The Pump.fun API provides powerful endpoints for tracking trades on specific coins. This guide shows you how to monitor trading activity effectively.
Get all trades for a coin
Retrieve all trades for a specific coin using its mint address:
curl -X GET "https://frontend-api-v3.pump.fun/trades/all/CxLHsqvjfisgPAGwcZJsTn6nzZXJLxmVYM7v9pump?limit=50&offset=0&minimumSize=0" \
-H "Authorization: Bearer <your_token>" \
-H "Accept: application/json"
Filter trades by size
Use the minimumSize parameter to filter out small trades and focus on significant transactions:
curl -X GET "https://frontend-api-v3.pump.fun/trades/all/CxLHsqvjfisgPAGwcZJsTn6nzZXJLxmVYM7v9pump?limit=50&offset=0&minimumSize=1000000" \
-H "Authorization: Bearer <your_token>" \
-H "Accept: application/json"
The minimumSize parameter helps reduce noise by filtering out small trades, making it easier to track whale activity.
Get trade count
Retrieve the total number of trades for a coin:
curl -X GET "https://frontend-api-v3.pump.fun/trades/count/CxLHsqvjfisgPAGwcZJsTn6nzZXJLxmVYM7v9pump?minimumSize=0" \
-H "Authorization: Bearer <your_token>" \
-H "Accept: application/json"
Paginate through trades
Implement pagination to efficiently retrieve large trade histories:
import requests
def fetch_all_trades(mint, minimum_size=0, page_size=100):
"""
Fetch all trades for a coin with pagination
"""
url = f"https://frontend-api-v3.pump.fun/trades/all/{mint}"
headers = {
"Authorization": "Bearer <your_token>",
"Accept": "application/json"
}
all_trades = []
offset = 0
while True:
params = {
"limit": page_size,
"offset": offset,
"minimumSize": minimum_size
}
response = requests.get(url, headers=headers, params=params)
trades = response.json()
if not trades:
break
all_trades.extend(trades)
offset += page_size
# Stop if we got fewer results than requested
if len(trades) < page_size:
break
return all_trades
# Usage
mint = "CxLHsqvjfisgPAGwcZJsTn6nzZXJLxmVYM7v9pump"
all_trades = fetch_all_trades(mint, minimum_size=100000)
print(f"Total trades: {len(all_trades)}")
When paginating, monitor the number of results returned. If you receive fewer results than your limit, you’ve reached the end of the data.
Track trades from followed users
Monitor trades made by users that a specific user follows:
curl -X GET "https://frontend-api-v3.pump.fun/trades/followsUserId/CxLHsqvjfisgPAGwcZJsTn6nzZXJLxmVYM7v9pump?followsUserId=user123&limit=50&offset=0&minimumSize=0" \
-H "Authorization: Bearer <your_token>" \
-H "Accept: application/json"
Get latest trade
Retrieve the most recent trade across all coins:
curl -X GET "https://frontend-api-v3.pump.fun/trades/latest" \
-H "Authorization: Bearer <your_token>" \
-H "Accept: application/json"
Real-time monitoring example
Implement a simple trade monitor that polls for new trades:
import requests
import time
from datetime import datetime
def monitor_trades(mint, interval_seconds=5):
"""
Monitor trades for a coin in real-time using polling
"""
url = f"https://frontend-api-v3.pump.fun/trades/all/{mint}"
headers = {
"Authorization": "Bearer <your_token>",
"Accept": "application/json"
}
seen_trades = set()
while True:
try:
params = {
"limit": 20,
"offset": 0,
"minimumSize": 0
}
response = requests.get(url, headers=headers, params=params)
trades = response.json()
for trade in trades:
trade_id = trade.get('signature') # Unique identifier
if trade_id and trade_id not in seen_trades:
seen_trades.add(trade_id)
print(f"[{datetime.now()}] New trade: {trade}")
time.sleep(interval_seconds)
except Exception as e:
print(f"Error monitoring trades: {e}")
time.sleep(interval_seconds)
# Usage
mint = "CxLHsqvjfisgPAGwcZJsTn6nzZXJLxmVYM7v9pump"
monitor_trades(mint, interval_seconds=10)
Polling too frequently can lead to rate limiting. See the best practices guide for recommended polling intervals. Best practices
Use appropriate page sizes
Set limit between 20-100 for optimal performance. Larger values may cause timeouts.
Filter by minimum size
Use minimumSize to reduce data volume and focus on significant trades.
Implement exponential backoff
When polling, use exponential backoff on errors to avoid overwhelming the API.
Cache trade data
Store historical trades locally to minimize redundant API calls.