GIVEN this spec and roadmap and code of next stage can you summerize the code, eval it, critic it, and break down each strategy and functionality in a table with your insights? i need to decide if i hire this person or not, here is it spec and his work:
Roadmap and Specification Document for the Next 5 Versions

## Introduction

This document outlines the roadmap and detailed specifications for the next five versions of the CryptoTrader agent. The goal is to enhance the agent's profitability, reduce risks, and align it with industry standards. The improvements will focus on integrating machine learning, optimizing for high-frequency trading, ensuring regulatory compliance, enhancing scalability, and incorporating advanced trading strategies.

---

## Version Overview

- Version 6: Integration of Machine Learning Models
- Version 7: High-Frequency Trading Optimization
- Version 8: Regulatory Compliance Module
- Version 9: Scalability Enhancements
- Version 10: Advanced Trading Strategies and Diversification

---

## Version 6: Integration of Machine Learning Models

### Objectives

- Implement predictive analytics to enhance market analysis.
- Improve the agent's ability to anticipate market movements.
- Utilize historical and real-time data for training models.

### Features

1. Data Collection and Preprocessing
- Collect extensive historical data for all traded assets.
- Clean and preprocess data for machine learning models.
- Implement data storage solutions (e.g., databases) for efficient retrieval.

2. Machine Learning Model Development
- Supervised Learning Models: Use regression models (e.g., Linear Regression, Random Forest, XGBoost) for price prediction.
- Classification Models: Predict market direction (uptrend or downtrend) using classifiers (e.g., SVM, Logistic Regression).
- Neural Networks: Implement deep learning models (e.g., LSTM networks) for time-series forecasting.

3. Model Training and Validation
- Split data into training, validation, and test sets.
- Use cross-validation techniques to prevent overfitting.
- Evaluate models using metrics like MAE, RMSE, and accuracy.

4. Real-Time Predictions
- Integrate trained models into the agent for real-time predictions.
- Continuously retrain models with new data to keep them updated.

5. Decision-Making Integration
- Use model outputs to inform trading decisions.
- Develop thresholds and confidence levels for executing trades based on predictions.

### Implementation Plan

- Week 1-2: Set up data infrastructure and collect historical data.
- Week 3-4: Develop and train initial machine learning models.
- Week 5: Integrate models into the agent and test in a simulated environment.
- Week 6: Deploy the updated agent and monitor performance.

### Requirements

- Libraries: scikit-learn, tensorflow or pytorch, pandas, numpy.
- Additional Data Sources: Access to comprehensive historical market data.

---

## Version 7: High-Frequency Trading Optimization

### Objectives

- Reduce latency in data retrieval and trade execution.
- Increase the rate of trading to capitalize on fleeting opportunities.
- Optimize network and system performance.

### Features

1. Infrastructure Enhancement
- Move to cloud services with lower latency (e.g., AWS EC2 instances close to exchange servers).
- Utilize virtual private servers (VPS) optimized for trading.

2. WebSocket Integration
- Replace REST API calls with WebSocket connections for real-time data streaming.
- Implement asynchronous event-driven architecture.

3. Algorithm Optimization
- Refactor code for performance, minimizing computational overhead.
- Use more efficient data structures and algorithms.

4. Parallel Processing
- Leverage multi-threading or multi-processing to handle concurrent tasks.
- Distribute tasks among available CPU cores.

5. Latency Monitoring
- Implement tools to monitor and log latency at each stage.
- Set up alerts for latency spikes.

### Implementation Plan

- Week 1-2: Set up new infrastructure and test connectivity.
- Week 3: Implement WebSocket connections and refactor code.
- Week 4: Optimize algorithms and conduct performance testing.
- Week 5: Deploy and monitor the high-frequency trading agent.

### Requirements

- Access to low-latency servers.
- Knowledge of WebSocket protocols and asynchronous programming.

