How to Utilize Pine Script with Python: A Comprehensive Guide

TradingView’s Pine Script is an incredibly powerful tool for technical analysis, indicator development, and backtesting strategies directly on charts. Its strength lies in its ease of use within the TradingView environment, providing real-time data access and visualization capabilities. However, when it comes to complex statistical analysis, machine learning, high-frequency trading, or integrating with external systems and brokers, Pine Script has inherent limitations.

This is where Python, with its vast ecosystem of libraries for data science, automation, and system integration, becomes the perfect complement. Combining the charting and real-time data capabilities of Pine Script with the analytical and operational power of Python unlocks sophisticated trading workflows that are simply not possible with Pine Script alone.

The Power of Combining Pine Script and Python

The synergy between Pine Script and Python allows traders and developers to leverage the best of both worlds. Pine Script excels at:

• Rapidly prototyping and visualizing indicators on charts.
• Defining simple to moderately complex trading strategies based on technical patterns.
• Generating real-time signals directly within the TradingView platform.

Python, on the other hand, provides:

• Advanced data processing and analysis capabilities (e.g., NumPy, Pandas).
• Sophisticated statistical modeling and machine learning algorithms (e.g., SciPy, Scikit-learn, TensorFlow).
• Tools for database interaction, API integration, and automation.
• The ability to connect to various broker APIs for automated order execution.

By using Pine Script to identify potential trading opportunities and generate signals, and then using Python to receive these signals, perform deeper analysis, manage risk, and execute trades, a much more robust and automated system can be built.

Use Cases: Backtesting, Automation, and Advanced Analysis

The integration of Pine Script and Python opens up several advanced use cases:

• Advanced Backtesting and Optimization: While Pine Script offers built-in backtesting, Python allows for more granular control, custom performance metrics, walk-forward analysis, Monte Carlo simulations, and optimization techniques that go beyond brute-force parameter sweeps.
• Automated Trading Execution: Pine Script can send signals via webhooks, which a Python script can receive. This Python script can then communicate with a broker’s API to place, manage, and close orders automatically, enabling true algorithmic trading.
• Complex Signal Processing and Filtering: Pine Script signals can be further processed and filtered in Python using techniques like machine learning models, sentiment analysis, or correlation analysis across multiple assets before a trade is considered.
• Data Logging and Performance Monitoring: Python can log detailed trade data, market conditions, and system performance metrics to databases for later analysis and reporting, offering deeper insights than TradingView’s Strategy Tester report alone.
• Integration with External Data Sources: Python can fetch data from APIs outside of TradingView (e.g., fundamental data, news feeds, alternative data) and use it in conjunction with technical signals from Pine Script.

Prerequisites: Setting up Your Environment

To follow along and implement these techniques, you will need:

1. A TradingView account (paid plan required for webhook alerts).
2. A Python installation (version 3.7 or higher recommended).
3. Familiarity with Pine Script syntax and the TradingView platform.
4. Basic to intermediate knowledge of Python programming, including working with libraries.

You will also need to install specific Python libraries using pip. Key libraries include:

• requests: For making HTTP requests, essential for receiving webhook data.
• pandas: For data manipulation and analysis.
• Optional but highly useful:
  • numpy: For numerical operations.
  • ta-lib (or a Python alternative like pandas_ta): For technical analysis calculations in Python.
  • flask or django: To create a simple web server endpoint to receive webhook data.
  • Libraries specific to your chosen broker’s API.

Install them via your terminal:

pip install requests pandas numpy flask pandas_ta

Understanding Pine Script Data Extraction

Ping Script primarily operates within the isolated environment of the TradingView charting platform. While it has functions for accessing historical and real-time market data available on TradingView, it lacks built-in capabilities to directly interact with external databases, APIs, or file systems for exporting large volumes of data programmatically outside of the platform.

