[TOC]

Introduction

Recently, users have left comments in Teacher Xiaocao’s article “How to Quickly Build Universal Multi-Currency Trading Strategies After FMZ Upgrade” requesting a Python implementation version of that strategy framework. To meet the needs of Python developers, this article will build upon Teacher Xiaocao’s original concepts and provide detailed explanations of how to construct a universal multi-currency quantitative trading framework, combined with Binance simulation trading environment. This framework features:

• Based on Python language, easy to understand and extend
• Supports multi-currency concurrent trading
• Uses Binance simulation exchange for safe testing
• Provides complete code examples with detailed comments

Through sharing this article, we hope to help everyone quickly get started with multi-currency quantitative trading development. We also welcome developers to innovate and optimize based on this foundation, creating more comprehensive personalized trading strategies. Let’s continue exploring together on the path of quantitative trading!

Strategy Initialization

In the strategy initialization phase, we first define global variables SYMBOLS, QUOTO, and INTERVAL, representing target trading currencies, base currency, and interval time. The Info variable stores all key data needed during strategy execution. Then, through the InitInfo function, we initialize this data, including account information, time management, market data for each trading currency, order information, position information, etc. These initialization steps ensure the strategy has clear data structures and initial states during runtime, laying the foundation for subsequent strategy execution.

import time
import json

SYMBOLS = 'LINK,ETH,TRB'
QUOTO = 'USDT'
INTERVAL = 5

# Global variable to store data
# SYMBOLS represents currencies to trade, format like "BTC,ETH,LTC"
# QUOTO is the base currency, common ones for perpetual contracts are USDT, USDC
# INTERVAL represents the loop interval

Info = {
    'trade_symbols': SYMBOLS.split(','),  # Trading currencies
    'base_coin': QUOTO,  # Base currency
    'ticker': {},  # Market data
    'order': {},  # Order information
    'account': {},  # Account information
    'precision': {},  # Precision information
    'position': {},  # Position information
    'time': {},  # Time-related data
    'count': {},  # Counters
    'interval': INTERVAL  # Loop interval time
}

# Initialize strategy
def InitInfo():
    # Initialize account information, initial balance is 0
    Info['account'] = {
        'init_balance': 0,  # Initial balance
        'wallet_balance': 0,  # Wallet balance
        'margin_balance': 0,  # Margin balance
        'margin_used': 0,  # Used margin
        'margin_free': 0,  # Available margin
        'profit': 0,  # Total profit
        'profit_rate': 0,  # Profit rate
        'unrealised_profit': 0,  # Unrealized profit
    }
    
    # Initialize time data to control update timing
    Info['time'] = {
        'update_ticker_time': 0,  # Time for updating market data
        'update_pos_time': 0,  # Time for updating positions
        'update_profit_time': 0,  # Time for updating profit
        'update_account_time': 0,  # Time for updating account info
        'update_status_time': 0,  # Time for updating status
        'last_loop_time': 0,  # Last main loop time
        'loop_delay': 0,  # Loop delay
        'start_time': time.time()
    }
    
    # Initialize data for each trading currency
    for symbol in Info['trade_symbols']:
        Info['ticker'][symbol] = {'last': 0, 'ask': 0, 'bid': 0}  # Market data
        Info['order'][symbol] = {  # Order information
            'buy': {'id': 0, 'price': 0, 'amount': 0},
            'sell': {'id': 0, 'price': 0, 'amount': 0}
        }
        Info['position'][symbol] = {  # Position information
            'amount': 0, 'hold_price': 0, 'unrealised_profit': 0, 'open_time': 0, 'value': 0
        }
        Info['precision'][symbol] = {}  # Initialize precision information as empty

Getting Precision Information

This step ensures that the currencies we handle comply with exchange standards in terms of price, quantity precision, and order amounts. Different trading pairs (such as BTC/USD or ETH/USDT) have different minimum precision for price and quantity, so we need to obtain precision information from the exchange API to avoid non-compliant orders. For example, ETH/USDT might allow two decimal places, but BTC/USDT might allow eight decimal places.

