Chaining Crypto Orders: A Proposal For Automated Trading

by Admin 57 views
Chaining Crypto Orders: A Proposal for Automated Trading

Hey guys! Let's dive into a cool way to automate your crypto trading. The main idea is to link pending orders together. So, imagine you set up an order to buy Bitcoin cheap, and once that order is filled, another order automatically kicks in to sell it at a higher price. This proposal outlines how we can make this happen, considering all the angles like user experience, edge cases, and, of course, making some sweet profit.

Proposal: Chained Pending Orders

The Core Concept

The central concept revolves around creating a dependency between two pending orders. Think of it as a domino effect: one order falling (being filled) triggers the next. In our specific scenario, we have a 'Buy' order (Buy_BTC_01) that, upon execution, activates a 'Sell' order (Sell_BTC_02). This allows for automated profit-taking based on predefined price levels. The goal is to make this system robust, easy to use, and reliable, ensuring traders can confidently set up chained orders without constant monitoring.

Configuration

The config.csv file will be the heart of this system. Currently, it holds the details of each pending order. We'll enhance it by adding an optional field: linked_order_id. This field will contain the ID of the order that should be activated upon the successful filling of the current order. If this field is left blank, the order will function as a standalone, independent order, maintaining backward compatibility and flexibility for users who don't need chained orders. The structure ensures that existing configurations remain valid while allowing users to opt-in to the new chained order functionality.

State Management

Deciding whether to add a filled column to config.csv or rely solely on state.csv is crucial. While state.csv likely already tracks the status of orders (pending, filled, cancelled, etc.), duplicating this information in config.csv could lead to data inconsistencies. Therefore, the proposal leans towards leveraging state.csv as the single source of truth for order status. We'll need to ensure that the system efficiently queries state.csv to determine when a triggering order has been filled.

User Interface (UI) and User Experience (UX)

Ease of use is paramount. The UI should clearly indicate which orders are linked and their dependencies. A simple visual representation, such as arrows connecting orders, could be implemented. When creating a new order, the user should have a straightforward way to select a 'linked order' from a dropdown or a similar intuitive control. Furthermore, clear and concise error messages should guide the user if they attempt to create illogical chains (e.g., linking an order to itself).

Financial Goals and Edge Cases

The primary financial goal is to automate profit-taking. However, several edge cases need careful consideration:

  • Order Partially Filled: What happens if the triggering order is only partially filled? Should the linked order be activated with a proportionally reduced size, or should it wait until the triggering order is completely filled? A configuration option could allow the user to choose their preferred behavior.
  • Order Cancellation: If the triggering order is cancelled before being filled, the linked order should also be automatically cancelled to prevent unintended trades.
  • Price Slippage: Significant price slippage between the filling of the triggering order and the activation of the linked order could result in the linked order being executed at an unfavorable price. We could implement a slippage tolerance setting to mitigate this risk. If the expected price deviates too much from the current market price, the linked order could be paused or cancelled, alerting the user.
  • Market Volatility: Extreme market volatility could lead to rapid filling of both orders, potentially resulting in unexpected profits or losses. Users should be educated about this risk and encouraged to use appropriate risk management techniques.

Critique of the Proposal

Potential Issues

  • Complexity: While the core concept is simple, implementing it robustly requires careful attention to detail. The added complexity could introduce bugs and make the system harder to maintain.
  • Performance: Constantly querying state.csv to check for order status changes could impact performance, especially with a large number of pending orders. We'll need to optimize these queries.
  • Error Handling: Robust error handling is crucial. The system needs to gracefully handle unexpected errors, such as database connection issues or invalid order IDs.

Alternative Solutions

  • Event-Driven Architecture: Instead of constantly polling state.csv, we could use an event-driven architecture. When an order is filled, an event is triggered, which then activates the linked order. This approach would be more efficient and scalable.
  • Integration with Existing Trading Bots: Instead of building this functionality from scratch, we could integrate it with existing trading bots or platforms that already offer similar features.

Addressing the Critiques

To address the potential issues, we can take the following steps:

  • Modular Design: Use a modular design to break down the system into smaller, more manageable components. This will make it easier to maintain and debug.
  • Caching: Implement caching to reduce the number of queries to state.csv. We can cache the status of frequently accessed orders.
  • Comprehensive Testing: Thoroughly test the system to identify and fix bugs. This should include unit tests, integration tests, and end-to-end tests.
  • Prioritize Event-Driven Architecture: As previously mentioned consider integrating the system into an event-driven architecture to improve efficiency and scalability.

Pseudo Code

function process_pending_orders():
  for each order in config.csv:
    order_id = order["order_id"]
    linked_order_id = order["linked_order_id"]

    if linked_order_id is not None:
      triggering_order_status = get_order_status(linked_order_id)

      if triggering_order_status == "filled":
        # Activate the current order
        activate_order(order_id)

function get_order_status(order_id):
  # Query state.csv to get the order status
  return status

function activate_order(order_id):
  # Submit the order to the exchange
  # Update the order status in state.csv
  pass

Critique of Pseudo Code

Potential Issues

  • Polling: The process_pending_orders function appears to be polling config.csv and state.csv repeatedly. This is inefficient.
  • Error Handling: The pseudo code lacks error handling. What happens if get_order_status fails?
  • Concurrency: The pseudo code doesn't consider concurrency. Multiple threads or processes could be trying to update state.csv at the same time, leading to race conditions.

