Trading Bot Integration - Connect ApudFlow to External Trading Systems

Advanced trading strategies often require combining market analysis from ApudFlow with execution capabilities of external trading platforms. The External Data Provider worker bridges this gap by exposing your analysis results through API endpoints that trading bots can consume for automated execution.

This guide demonstrates how to integrate ApudFlow analysis with popular trading platforms like Binance, Saxo Bank, and Polymarket.

Integration Architecture

Analysis → Execution Flow

ApudFlow Analysis

Technical indicators (Support/Resistance, RSI, MACD)
AI-powered signals (AI Classifier, AI Data Analyzer)
Market data processing (Twelve Data, Yahoo Finance)

External Data Provider

Exposes analysis results via REST endpoints
Provides real-time data access for trading bots
Supports both signal-based and data-based integration

Trading Platform Execution

Receives signals through API calls
Executes trades based on ApudFlow analysis
Provides execution confirmations

Binance Integration Example

Signal-Based Trading Bot

Workflow Setup

Add technical analysis workers (Support/Resistance Calculator)
Include AI Classifier for buy/sell signals
Connect results to External Data Provider
Configure JSON response with signal data

Example Workflow Output

{
  "signal": "BUY",
  "symbol": "BTCUSDT",
  "price": 45000,
  "confidence": 0.85,
  "indicators": {
    "rsi": 35,
    "support_level": 44000,
    "resistance_level": 47000
  },
  "timestamp": "2025-12-26T14:30:00Z"
}

Trading Bot Implementation

import requests
from binance.client import Client

# ApudFlow endpoint
signal_endpoint = "https://api.apudflow.io/api/w/2511351432345546/3167507321031740"

# Binance client setup
client = Client(api_key, api_secret)

def check_signals():
    try:
        response = requests.get(signal_endpoint)
        signal_data = response.json()['data']
        
        if signal_data['signal'] == 'BUY' and signal_data['confidence'] > 0.8:
            # Execute buy order
            order = client.order_market_buy(
                symbol=signal_data['symbol'],
                quantity=0.001  # Adjust based on your strategy
            )
            print(f"Buy order executed: {order}")
            
    except Exception as e:
        print(f"Error checking signals: {e}")

# Run signal checking every 5 minutes
import schedule
schedule.every(5).minutes.do(check_signals)

Saxo Bank Multi-Asset Integration

Portfolio Rebalancing Bot

Advanced Analysis Workflow

Use Twelve Data for multi-asset price feeds
Apply AI Data Analyzer for portfolio optimization
Calculate rebalancing signals with Function worker
Expose rebalancing instructions via External Data Provider

Portfolio Analysis Output

{
  "rebalance_actions": [
    {
      "action": "SELL",
      "symbol": "AAPL",
      "shares": 50,
      "target_allocation": 0.15
    },
    {
      "action": "BUY", 
      "symbol": "MSFT",
      "amount": 5000,
      "target_allocation": 0.25
    }
  ],
  "current_allocation": {
    "AAPL": 0.20,
    "MSFT": 0.15,
    "GOOGL": 0.30
  },
  "analysis_timestamp": "2025-12-26T16:00:00Z"
}

Saxo Bank Integration Code

import requests
import saxo_openapi

# ApudFlow rebalancing endpoint
rebalance_endpoint = "https://api.apudflow.io/api/w/2511351432345546/3167507321031740"

def execute_rebalancing():
    # Get rebalancing instructions
    response = requests.get(rebalance_endpoint)
    instructions = response.json()['data']
    
    # Initialize Saxo client
    client = saxo_openapi.Client(app_key, app_secret, access_token)
    
    for action in instructions['rebalance_actions']:
        if action['action'] == 'BUY':
            # Place buy order
            order = client.place_order(
                account_key=account_key,
                uic=action['uic'],  # Saxo instrument identifier
                asset_type='Stock',
                order_type='Market',
                side='Buy',
                amount=action['amount']
            )
        elif action['action'] == 'SELL':
            # Place sell order
            order = client.place_order(
                account_key=account_key,
                uic=action['uic'],
                asset_type='Stock', 
                order_type='Market',
                side='Sell',
                quantity=action['shares']
            )
        print(f"Executed {action['action']} order for {action['symbol']}")