Purpose:

• Obtain price and quantity precision for each currency to ensure order data correctness
• Prevent order failures due to precision errors

# Get precision information
def GetPrecision():
    # Get exchange market information
    for presym in Info['trade_symbols']:
        curcontract = presym + '_USDT.swap'
        exchange.GetTicker(curcontract)
        exchange_info = exchange.GetMarkets()
        
        # Traverse all trading pairs in the market
        for pair, data in exchange_info.items():
            symbol = pair.split('_')[0]  # Perpetual contract format is BTC_USDT.swap
            
            # Check if this trading pair is one we want to trade, base currency matches, and is perpetual contract
            if symbol in Info['trade_symbols'] and pair.split('.')[0].endswith(Info['base_coin']) and pair.endswith('swap'):
                # Get precision information for this trading pair
                Info['precision'][symbol] = {
                    'tick_size': data['TickSize'],  # Price precision
                    'amount_size': data['AmountSize'],  # Quantity precision
                    'price_precision': data['PricePrecision'],  # Price decimal precision
                    'amount_precision': data['AmountPrecision'],  # Quantity decimal precision
                    'min_qty': data['MinQty'],  # Minimum order quantity
                    'max_qty': data['MaxQty'],  # Maximum order quantity
                    'min_notional': data['MinNotional'],  # Minimum trade amount
                    'ctVal': data['CtVal']  # Contract value, e.g., 1 contract represents 0.01 coins
                }
                
                # Check if contract value currency is symbol to avoid coin-margined situations
                if data['CtValCcy'] != symbol:
                    raise Exception("Coin-margined contracts not supported")

GetPrecision Function:

• Obtains exchange market information through API, including precision settings for trading pairs
• For qualifying trading pairs, records their price precision, quantity precision, minimum and maximum trading volumes, and contract value information
• Checks whether it’s coin-margined trading, and throws an exception if it is

Updating Market Data

Obtain current market data through API interface, such as latest price, best bid, best ask, order book depth, etc. This information is crucial for subsequent trading decisions. Depending on different strategies, this data can be based on candlesticks (OHLC data) or tick-by-tick trading data.

Purpose:

• Obtain the latest trading data from current market for strategy to make buy/sell decisions based on this data
• Include real-time and historical data acquisition for technical indicator calculations

# Update market information
def UpdateTicker():
    # Record current update timestamp
    Info['time']['update_ticker_time'] = time.time() * 1000  # Use time.time() to get current timestamp
    
    # Traverse obtained market data
    for ticpre in Info['trade_symbols']:
        curcontract = ticpre + '_' + QUOTO + '.swap'
        data = exchange.GetTicker(curcontract)
        if not data:
            Log("Failed to get market data", GetLastError())
            return
            
        # Extract trading currency, perpetual contract format like 'BTC_USDT.swap', here take currency name 'BTC'
        symbol = data['Symbol'].split('_')[0]
        
        # Filter out trading pairs that are not base currency or not perpetual contracts
        if not data['Symbol'].split('.')[0].endswith(Info['base_coin']) or symbol not in Info['trade_symbols'] or not data['Symbol'].endswith('swap'):
            continue
            
        # Update market ask price, bid price, and last traded price
        Info['ticker'][symbol]['ask'] = float(data['Sell'])  # Ask price
        Info['ticker'][symbol]['bid'] = float(data['Buy'])  # Bid price
        Info['ticker'][symbol]['last'] = float(data['Last'])  # Last traded price

1、Get Market Data:

Use exchange.GetTicker to obtain real-time market information for all trading pairs. The data variable stores all trading pair information.

2、Error Handling:

If market data cannot be successfully obtained, the program uses the Log function to record logs and output error information returned by GetLastError.

3、Update Time:

Use time.time to record current timestamp, used to mark the time of market data updates.

4、Data Filtering:

Extract trading currency names from market data. Filter out non-qualifying trading pairs, such as trading pairs not settled in base currency, or those that are not perpetual contracts.

5、Update Market Data:

For qualifying trading currencies, update their bid price, ask price, and last traded price.

Through this function, the system can obtain and update the latest market information for target trading currencies in real-time.

Updating Account and Position Information

Obtain account balance and current position status (purchased currencies, quantities, cost prices, etc.) through API. This step is crucial for evaluating available funds, calculating risks, and managing positions. For example, current positions determine whether to add or reduce positions.