Limitations of Direct Data Access in Pine Script

The sandbox environment of Pine Script is designed for security and stability within TradingView. You cannot, for instance, write data directly to a local file on your computer, make arbitrary HTTP requests to external websites (outside of specific webhook functionality), or query an external database from within a Pine Script indicator or strategy.

Exporting historical bar data for extensive backtesting or analysis in Python usually involves manually downloading CSV data from TradingView charts, which is cumbersome and not suitable for automated workflows. Real-time data access outside of TradingView’s charting interface requires using their dedicated data APIs, which is separate from Pine Script itself.

Methods for Exporting Pine Script Data (Alerts, Webhooks)

The primary method for getting real-time signals or calculated data out of a running Pine Script and into an external system like Python is by using TradingView Alerts combined with Webhooks.

Here’s the typical flow:

1. Your Pine Script strategy or indicator identifies a condition you want to act upon (e.g., a buy signal, a specific price level hit, a calculated value crossing a threshold).
2. You use the alert() or alertcondition() function within your script to trigger an alert when this condition is met.
3. When setting up the alert on TradingView, you configure it to use a Webhook URL. This URL points to an endpoint you have set up on your server or a cloud function that is listening for incoming HTTP POST requests.
4. When the alert triggers, TradingView sends an HTTP POST request to the specified Webhook URL. The body of this request contains information about the alert, which you can customize.

This webhook payload is the bridge between Pine Script’s logic and your external Python script.

Structuring Data for Python Compatibility (JSON, CSV)

When configuring a webhook alert in TradingView, you can define the message that will be sent in the HTTP POST request body. To make this data easily consumable by Python, it’s best to structure it in a standard format like JSON or CSV.

JSON is generally preferred as it’s flexible and maps easily to Python dictionaries. In the TradingView alert message box, you can use special placeholders like {{strategy.order.action}}, {{strategy.order.contracts}}, {{strategy.order.price}}, {{ticker}}, {{interval}}, {{time}}, etc., to include dynamic data from your script or the chart.

A common practice is to format the alert message as a JSON string:

{
  "action": "{{strategy.order.action}}",
  "ticker": "{{ticker}}",
  "price": {{strategy.order.price}}, "
  "contracts": {{strategy.order.contracts}},
  "time": "{{time}}",
  "interval": "{{interval}}"
}

Your Python script receiving the webhook will then parse this JSON payload. Ensure that the data types match what you expect (e.g., numbers should not be quoted in JSON unless you intend to parse them as strings in Python). You can include any variable from your Pine Script using the + syminfo.ticker + syntax in the alert() function message, though constructing complex JSON strings directly within alert() can be cumbersome. The preferred method is using the alert message box placeholders which are specifically designed for this.

Python for Data Acquisition and Processing

Once your Pine Script is configured to send webhook alerts with structured data (preferably JSON), your Python environment needs to be set up to receive and process this information. This typically involves running a small web server or a serverless function that listens on the designated Webhook URL.

Setting up Python Libraries (requests, pandas, ta-lib)

As mentioned, you’ll need libraries like requests (often used implicitly by web frameworks), pandas for data handling, and optionally numpy and a TA library. For receiving webhooks, a lightweight web framework like Flask is ideal for simple setups.

Install Flask:

pip install Flask

Receiving Data from TradingView Alerts/Webhooks

A Flask application can expose an endpoint that corresponds to the Webhook URL you configured in TradingView. When TradingView sends an alert, it will make a POST request to this endpoint. Your Flask route function will capture this request.