---

## Version 8: Regulatory Compliance Module

### Objectives

- Ensure all trading activities comply with local and international regulations.
- Integrate features to handle KYC/AML requirements.
- Implement geo-fencing to respect jurisdictional restrictions.

### Features

1. Compliance Framework
- Develop a compliance checklist based on relevant regulations (e.g., GDPR, FATF guidelines).
- Implement procedures to verify compliance at each step.

2. KYC/AML Integration
- Interface with third-party services for identity verification.
- Monitor transactions for suspicious activity and flag them for review.

3. Geo-Restriction Handling
- Detect user location and restrict access to certain exchanges or assets if prohibited.
- Update exchange and asset lists based on regulatory changes.

4. Audit Logging
- Maintain detailed logs of all transactions and operations.
- Ensure logs meet standards for regulatory audits.

5. User Data Protection
- Implement strong encryption for storing sensitive information.
- Ensure data handling complies with privacy laws.

### Implementation Plan

- Week 1: Research and compile regulatory requirements.
- Week 2: Develop the compliance framework and audit logging.
- Week 3-4: Integrate KYC/AML features and geo-restriction handling.
- Week 5: Test the compliance module and deploy.

### Requirements

- Legal consultation for compliance requirements.
- Services for KYC/AML verification.

---

## Version 9: Scalability Enhancements

### Objectives

- Improve the agent's ability to handle increased load and higher volumes.
- Optimize resource utilization and performance.
- Ensure the system remains stable under stress.

### Features

1. Modular Architecture
- Refactor codebase into modular components.
- Implement microservices architecture where appropriate.

2. Load Balancing
- Utilize load balancers to distribute workloads evenly.
- Implement autoscaling policies based on demand.

3. Database Optimization
- Move to scalable databases (e.g., NoSQL databases like MongoDB).
- Optimize queries and data access patterns.

4. Asynchronous Task Queues
- Use message brokers (e.g., RabbitMQ, Kafka) for task distribution.
- Ensure non-blocking operations throughout the system.

5. Stress Testing
- Conduct load testing to identify bottlenecks.
- Use tools like Apache JMeter or Locust.

### Implementation Plan

- Week 1-2: Refactor code into modular components.
- Week 3: Implement load balancing and database optimization.
- Week 4: Set up asynchronous task queues.
- Week 5: Perform stress testing and optimize accordingly.

### Requirements

- Cloud infrastructure that supports scaling (e.g., AWS, GCP).
- Expertise in distributed systems.

---

## Version 10: Advanced Trading Strategies and Diversification

### Objectives

- Incorporate sophisticated trading strategies to enhance profitability.
- Diversify assets and markets to spread risk.
- Utilize options, futures, and other derivatives.

### Features

1. Algorithmic Trading Strategies
- Implement algorithms like mean reversion, momentum trading, and scalping.
- Use statistical arbitrage methods.

2. Options and Futures Trading
- Integrate with exchanges that offer derivatives.
- Develop strategies for hedging and speculation using options and futures.

3. Cross-Market Arbitrage
- Identify opportunities across different markets (e.g., crypto vs. forex).
- Expand asset classes to include stocks, commodities, etc.

4. Sentiment Analysis Enhancement
- Utilize natural language processing (NLP) to analyze news articles, social media, and forums.
- Use sentiment scores to predict market movements.

5. Risk-adjusted Performance Metrics
- Track metrics like Sharpe Ratio, Sortino Ratio, and Maximum Drawdown.
- Adjust strategies based on performance analysis.

### Implementation Plan

- Week 1-2: Research and select advanced trading strategies.
- Week 3-4: Develop and integrate selected strategies.
- Week 5: Implement sentiment analysis tools and performance metrics.
- Week 6: Test the new strategies in a simulated environment before deployment.

### Requirements

- Access to additional data sources (e.g., news feeds, social media APIs).
- Advanced statistical and mathematical knowledge.

