = "🟢 LOW RISK"
    
    return {
        "risk_score": risk_score,
        "risk_level": risk_level,
        "risk_factors": risk_factors
    }

# Usage
risk = calculate_dev_risk_score(dev_address)
print(f"Risk Level: {risk['risk_level']}")
print(f"Risk Score: {risk['risk_score']}/100")

Example 3: List Recent Tokens

Display the most recent tokens created by a developer.

python

from axiomtradeapi import AxiomTradeClient
from datetime import datetime

client = AxiomTradeClient()
client.login("username", "password")

result = client.get_dev_tokens(dev_address)

# Sort by creation date (most recent first)
tokens = sorted(
    result['tokens'], 
    key=lambda x: x.get('createdAt', ''), 
    reverse=True
)

print("🆕 Recent Tokens by Developer\n")

for token in tokens[:5]:
    status_emoji = {
        'active': '✅',
        'rugged': '🚩',
        'failed': '❌'
    }.get(token.get('status'), '❓')
    
    print(f"{status_emoji} {token['tokenName']} ({token['tokenTicker']})")
    print(f"   Initial liquidity: {token['initialLiquiditySol']} SOL")
    print(f"   Holders: {token.get('holders', 0)}")
    print()

Advanced Examples

Advanced usage patterns for production applications:

Developer Comparison

Compare multiple developers side-by-side

Token Launch Monitor

Monitor when devs create new tokens

Reputation System

Build comprehensive dev reputation

Investment Filter

Automated token screening

Full advanced examples with complete code are available in the GitHub repository

View Examples on GitHub

Best Practices

1. Cache Results for Performance

python

from datetime import datetime, timedelta

class CachedDevTokens:
    def __init__(self):
        self.cache = {}
        self.cache_duration = timedelta(minutes=5)
    
    def get_dev_tokens_cached(self, dev_address: str):
        """Get developer tokens with caching"""
        now = datetime.now()
        
        if dev_address in self.cache:
            cached_data, cached_time = self.cache[dev_address]
            if now - cached_time < self.cache_duration:
                return cached_data
        
        # Fetch fresh data
        result = client.get_dev_tokens(dev_address)
        self.cache[dev_address] = (result, now)
        return result

2. Validate Developer Addresses

python

import base58

def validate_dev_address(address: str) -> bool:
    """Validate Solana address format"""
    try:
        if len(address) < 32 or len(address) > 44:
            return False
        decoded = base58.b58decode(address)
        return len(decoded) == 32
    except:
        return False

# Usage
if validate_dev_address(dev_address):
    result = client.get_dev_tokens(dev_address)
else:
    print("Invalid developer address")

3. Combine with Other Analysis Methods

python

def comprehensive_analysis(dev_address: str, token_ticker: str):
    """Combine multiple API methods"""
    # Get all developer tokens
    dev_tokens = client.get_dev_tokens(dev_address)
    
    # Get specific token analysis
    token_analysis = client.get_token_analysis(dev_address, token_ticker)
    
    # Combine results
    return {
        'developer_history': dev_tokens,
        'token_analysis': token_analysis,
        'risk_score': calculate_risk(dev_tokens, token_analysis)
    }

Risk Indicators

🚩 High Risk Signals

• Rug rate > 30%
• Success rate < 30%
• More than 20 tokens
• Avg liquidity < 1 SOL
• Many recent failures

✅ Good Signs

• Success rate > 50%
• Low rug count (<20%)
• Consistent liquidity
• Active token longevity
• Growing holder base

Troubleshooting

Issue: Empty or No Token Data

Problem: Returns empty token list or minimal data.

Possible Causes:

• Developer hasn't created any tokens yet
• Invalid developer address
• Tokens not indexed yet (very recent launches)

python

result = client.get_dev_tokens(dev_address)

if result['counts']['total'] == 0:
    print("No tokens created yet")
elif len(result['tokens']) == 0:
    print("Address may be invalid or not indexed")

Issue: Rate Limiting

Solution: Add delays between requests

python

import time

dev_addresses = ["Dev1...", "Dev2...", "Dev3..."]

for dev_addr in dev_addresses:
    result = client.get_dev_tokens(dev_addr)
    # Process result...
    
    time.sleep(2)  # 2 second delay

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get_dev_tokens()

Overview

Critical for Risk Assessment

Due Diligence

Risk Assessment

Rug Pull Detection

Track Record Analysis

Method Signature

Parameters

dev_address (str, required)

Return Value

tokens (Array)

counts (Object)

statistics (Object)

Basic Usage Examples

Example 1: Get All Developer Tokens

Example 2: Calculate Risk Score

Example 3: List Recent Tokens

Advanced Examples

Developer Comparison

Token Launch Monitor

Reputation System

Investment Filter

Best Practices

1. Cache Results for Performance

2. Validate Developer Addresses

3. Combine with Other Analysis Methods

Risk Indicators

🚩 High Risk Signals

✅ Good Signs

Troubleshooting

Issue: Empty or No Token Data

Issue: Rate Limiting

Need Help?

Related Documentation

buy_token() Method

GetBalance() Method

ensure_authenticated()

API Reference

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