Improvements

  • Event-Driven Approach: As mentioned earlier, an event-driven approach would be more efficient. Instead of polling, we could subscribe to order status updates from the exchange.
  • Error Handling: Add error handling to all functions. Log errors and take appropriate actions.
  • Concurrency Control: Use locking or other concurrency control mechanisms to prevent race conditions.

Final Code (Ready for Review)

import csv
import threading
import time

# Assuming state.csv is updated by another process/thread
STATE_FILE = "state.csv"
CONFIG_FILE = "config.csv"

# --- Thread safety --- #
state_lock = threading.Lock()

def get_order_status(order_id):
    with state_lock:
        with open(STATE_FILE, 'r') as f:
            reader = csv.DictReader(f)
            for row in reader:
                if row['order_id'] == order_id:
                    return row['status']
    return None # Order not found or error

def update_order_status(order_id, status):
    with state_lock:
        # Read all rows
        rows = []
        with open(STATE_FILE, 'r') as f:
            reader = csv.DictReader(f)
            rows = list(reader)

        # Modify the specific row
        for row in rows:
            if row['order_id'] == order_id:
                row['status'] = status
                break # Found and updated

        # Write back all rows
        with open(STATE_FILE, 'w', newline='') as f:
            writer = csv.DictWriter(f, fieldnames=rows[0].keys())
            writer.writeheader()
            writer.writerows(rows)

def activate_order(order_details):
    # Simulate submitting order to exchange (replace with actual exchange API call)
    print(f"Submitting order to exchange: {order_details}")
    update_order_status(order_details['order_id'], 'submitted')
    # Simulate order filling after some time
    time.sleep(5)  # Simulate time for order to fill
    update_order_status(order_details['order_id'], 'filled')
    print(f"Order {order_details['order_id']} filled!")


def process_pending_orders():
    while True:
        try:
            with open(CONFIG_FILE, 'r') as f:
                reader = csv.DictReader(f)
                for order in reader:
                    order_id = order['order_id']
                    linked_order_id = order.get('linked_order_id') # Use .get() for optional field

                    if linked_order_id:
                        triggering_order_status = get_order_status(linked_order_id)

                        if triggering_order_status == 'filled':
                            current_order_status = get_order_status(order_id)
                            if current_order_status != 'submitted' and current_order_status != 'filled':
                                print(f"Activating order {order_id} because linked order {linked_order_id} is filled.")
                                activate_order(order)

        except FileNotFoundError:
            print(f"Error: {CONFIG_FILE} or {STATE_FILE} not found.")
            time.sleep(10)
        except Exception as e:
            print(f"An error occurred: {e}")

        time.sleep(1)  # Check every 1 second

# Example Usage (assuming state.csv and config.csv exist)
if __name__ == "__main__":
    # Example state.csv (headers: order_id, status)
    # Example config.csv (headers: order_id, price, quantity, linked_order_id)

    # Example config.csv content:
    # order_id,price,quantity,linked_order_id
    # Buy_BTC_01,10000,0.01,
    # Sell_BTC_02,12000,0.01,Buy_BTC_01

    # Example state.csv content:
    # order_id,status
    # Buy_BTC_01,pending
    # Sell_BTC_02,pending

    process_thread = threading.Thread(target=process_pending_orders)
    process_thread.daemon = True  # Exit when main thread exits
    process_thread.start()

    # Keep the main thread alive
    while True:
        time.sleep(60)

This code provides a basic implementation of chained orders. It includes thread safety using locks, handles file exceptions, and simulates order execution. Remember that this code is a starting point and needs to be adapted to your specific exchange API and trading environment. This code emphasizes readability and includes comments to facilitate review. The example config.csv and state.csv contents should help the reviewer understand the expected data format.

Key improvements and considerations:

  • Thread Safety: Uses a threading.Lock to protect access to the state.csv file, preventing race conditions.
  • Error Handling: Includes try...except blocks to handle potential FileNotFoundError and other exceptions.
  • Optional linked_order_id: Uses order.get('linked_order_id') to gracefully handle the optional linked_order_id field.
  • Status Checks: Checks the current order status before activating it to prevent duplicate submissions.
  • Simulation: The activate_order function simulates order submission and filling. You'll need to replace this with actual exchange API calls.
  • Continuous Processing: The process_pending_orders function runs in a loop, continuously checking for linked orders that can be activated.

Further steps:

  • Replace Simulations: Replace the simulated order submission and filling with calls to your exchange's API.
  • Implement Robust Error Handling: Add more comprehensive error handling, including logging and retry mechanisms.
  • Add Configuration Options: Add configuration options to control the behavior of the system, such as slippage tolerance and partial fill handling.
  • Test Thoroughly: Thoroughly test the system in a simulated environment before deploying it to a live trading account.

By following this proposal and carefully reviewing the code, you can build a robust and reliable system for chaining crypto orders, automating your trading strategy, and maximizing your profits. Good luck, and happy trading!