---

## Summary Roadmap

| Version | Duration (Weeks) | Key Objectives |
|---------|------------------|-------------------------------------------|
| 6 | 6 | Machine Learning Integration |
| 7 | 5 | High-Frequency Trading Optimization |
| 8 | 5 | Regulatory Compliance Module |
| 9 | 5 | Scalability Enhancements |
| 10 | 6 | Advanced Strategies and Diversification |

---

## Considerations for Implementation by a New Agent

Given that a less experienced agent will continue this work, the following considerations are important:

- Clear Documentation: Provide thorough documentation for each version to ensure the agent understands the purpose and methods.
- Incremental Development: Break down tasks into manageable chunks with clear goals.
- Testing at Each Stage: Implement unit tests and integration tests to validate functionality.
- Code Reviews: Encourage peer reviews to catch errors and share knowledge.
- Learning Resources: Provide resources or references for unfamiliar concepts (e.g., machine learning tutorials).

---

## Conclusion

This roadmap provides a structured plan for enhancing the CryptoTrader agent over the next five versions. Each version builds upon the previous, aiming to increase profitability, reduce risk, and create a robust, industry-standard trading system. With careful planning and execution, even a less experienced agent can successfully continue this work by following the detailed specifications and utilizing the provided resources.

lets start with the next key item on your road map and dev it only code zero text output


import asyncio
import aiohttp
import time
import json
import hmac
import hashlib
import os
import ssl
from urllib.parse import urlencode
from datetime import datetime
import logging
import sys
from web3 import Web3
from web3.middleware import geth_poa_middleware
import pandas as pd
import numpy as np
import talib
from sklearn.preprocessing import MinMaxScaler
from sklearn.model_selection import train_test_split
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, LSTM

# Configuration Settings
API_KEYS = {
    'binance': {
        'api_key': os.getenv('BINANCE_API_KEY'),
        'api_secret': os.getenv('BINANCE_API_SECRET')
    }
}

EXCHANGES = ['binance']
DEXES = ['uniswap']
BASE_CURRENCY = 'ETH'
INITIAL_CAPITAL = 0.1
TRADE_INTERVAL = 60
ARBITRAGE_THRESHOLD = 0.5
FEE_PERCENTAGES = {
    'LINK': 0.001,
    'ADA': 0.001,
    'DOT': 0.001,
    'LTC': 0.001,
    'XRP': 0.001
}
DEX_FEE = 0.003
SYMBOLS = ['LINKETH', 'ADAETH', 'DOTETH', 'LTCETH', 'XRPETH']
TOKENS = {
    'LINK': '0x514910771AF9Ca656af840dff83E8264EcF986CA',
    'ADA': '0x3EE2200Efb3400fAbB9AacF31297cBdD1d435D47',
    'DOT': '0x7083609fCfD96c3245F5a44F5a43b21b64f181579',
    'LTC': '0x4338665CBB7B2485A8855A139b75D5e34AB0DB94',
    'XRP': '0x1D2F0dA169ceB9Fc7C8bE86B203D2983AcC0a828'
}
WETH_ADDRESS = '0xC02aaA39b223FE8D0A0e5C4F27eAD9083C756Cc2'
UNISWAP_ROUTER_ADDRESS = '0x7a250d5630B4cf539739df2C5dAcb4c659F2488D'

with open('uniswap_router_abi.json', 'r') as abi_file:
    UNISWAP_ROUTER_ABI = json.load(abi_file)

with open('erc20_abi.json', 'r') as abi_file:
    ERC20_ABI = json.load(abi_file)