Here’s a basic Flask example to receive a webhook:

from flask import Flask, request, jsonify
import json

app = Flask(__name__)

@app.route('/webhook', methods=['POST'])
def webhook():
    try:
        data = request.json # Assuming the payload is JSON
        if data is None:
            # Handle non-JSON or empty payload
            data = json.loads(request.data)

        print("Received Webhook Data:")
        print(json.dumps(data, indent=4))

        # Process the data here
        handle_tradingview_signal(data)

        return jsonify({"status": "success", "message": "Webhook received"}), 200

    except Exception as e:
        print(f"Error processing webhook: {e}")
        return jsonify({"status": "error", "message": str(e)}), 500

def handle_tradingview_signal(signal_data):
    # Implement your logic here based on the signal_data
    action = signal_data.get('action')
    ticker = signal_data.get('ticker')
    price = signal_data.get('price')
    contracts = signal_data.get('contracts')
    time = signal_data.get('time')

    print(f"Signal: {action} {contracts} of {ticker} at {price} ({time})")

    # Example: Log to a file, store in DB, or proceed to execution logic
    with open("trading_signals.log", "a") as f:
        f.write(f"{time},{ticker},{action},{price},{contracts}\n")

if __name__ == '__main__':
    # In a production environment, use a production-ready WSGI server like Gunicorn or uWSGI
    # and ensure your server is accessible from the internet on the specified URL/port.
    app.run(port=5000, debug=True) # Use debug=False in production!

To make this endpoint accessible from TradingView’s servers, you’ll need to deploy it to a server with a public IP address and a domain name, or use tunneling services like ngrok for local testing.

Data Cleaning and Transformation with Pandas

Once the signal data is received in Python, Pandas is invaluable for handling it. Although a single webhook might just contain one signal, you might aggregate data over time or combine it with other data sources.

If your webhook payload was more complex or if you were receiving data points incrementally (less common for TradingView webhooks, more for data feeds), you would use Pandas DataFrames. However, even for simple signal data, you can use Pandas Series or perform transformations if needed.

For instance, if you were receiving price data points via a different mechanism, you could use Pandas to calculate moving averages, volatility, or other indicators using libraries like pandas_ta:

import pandas as pd
import pandas_ta as ta

# Example: Assume you have a list of closing prices
prices = [100, 101, 102, 101.5, 103, 104, 103.5]
price_series = pd.Series(prices)

# Calculate an RSI
rsi_value = price_series.ta.rsi(length=14).iloc[-1] # Get the latest RSI
print(f"Latest RSI: {rsi_value}")

# This demonstrates that you can perform TA in Python on data received
# from TradingView or other sources.

In the context of webhook signals, you might use Pandas to:

• Timestamp signals accurately upon arrival.
• Combine signal data with other state information maintained by your Python system (e.g., current positions, account balance).
• Validate received data types and ranges.

Implementing Trading Strategies with Python and Pine Script

The core idea is that Pine Script identifies a potential trading opportunity and signals it, while Python makes the final decision and handles execution. Pine Script becomes the ‘eyes’ on the chart, and Python becomes the ‘brain’ and ‘hands’.

Developing Trading Logic in Python

While Pine Script provides the initial alert based on technical conditions, your Python script can contain the more complex parts of your strategy logic. This might include:

• Position Sizing: Calculating the exact number of shares or contracts based on account size, risk tolerance, and volatility (e.g., using the Kelly criterion or fixed fractional sizing).
• Risk Management: Checking if the potential trade aligns with overall portfolio risk limits, existing positions, or daily loss limits.
• Filtering: Applying additional filters not easily implemented in Pine Script, such as checking news sentiment, fundamental data points, or machine learning model outputs.
• Multi-asset Coordination: Managing a portfolio of different assets, ensuring diversification, and avoiding over-exposure.
• State Management: Keeping track of open positions, past trade performance, and other persistent data needed for decision making.