Purpose:

• Ensure sufficient funds or positions in account to execute trades
• Decide trade size and direction based on current account positions and fund status

# Update account information
def UpdateAccount():
    # If last update was less than 1 minute ago, return directly
    if time.time() - Info['time']['update_account_time'] < 60:
        return
        
    # Record account information update time
    Info['time']['update_account_time'] = time.time() * 1000
    
    # Get account information
    account = exchange.GetAccount()
    
    # If account information retrieval failed, log and return
    if account is None:
        Log("Failed to update account")
        return
        
    # Calculate account information
    Info['account']['margin_used'] = round(account['Equity'] - account['Balance'], 2)  # Used margin
    Info['account']['margin_balance'] = round(account['Equity'], 2)  # Current account balance
    Info['account']['margin_free'] = round(account['Balance'], 2)  # Available balance
    Info['account']['wallet_balance'] = round(account['Equity'] - account['UPnL'], 2)  # Wallet balance
    Info['account']['unrealised_profit'] = round(account['UPnL'], 2)  # Unrealized P&L
    
    # Initialize account initial balance
    if not Info['account']['init_balance']:
        if _G("init_balance") and _G("init_balance") > 0:
            Info['account']['init_balance'] = round(_G("init_balance"), 2)
        else:
            Info['account']['init_balance'] = Info['account']['margin_balance']
            _G("init_balance", Info['account']['init_balance'])
    
    # Calculate account profit and profit rate
    Info['account']['profit'] = round(Info['account']['margin_balance'] - Info['account']['init_balance'], 2)
    Info['account']['profit_rate'] = round((100 * Info['account']['profit']) / Info['account']['init_balance'], 2)
    
    # Calculate total position value and leverage ratio
    Info['count']['total'] = round(Info['count']['long'] + Info['count']['short'], 2)
    Info['count']['leverage'] = round(Info['count']['total'] / Info['account']['margin_balance'], 2)

# Update position information
def UpdatePosition():
    # Get perpetual contract position information
    pos = exchange.GetPositions(Info['base_coin'] + ".swap")
    
    # Record position information update time
    Info['time']['update_pos_time'] = time.time() * 1000
    
    # Initialize position information
    position_info = {symbol: {'amount': 0, 'hold_price': 0, 'unrealised_profit': 0} for symbol in Info['trade_symbols']}
    
    # Traverse position information, update corresponding currency positions
    for data in pos:
        symbol = data['Symbol'].split("_")[0]
        
        # Filter out currencies that don't meet conditions
        if not data['Symbol'].split(".")[0].endswith(Info['base_coin']) or symbol not in Info['trade_symbols']:
            continue
            
        # If position is not zero, one-way position holding is required
        if position_info[symbol]['amount'] != 0:
            raise Exception("One-way position holding required")
            
        # Update position information
        position_info[symbol] = {
            'amount': data['Amount'] * Info['precision'][symbol]['ctVal'] if data['Type'] == 0 else -data['Amount'] * Info['precision'][symbol]['ctVal'],
            'hold_price': data['Price'],
            'unrealised_profit': data['Profit']
        }
    
    # Initialize position statistics
    Info['count'] = {'long': 0, 'short': 0, 'total': 0, 'leverage': 0}
    
    # Traverse updated position information
    for symbol, info in position_info.items():
        deal_volume = abs(info['amount'] - Info['position'][symbol]['amount'])
        direction = 1 if info['amount'] - Info['position'][symbol]['amount'] > 0 else -1
        
        # If position changed, record trade information
        if deal_volume:
            deal_price = Info['order'][symbol]['buy']['price'] if direction == 1 else Info['order'][symbol]['sell']['price']
            Log(
                symbol,
                "Position update:",
                round(Info['position'][symbol]['value'], 1),
                " -> ",
                round(info['amount'] * Info['ticker'][symbol]['last'], 1),
                ", Buy" if direction == 1 else ", Sell",
                ", Deal price:",
                deal_price,
                ", Cost price:",
                round(Info['position'][symbol]['hold_price'], Info['precision'][symbol]['price_precision']),
            )
        
        # Update position information
        Info['position'][symbol]['amount'] = info['amount']
        Info['position'][symbol]['hold_price'] = info['hold_price']
        Info['position'][symbol]['value'] = round(Info['position'][symbol]['amount'] * Info['ticker'][symbol]['last'], 2)
        Info['position'][symbol]['unrealised_profit'] = info['unrealised_profit']
        
        # Count long and short position values
        if Info['position'][symbol]['amount'] > 0:
            Info['count']['long'] += abs(Info['position'][symbol]['value'])
        else:
            Info['count']['short'] += abs(Info['position'][symbol]['value'])

1、UpdateAccount Function:

• Check Time Interval: When updating account each time, ensure more than 1 minute has passed since last update to avoid overly frequent updates.
• Update Account Information: Call exchange.GetAccount to get account information and calculate various account parameters such as margin_used, wallet_balance, unrealised_profit, etc.
• Initialize Balance: Initialize the account’s initial balance on first run and save it in global variable Info[‘account’][‘init_balance’].
• Calculate Profit: Calculate profit and profit rate based on current balance and initial balance.