# Execute rebalancing daily
import schedule
schedule.every().day.at("09:30").do(execute_rebalancing)

Polymarket Prediction Market Integration

Event-Driven Trading

News Analysis Workflow

Fetch news with Fetch NewsAPI or Fetch RSS
Analyze sentiment with AI Summarizer
Generate market predictions with AI Classifier
Provide prediction signals via External Data Provider

Prediction Signal Output

{
  "market_slug": "will-bitcoin-hit-100k-by-2026",
  "prediction": "YES",
  "confidence": 0.78,
  "analysis": {
    "sentiment_score": 0.65,
    "key_factors": ["institutional_adoption", "halving_expectations"],
    "news_volume": 45
  },
  "recommended_position": {
    "outcome": "YES",
    "amount_usd": 100
  }
}

Polymarket Trading Bot

import requests
from polymarket import Polymarket

# ApudFlow prediction endpoint
prediction_endpoint = "https://api.apudflow.io/api/w/2511351432345546/3167507321031740"

def trade_predictions():
    # Get prediction signals
    response = requests.get(prediction_endpoint)
    signal = response.json()['data']
    
    if signal['confidence'] > 0.75:
        # Initialize Polymarket client
        pm = Polymarket(private_key, funder_address)
        
        # Find market
        market = pm.get_market(signal['market_slug'])
        
        # Place trade
        order = pm.place_order(
            token_id=market['token_id'],
            side=signal['prediction'].lower(),
            price=0.5,  # Mid price for simplicity
            size=signal['recommended_position']['amount_usd']
        )
        print(f"Placed prediction trade: {order}")

# Check for new predictions hourly
schedule.every().hour.do(trade_predictions)

Integration Best Practices

Error Handling and Monitoring

Robust Bot Implementation

def safe_api_call(endpoint_url, max_retries=3):
    for attempt in range(max_retries):
        try:
            response = requests.get(endpoint_url, timeout=10)
            response.raise_for_status()
            return response.json()
        except requests.RequestException as e:
            print(f"API call failed (attempt {attempt+1}): {e}")
            if attempt < max_retries - 1:
                time.sleep(2 ** attempt)  # Exponential backoff
    return None

# Usage in trading bot
signal_data = safe_api_call(signal_endpoint)
if signal_data and signal_data.get('confidence', 0) > 0.8:
    execute_trade(signal_data)

Position Sizing and Risk Management

Dynamic Position Sizing

def calculate_position_size(signal_confidence, account_balance, max_risk_percent=0.02):
    # Higher confidence = larger position
    risk_amount = account_balance * max_risk_percent
    confidence_multiplier = min(signal_confidence * 2, 1.0)  # Cap at 1.0
    
    return risk_amount * confidence_multiplier

# Example usage
position_size = calculate_position_size(
    signal_data['confidence'], 
    account_balance=10000
)

Logging and Auditing

Comprehensive Logging

import logging

logging.basicConfig(
    filename='trading_bot.log',
    level=logging.INFO,
    format='%(asctime)s - %(levelname)s - %(message)s'
)

def log_trade_execution(signal, order_result):
    logging.info(f"""
    Signal: {signal}
    Order: {order_result}
    Timestamp: {datetime.now()}
    """)

Security Considerations

API Key Protection: Store trading platform credentials securely
Rate Limiting: Respect API rate limits to avoid bans
Position Limits: Implement maximum position size limits
Error Recovery: Handle connection failures gracefully
Audit Trail: Log all trading decisions and executions

Advanced Integration Patterns

Multi-Signal Aggregation: Combine signals from multiple workflows
Portfolio Optimization: Use AI Data Analyzer for asset allocation
Risk Parity: Implement advanced risk management strategies
Machine Learning: Train models on historical signal performance

This integration approach transforms ApudFlow from a analysis platform into a complete trading ecosystem, connecting your sophisticated analysis to real-world execution capabilities.

Integration Architecture​

Analysis → Execution Flow​

Binance Integration Example​

Signal-Based Trading Bot​

Saxo Bank Multi-Asset Integration​

Portfolio Rebalancing Bot​

Polymarket Prediction Market Integration​

Event-Driven Trading​

Integration Best Practices​

Error Handling and Monitoring​

Position Sizing and Risk Management​

Logging and Auditing​

Security Considerations​

Advanced Integration Patterns​