Your handle_tradingview_signal function (or a system it calls) would contain this logic:

def handle_tradingview_signal(signal_data, current_positions, account_balance):
    action = signal_data.get('action')      # e.g., 'buy', 'sell', 'sell' (for close), 'buy' (for close)
    ticker = signal_data.get('ticker')
    signal_price = signal_data.get('price')
    signal_contracts = signal_data.get('contracts') # Pine script contracts might be just 1 or -1 for signal
    time = signal_data.get('time')

    print(f"Processing signal: {action} on {ticker} at {signal_price}")

    # --- Python-side Logic --- 
    if action == 'buy': # Assuming 'buy' from Pine means enter long
        if ticker in current_positions and current_positions[ticker]['side'] == 'long':
            print("Already long. Handle scaling in if needed.")
            # Logic for scaling in or ignoring signal
        elif ticker in current_positions and current_positions[ticker]['side'] == 'short':
             print("Currently short. Handle closing short before going long.")
             # Logic to send close short order
             send_order(ticker, 'close', 'short') # Placeholder
             # Then send long order
             calculated_size = calculate_position_size(account_balance, ticker, risk_params)
             send_order(ticker, 'buy', 'long', size=calculated_size)
             current_positions[ticker] = {'side': 'long', 'size': calculated_size}
        else:
            print("Not in a position. Calculate size and enter long.")
            calculated_size = calculate_position_size(account_balance, ticker, risk_params)
            send_order(ticker, 'buy', 'long', size=calculated_size)
            current_positions[ticker] = {'side': 'long', 'size': calculated_size}

    elif action == 'sell': # Assuming 'sell' from Pine means enter short
         # Similar logic for managing short positions
         pass # ... implementation omitted for brevity ...

    # Pine Script strategy.exit() or strategy.close() could also trigger alerts.
    # You'd need to parse the signal_data to understand if it's an entry or exit.
    # Custom alert messages are key here.
    # Example: {{"type": "exit", "ticker": "{{ticker}}", "reason": "{{strategy.exit.comment}}"}}

    elif signal_data.get('type') == 'exit': # Custom type from alert message
        print(f"Received exit signal for {ticker}.")
        if ticker in current_positions:
             side_to_close = current_positions[ticker]['side']
             print(f"Closing {side_to_close} position for {ticker}.")
             send_order(ticker, 'close', side_to_close)
             del current_positions[ticker]
        else:
            print(f"Received exit signal for {ticker} but no active position.")

    # Update account_balance and current_positions based on executed trades (requires broker feedback)

def calculate_position_size(balance, ticker, params):
    # Implement your position sizing logic (e.g., ATR based, fixed risk %)
    return 10 # Placeholder

def send_order(ticker, type, side, size=None):
    # This function would interface with your broker API
    print(f"Executing Order: {type} {side} {size if size else ''} on {ticker}")
    # Example: broker_api.place_order(symbol=ticker, type=type, side=side, qty=size)
    pass # Placeholder

# Example Usage (within your webhook receiver):
# current_positions = {'AAPL': {'side': 'long', 'size': 50}} # Example state
# account_balance = 100000 # Example state
# handle_tradingview_signal(data, current_positions, account_balance)

This structure separates the signal generation (Pine Script) from the execution logic (Python), making both parts more manageable and testable.

Sending Trading Signals to TradingView (Webhooks)

It’s important to clarify that webhooks are primarily used for Pine Script to send signals out. Python doesn’t typically send signals into a running Pine Script instance to trigger trades within TradingView’s backtester or paper trading engine based on Pine’s strategy logic. If you want Python to control execution based on Pine’s signals, Python receives the signal and interacts directly with a broker API.

However, you can use webhooks to send data from Python to another external system, or potentially to trigger actions within a TradingView chart if you were using something like TradingView’s Broker API programmatically (a different, more complex topic). For the common Pine-to-Python automation flow, the data direction for signals is Pine -> Python -> Broker.

If your goal was to get data into Pine Script from Python (e.g., custom indicator values, news sentiment scores), you would typically need to feed that data into a custom indicator or symbol on TradingView via their Data API or a third-party service that provides custom data feeds compatible with TradingView.

Automating Order Execution (Paper Trading & Broker Integration Considerations)

After your Python script receives a signal from Pine Script and decides to act, the next step is automated order execution. This requires integrating with a broker’s API.

• Paper Trading: Many brokers offer a paper trading environment accessible via their API. This is crucial for testing your entire system (Pine Script signal generation + Python logic + API communication) without risking real capital.
• Live Trading: Once confident, you switch to the live trading environment of your broker’s API.