2、UpdatePosition Function:

• Get Position Information: Call exchange.GetPositions() to get current position status.
• Update Positions: Update target currency position information based on obtained position data (such as amount, hold_price, unrealised_profit, etc.).
• Calculate Leverage: Calculate overall leverage based on current long and short position values and margin balance.

Through these two functions, the strategy can continuously monitor account status and position changes, providing real-time data support for subsequent trading decisions.

Order Execution

Execute buy/sell operations based on strategy logic. Can be market orders, limit orders, or other order types. This step involves interacting with exchange APIs to send buy or sell requests. Successful order execution affects account balance and positions.

Purpose:

• Execute trading instructions through API to complete buy/sell operations
• Ensure order types and quantities comply with strategy requirements and exchange specifications

# Order function
def Order(symbol, direction, price, amount, msg):
    pair = f"{symbol}_{Info['base_coin']}.swap"  # Construct trading pair name
    ret = exchange.CreateOrder(pair, direction, price, amount, msg)  # Execute order
    
    # Check if order was successful
    if ret:
        Info['order'][symbol][direction]['id'] = ret  # Record order ID
        Info['order'][symbol][direction]['price'] = price  # Record order price
    else:
        Log(f"{symbol} {direction} {price} {amount} Order exception")  # Output exception info

# Trading function
def Trade(symbol, direction, price, amount, msg):
    # Adjust price according to minimum price movement
    price = round(price - (price % Info['precision'][symbol]['tick_size']), Info['precision'][symbol]['price_precision'])
    
    # Calculate adjusted trading quantity
    amount = amount / Info['precision'][symbol]['ctVal']  # Calculate actual contract quantity
    amount = round(amount - (amount % Info['precision'][symbol]['amount_size']), Info['precision'][symbol]['amount_precision'])
    
    # Limit maximum trading quantity
    if Info['precision'][symbol]['max_qty'] > 0:
        amount = min(amount, Info['precision'][symbol]['max_qty'])
    
    new_order = False
    
    # If new price differs from previous order price by more than 0.0001, place new order
    if Info['order'][symbol][direction]['price'] > 0 and abs(price - Info['order'][symbol][direction]['price']) / price > 0.0001:
        Log('Existing order, order price changed')
        new_order = True
    
    # If trading quantity is 0 or current order ID is 0, cancel order
    if amount <= 0 or Info['order'][symbol][direction]['id'] == 0:
        Log('New order generated')
        new_order = True
    
    # If new order is needed
    if new_order:
        # If there's an existing order, cancel it
        if Info['order'][symbol][direction]['id'] != 0:
            exchange.CancelOrder(Info['order'][symbol][direction]['id'])
            Info['order'][symbol][direction]['id'] = 0
            Info['order'][symbol][direction]['price'] = 0
            Log('Order cancelled successfully:', symbol)
        
        # If position or ticker update delay is too high, don't place order
        if (time.time() * 1000 - Info['time']['update_pos_time'] > 2 * Info['interval'] * 1000 or
            time.time() * 1000 - Info['time']['update_ticker_time'] > 2 * Info['interval'] * 1000):
            Log(time.time() * 1000, Info['time']['update_pos_time'], time.time() * 1000 - Info['time']['update_pos_time'])
            Log(time.time() * 1000, Info['time']['update_ticker_time'], time.time() * 1000 - Info['time']['update_ticker_time'])
            Log('Delay too high')
            return
        
        # If order amount or quantity is too low, don't execute order
        if price * amount <= Info['precision'][symbol]['min_notional'] or amount < Info['precision'][symbol]['min_qty']:
            Log(f"{symbol} Order amount too low", price * amount)
            return
        
        # Execute order
        Log('Placing order:', symbol)
        Order(symbol, direction, price, amount, msg)

• Order函数 - 负责创建订单，处理成功和失败情况。
• Trade函数 - 处理价格和数量调整，管理订单的撤销和重新下单逻辑。
• CancelOrder函数 - 自定义撤单操作功能。

Status Display

Real-time display of strategy running status, including account balance, current positions, trade execution status, current market prices, etc. This step is not only for monitoring strategy performance but also for facilitating strategy optimization and debugging by quickly understanding strategy performance.