Each broker has its own API with different libraries and methods (e.g., Alpaca, Interactive Brokers (IB-insync library), MetaTrader 5 (MetaTrader5 library), Binance, Coinbase Pro, etc.). Your send_order function will be specific to the broker you use.

Key considerations for order execution:

• API Keys and Authentication: Securely manage your broker API credentials.
• Order Types: Support for various order types (market, limit, stop, etc.) that your strategy requires.
• Error Handling: What happens if an order fails? Implement retry logic and robust error reporting.
• Position Management: Your Python script needs to track open positions, average entry prices, and realized/unrealized P&L.
• Account Balance Monitoring: Keep track of your available capital and margin.
• Rate Limits: Be mindful of broker API rate limits to avoid getting blocked.

Integrating with a broker API is a significant step that requires careful testing and understanding of the broker’s specific implementation details.

Advanced Techniques and Best Practices

Building a reliable Pine Script and Python trading system involves more than just sending and receiving signals. Attention to detail in data handling, error management, and system architecture is crucial.

Optimizing Data Transfer and Communication

• Minimize Webhook Payload: Only send necessary data in the alert message. Avoid sending large arrays or complex objects if simpler data will suffice. This reduces latency and potential errors.
• Use a Dedicated Server/Service: Run your Python webhook receiver on a reliable server (VPS, cloud instance like AWS EC2/Lambda, Google Cloud Functions, Azure Functions) rather than your local machine, which might have network issues or downtime.
• Asynchronous Processing: If your Python logic after receiving a webhook takes time (e.g., calling multiple external APIs), consider processing the webhook request quickly and then passing the data to a background worker queue (like Celery with RabbitMQ or Redis) to avoid blocking the webhook endpoint. TradingView might time out if your endpoint doesn’t respond quickly.

Error Handling and Robustness

• Pine Script Errors: Use log.info(), log.warning(), log.error() in Pine Script to log internal issues. Handle potential na values carefully.
• Webhook Receiver Errors: Implement try...except blocks in your Python webhook receiver to catch parsing errors, missing data keys, or issues with your processing logic. Log errors thoroughly.
• Broker API Errors: Handle exceptions when placing orders or querying account information. Understand specific API error codes.
• Idempotency: Design your Python signal handling to be idempotent if possible. If a webhook is sent multiple times due to network issues, processing it twice shouldn’t lead to unintended duplicate actions (e.g., check if an order was already placed for a given signal ID).
• Monitoring: Set up monitoring and alerting for your Python script. Be notified if the webhook receiver goes down, starts throwing errors, or if no signals are received for an extended period when expected.

Security Considerations when Using Webhooks

Opening a public endpoint to receive webhooks introduces security risks. Consider the following:

• Authentication: TradingView webhooks don’t have a built-in, universally standard authentication mechanism like OAuth. You can implement a form of shared secret: include a unique key or token in your webhook URL or payload from TradingView and verify it in your Python script. If the token doesn’t match, reject the request.
  • Example (in Alert Message or URL parameter): .../webhook?token=YOUR_SECRET_TOKEN
  • Python Check: if request.args.get('token') != 'YOUR_SECRET_TOKEN': return "Unauthorized", 401
• HTTPS: Always use HTTPS for your webhook URL to encrypt the data in transit.
• Input Validation: Strictly validate the data received in the webhook payload. Do not blindly trust the incoming data.
• Least Privilege: The system running your webhook receiver should only have necessary permissions.
• Logging: Log all incoming webhook requests (origin IP, headers, payload – carefully redacting sensitive info) for auditing and debugging.