Purpose:

• Timely display key information about strategy execution for user monitoring
• Provide feedback mechanism to show whether trade execution is successful or if errors exist

# Update status function
def UpdateStatus():
    # If less than 4 seconds since last update, return directly
    if time.time() * 1000 - Info['time']['update_status_time'] < 4000:
        return
    
    # Update status time
    Info['time']['update_status_time'] = time.time() * 1000
    
    # Account information table
    table1 = {
        "type": "table",
        "title": "Account Information",
        "cols": [
            "Initial Balance", "Wallet Balance", "Margin Balance", "Used Margin", "Available Margin",
            "Total Profit", "Profit Rate", "Unrealized Profit", "Total Position", "Used Leverage", "Loop Delay"
        ],
        "rows": [
            [
                Info['account']['init_balance'],  # Initial balance
                Info['account']['wallet_balance'],  # Wallet balance
                Info['account']['margin_balance'],  # Margin balance
                Info['account']['margin_used'],  # Used margin
                Info['account']['margin_free'],  # Available margin
                Info['account']['profit'],  # Total profit
                str(Info['account']['profit_rate']) + "%",  # Profit rate
                round(Info['account']['unrealised_profit'], 2),  # Unrealized profit
                round(Info['count']['total'], 2),  # Total position
                Info['count']['leverage'],  # Used leverage
                str(Info['time']['loop_delay']) + "ms",  # Loop delay
            ],
        ],
    }
    
    # Trading pair information table
    table2 = {
        "type": "table",
        "title": "Trading Pair Information",
        "cols": [
            "Currency", "Direction", "Quantity", "Position Price", "Position Value",
            "Current Price", "Pending Buy Price", "Pending Sell Price", "Unrealized P&L"
        ],
        "rows": [],
    }
    
    # Traverse each trading pair, fill trading pair information
    for symbol in Info['trade_symbols']:
        table2['rows'].append([
            symbol,  # Currency
            "LONG" if Info['position'][symbol]['amount'] > 0 else "SHORT",  # Direction
            round(Info['position'][symbol]['amount'], Info['precision'][symbol]['amount_precision'] + 2),  # Quantity
            round(Info['position'][symbol]['hold_price'], Info['precision'][symbol]['price_precision']),  # Position price
            round(Info['position'][symbol]['value'], 2),  # Position value
            round(Info['ticker'][symbol]['last'], Info['precision'][symbol]['price_precision']),  # Current price
            Info['order'][symbol]['buy']['price'],  # Pending buy price
            Info['order'][symbol]['sell']['price'],  # Pending sell price
            round(Info['position'][symbol]['unrealised_profit'], 2),  # Unrealized P&L
        ])
    
    # Output status log
    LogStatus(
        f"Initialization time: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(Info['time']['start_time']))}\n",
        f"`{json.dumps(table1)}`\n" + f"`{json.dumps(table2)}`\n",
        f"Last execution time: {time.strftime('%Y-%m-%d %H:%M:%S', time.localtime())}\n"
    )
    
    # Update account information every 10 seconds
    if time.time() * 1000 - Info['time']['update_profit_time'] > 10 * 1000:
        UpdateAccount()  # Update account information
        LogProfit(round(Info['account']['profit'], 3), '&')  # Output profit log
        Info['time']['update_profit_time'] = time.time() * 1000  # Update profit time

Trading Logic

The core trading decision part. Based on market data, account information, and positions, combined with technical indicators or quantitative models, decide whether to buy, sell, add positions, or reduce positions. Trading logic varies according to different strategies, such as trend-following strategies, mean reversion strategies, or breakout strategies.

Purpose:

• Make intelligent trading decisions based on market data and account status
• Handle various market situations, ensuring strategy operates reasonably in different market environments

For convenience in demonstrating framework applicability, the trading strategy in this article uses simple logic. Each order amount is fixed at 50 USDT, and when executing trades, corresponding buy or sell quantities are calculated based on current market bid/ask prices. This strategy’s execution rules are very basic, aimed at demonstrating how to apply the multi-currency contract trading strategy framework to live trading environments. To ensure strategy simplicity and operability, we set a stop condition: when a trading pair’s total position value reaches 2000 USDT, stop placing further orders. Through this approach, we can conveniently demonstrate the basic structure of the strategy and how the framework interacts with exchange market data.

def MakeOrder():
    # Traverse all trading pairs
    for symbol in Info['trade_symbols']:
        # Get bid price (pending buy price)
        buy_price = Info['ticker'][symbol]['bid']
        # Calculate buy quantity, based on buy amount divided by buy price
        buy_amount = 50 / buy_price
        