Example: A Complete Pine Script and Python Trading System (Conceptual)

Let’s outline a simple system:

Pine Script (MyStrategy.pine):

//@version=5
strategy("Simple MA Crossover Signal", overlay=true)

fast_ma_len = input.int(10, "Fast MA Length")
slow_ma_len = input.int(30, "Slow MA Length")

fast_ma = ta.sma(close, fast_ma_len)
slow_ma = ta.sma(close, slow_ma_len)

// Define signals
long_signal = ta.crossover(fast_ma, slow_ma)
short_signal = ta.crossunder(fast_ma, slow_ma)

// Strategy logic (optional in Pine, but good for backtesting viz)
strategy.entry("Long", strategy.long, when = long_signal)
strategy.entry("Short", strategy.short, when = short_signal)
strategy.close("Long", when = short_signal) // Exit long on opposite signal
strategy.close("Short", when = long_signal)  // Exit short on opposite signal

// --- Alerting --- 
// Use custom alert messages to send structured data
long_msg = '{ "type": "entry", "side": "long", "ticker": "{{ticker}}", "price": {{close}}, "time": "{{timenow}}" }'
short_msg = '{ "type": "entry", "side": "short", "ticker": "{{ticker}}", "price": {{close}}, "time": "{{timenow}}" }'
exit_long_msg = '{ "type": "exit", "side": "long", "ticker": "{{ticker}}", "price": {{close}}, "time": "{{timenow}}"}, "reason": "MA Cross" }'
exit_short_msg = '{ "type": "exit", "side": "short", "ticker": "{{ticker}}", "price": {{close}}, "time": "{{timenow}}"}, "reason": "MA Cross" }'

if long_signal
    alert(long_msg, alert.freq_once_per_bar_close)

if short_signal
    alert(short_msg, alert.freq_once_per_bar_close)

// Alert for exits triggered by strategy.close/exit
// Note: Capturing strategy exits accurately via alerts requires careful message design
// or separate alertconditions based on exit logic.
// A simpler approach might be to let Python manage position state and determine exit actions.

// Example using alertcondition for exits (less robust for strategy.close)
// alertcondition(ta.crossunder(fast_ma, slow_ma) and strategy.position_size[1] > 0, title='Exit Long Alert', message=exit_long_msg)
// alertcondition(ta.crossover(fast_ma, slow_ma) and strategy.position_size[1] < 0, title='Exit Short Alert', message=exit_short_msg)

// A more reliable approach for exit signals is often to base them on the same conditions 
// as entries but check current position state within Pine *before* alerting, 
// or let the Python script figure out if a signal implies an exit based on current Python-side state.

TradingView Alert Setup:

• Create an alert on the chart using the MyStrategy script.
• Condition: MyStrategy + Any alert function call.
• Options: Once Per Bar Close (recommended for strategies).
• Webhook URL: https://your-public-server.com/webhook
• Message: Use the JSON message formats defined in the Pine Script alert() calls (or simpler placeholders if defining message here).

Python Script (receiver.py):

This would be the Flask application from the earlier example, but with the handle_tradingview_signal function connected to a broker API.

from flask import Flask, request, jsonify
import json
# import your_broker_api as broker

app = Flask(__name__)

# In-memory state (for simplicity, use a database in production)
current_positions = {}
account_balance = 100000 # Load from broker/DB on startup
SECRET_TOKEN = "YOUR_SECRET_TOKEN"

# Initialize broker API (credentials securely loaded)
# broker.init(api_key="...", secret_key="...") 

@app.route('/webhook', methods=['POST'])
def webhook():
    # Basic Security Check
    # if request.args.get('token') != SECRET_TOKEN:
    #     print("Unauthorized request")
    #     return "Unauthorized", 401

    try:
        data = request.json
        if data is None:
             data = json.loads(request.data)

        print(f"[{datetime.datetime.now()}] Received signal: {json.dumps(data)}")

        handle_tradingview_signal(data)

        return jsonify({"status": "success"}), 200

    except Exception as e:
        print(f"[{datetime.datetime.now()}] Error processing webhook: {e}")
        # Log traceback
        import traceback
        print(traceback.format_exc())
        return jsonify({"status": "error", "message": str(e)}), 500