        # Get ask price (pending sell price)
        sell_price = Info['ticker'][symbol]['ask']
        # Calculate sell quantity, based on sell amount divided by sell price
        sell_amount = 50 / sell_price
        
        # If current position total value is less than 2000, execute buy operation
        if Info['position'][symbol]['value'] < 2000:
            Log('Entering trade')
            Log('Set price:', Info['ticker'][symbol]['bid'])
            Trade(symbol, "buy", buy_price, buy_amount, symbol)  # Execute buy operation
            #if Info['position'][symbol]['value'] < 3000:
            #    Trade(symbol, "sell", sell_price, sell_amount, symbol)  # Execute sell operation

Main Loop

The main loop is the core part of the strategy framework, responsible for ensuring the strategy runs continuously and stably in the market. It will periodically execute various operations according to set time intervals, such as obtaining latest market data, updating position information, executing trading decisions, etc. Through the main loop, the strategy can respond to market changes in real-time and ensure each execution is based on the latest market data. Usually, the main loop triggers once every new time period (such as every minute, every hour, or every new candlestick generation).

Purpose:

• Ensure strategy runs continuously and stably, obtaining market data in real-time and making trading decisions
• Ensure real-time accuracy of strategy execution in each loop

def OnTick():
    try:
        # Update market information
        UpdateTicker()
        # Update position information
        UpdatePosition()
        # Execute order operations
        MakeOrder()
        # Update status information
        UpdateStatus()
    except Exception as error:
        # Record errors occurring in the loop
        Log("Loop error: " + str(error))

def main():
    LogReset(0)
    apiBase = "https://testnet.binancefuture.com"  # Binance Futures simulation exchange
    exchange.SetBase(apiBase)  # Set simulation exchange base
    
    # Initialize information
    exchange.IO('dual', False)  # One-way position holding
    InitInfo()
    GetPrecision()
    
    while True:  # Infinite loop
        loop_start_time = time.time() * 1000  # Get current time (milliseconds)
        
        # Check if set interval time has passed since last loop
        if time.time() * 1000 - Info['time']['last_loop_time'] > Info['interval'] * 1000:
            OnTick()  # Call OnTick function
            
            # Update last loop time
            Info['time']['last_loop_time'] = time.time() * 1000
            
            # Calculate current loop delay time
            Info['time']['loop_delay'] = time.time() * 1000 - loop_start_time
        
        # Pause 5 milliseconds to avoid excessive CPU resource consumption
        Sleep(5000)

Code Explanation

1、OnTick Function: This function is the core task executed in each main loop iteration. It’s responsible for updating market data, position information, executing trading operations, and updating strategy status. All trading and information update operations are completed in this function. If errors occur during execution, they will be caught and logged. 2、Main Function: The main function initializes relevant information, sets the exchange base, and starts an infinite loop. In each loop iteration, the program checks whether the set time interval has passed. If so, the OnTick function is called to execute the strategy. If the time interval hasn’t passed, the program waits and continues looping, ensuring the strategy executes according to the predetermined time interval. 3、Delay Control: After each loop iteration ends, the program pauses for 5 milliseconds to reduce CPU load. This prevents high-frequency loops from causing excessive computational resource consumption.

Execution Logic

• Initialization Phase: First set up API and other necessary parameters, then initialize information and obtain currency precision.
• Main Loop Phase: Enter an infinite loop where each iteration checks the time interval to ensure strategy execution follows the predetermined schedule.
• Execution Phase: When the time interval condition is met, the OnTick function is called to execute all strategy operations, including market information updates, trade execution, etc.

This main loop design ensures the strategy runs continuously under real-time market conditions and makes timely trading decisions, thereby improving the strategy’s stability and accuracy.

Summary

This strategy framework’s design focuses on modularity and extensibility. Each step has clear responsibilities, ensuring strategy robustness while facilitating future expansion and optimization. By properly utilizing exchange API functions, strategies can simplify execution and improve consistency between backtesting and live trading. It should be noted that this demonstration framework uses Binance simulation exchange as an example, aimed at providing a basic strategy development and execution framework. In actual operations, it needs to be optimized in combination with specific live strategy logic, such as dynamically adjusting parameters based on market conditions, optimizing risk control mechanisms, adding exception handling, etc. Additionally, since different exchanges have differences in API interfaces, trading rules, precision settings, fee structures, etc., in actual applications, detail optimization and adaptation according to target exchange requirements are needed to ensure strategy stability and compatibility. It’s recommended to thoroughly test and verify strategy reliability and performance before live deployment.

Appendix: Python Multi-Currency Framework Address