def handle_tradingview_signal(signal_data):
    global current_positions, account_balance

    signal_type = signal_data.get('type') # 'entry' or 'exit'
    side = signal_data.get('side')       # 'long' or 'short'
    ticker = signal_data.get('ticker')
    price = signal_data.get('price')
    timestamp = signal_data.get('time')

    print(f"Handling {signal_type} signal for {ticker} ({side}) at {price}")

    if signal_type == 'entry':
        if ticker in current_positions and current_positions[ticker]['side'] == side:
            print(f"Already in a {side} position for {ticker}. Ignoring entry signal or scaling in.")
            # Add logic for scaling in if desired
        elif ticker in current_positions and current_positions[ticker]['side'] != side:
            print(f"Currently in a {current_positions[ticker]['side']} position for {ticker}. Need to close first.")
            # Implement logic to close opposite position
            # send_order(ticker, 'close', current_positions[ticker]['side'])
            # del current_positions[ticker]
            # Then proceed with new entry after confirmation of close
            # calculated_size = calculate_position_size(account_balance, ticker)
            # send_order(ticker, 'limit', side, size=calculated_size, limit_price=price)
            # current_positions[ticker] = {'side': side, 'size': calculated_size}
        else:
            print(f"Entering new {side} position for {ticker}.")
            calculated_size = calculate_position_size(account_balance, ticker)
            if calculated_size > 0:
                # Use a limit order at the signal price to avoid slippage on execution
                # send_order(ticker, 'limit', side, size=calculated_size, limit_price=price)
                # For live trading, you'd need to check order execution status via broker API
                current_positions[ticker] = {'side': side, 'size': calculated_size, 'entry_price': price}
                print(f"Attempted to open {side} {calculated_size} of {ticker} at {price}.")
            else:
                 print("Calculated position size is zero.")

    elif signal_type == 'exit':
         # Note: This assumes Pine sends an explicit exit signal
         if ticker in current_positions and current_positions[ticker]['side'] == side:
             print(f"Exiting {side} position for {ticker}.")
             # send_order(ticker, 'close', side) # Or 'limit'/'market' order
             # Check broker API if order successful before deleting position state
             del current_positions[ticker]
             print(f"Attempted to close {side} position for {ticker}.")
         else:
             print(f"Received {side} exit signal for {ticker} but no matching position found.")

    # TODO: Add functions to interact with broker API (send_order, get_positions, get_balance, check_order_status)
    # TODO: Update account_balance and current_positions based on actual trades reported by broker API

def calculate_position_size(balance, ticker):
     # Implement robust position sizing based on risk metrics, volatility, etc.
     # Example: Risk 1% of equity per trade, size based on distance to stop loss
     return 10 # Simple placeholder

# def send_order(ticker, order_type, side, size=None, limit_price=None):
#     # This is where your broker API calls go
#     print(f"Simulating sending order: {order_type} {side} {size} {ticker} at {limit_price}")
#     # Example using a hypothetical broker library:
#     # if order_type == 'limit':
#     #     order_id = broker.place_limit_order(symbol=ticker, side=side, qty=size, price=limit_price)
#     # elif order_type == 'market':
#     #     order_id = broker.place_market_order(symbol=ticker, side=side, qty=size)
#     # elif order_type == 'close':
#     #     order_id = broker.close_position(symbol=ticker, side=side)
#     # return order_id # Return order ID for tracking

import datetime

if __name__ == '__main__':
    print("Starting webhook receiver...")
    # Replace with a production WSGI server in a real application
    app.run(host='0.0.0.0', port=5000, debug=True) # Use debug=False in production!

This example provides a basic framework. A production system would require more sophisticated state management (using a database), comprehensive error handling, robust position sizing, proper logging, security hardening, and detailed integration with a specific broker API.

By understanding how to effectively send data out of Pine Script and process it within Python, you can build highly customized and automated trading systems that leverage the strengths of both platforms.