MAX_DRAWDOWN = 0.2
TARGET_ALLOCATIONS = {
    'LINK': 0.2,
    'ADA': 0.2,
    'DOT': 0.2,
    'LTC': 0.2,
    'XRP': 0.1,
    'ETH': 0.1
}
WEB3_PROVIDER = 'https://mainnet.infura.io/v3/YOUR_INFURA_PROJECT_ID'
BASE_FEE = 0.001
CONCURRENT_AGENTS = 1

logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(message)s',
    handlers=[
        logging.FileHandler("trading_agent.log"),
        logging.StreamHandler(sys.stdout)
    ]
)

class CryptoTrader:
    def __init__(self):
        self.api_keys = API_KEYS
        self.exchanges = EXCHANGES
        self.dexes = DEXES
        self.base_currency = BASE_CURRENCY
        self.prices = {}
        self.prices['uniswap'] = {}
        self.order_books = {}
        self.portfolio = {self.base_currency: INITIAL_CAPITAL}
        self.transaction_history = []
        self.last_trade_time = None
        self.trade_interval = TRADE_INTERVAL
        self.arbitrage_threshold = ARBITRAGE_THRESHOLD
        self.max_trade_amount = self.portfolio[self.base_currency] * 0.1
        self.volatility_data = {}
        self.sentiment_data = {}
        self.price_history = {}
        self.ssl_context = ssl.create_default_context()
        self.web3 = Web3(Web3.HTTPProvider(WEB3_PROVIDER))
        self.web3.middleware_onion.inject(geth_poa_middleware, layer=0)
        self.account = self.web3.eth.account.from_key(os.getenv('PRIVATE_KEY'))
        self.uniswap_router = self.web3.eth.contract(
            address=Web3.toChecksumAddress(UNISWAP_ROUTER_ADDRESS),
            abi=UNISWAP_ROUTER_ABI
        )
        self.token_contracts = {}
        for token_symbol, token_address in TOKENS.items():
            self.token_contracts[token_symbol] = self.web3.eth.contract(
                address=Web3.toChecksumAddress(token_address),
                abi=ERC20_ABI
            )
        self.models = {}
        self.scalers = {}

    async def get_current_prices(self):
        async with aiohttp.ClientSession() as session:
            tasks = [self.fetch_prices(session, exchange) for exchange in self.exchanges]
            await asyncio.gather(*tasks)
            tasks = [self.fetch_dex_prices(token) for token in TOKENS.keys()]
            await asyncio.gather(*tasks)

    async def fetch_prices(self, session, exchange):
        try:
            if exchange == 'binance':
                url = 'https://api.binance.com/api/v3/ticker/price'
                async with session.get(url, timeout=5, ssl=self.ssl_context) as response:
                    data = await response.json()
                    self.prices['binance'] = {item['symbol']: float(item['price']) for item in data}
        except Exception as err:
            logging.error(f"Error fetching prices from {exchange}: {err}")

    async def fetch_dex_prices(self, token):
        try:
            token_address = Web3.toChecksumAddress(TOKENS[token])
            amount_in = self.web3.toWei(1, 'ether')
            amounts = self.uniswap_router.functions.getAmountsOut(
                amount_in,
                [token_address, self.web3.toChecksumAddress(WETH_ADDRESS)]
            ).call()
            price = self.web3.fromWei(amounts[1], 'ether')
            self.prices['uniswap'][token] = float(price)
        except Exception as err:
            logging.error(f"Error fetching DEX price for {token}: {err}")

    async def get_price_history(self):
        async with aiohttp.ClientSession() as session:
            tasks = [self.fetch_price_history(session, symbol) for symbol in SYMBOLS]
            await asyncio.gather(*tasks)

    async def fetch_price_history(self, session, symbol):
        try:
            url = 'https://api.binance.com/api/v3/klines'
            params = {'symbol': symbol, 'interval': '1m', 'limit': 1000}
            async with session.get(url, params=params, timeout=5, ssl=self.ssl_context) as response:
                data = await response.json()
                df = pd.DataFrame(data, columns=[
                    'OpenTime', 'Open', 'High', 'Low', 'Close', 'Volume',
                    'CloseTime', 'QuoteAssetVolume', 'NumberOfTrades',
                    'TakerBuyBaseAssetVolume', 'TakerBuyQuoteAssetVolume', 'Ignore'
                ])
                df['Close'] = df['Close'].astype(float)
                self.price_history[symbol] = df
        except Exception as err:
            logging.error(f"Error fetching price history for {symbol}: {err}")

    async def collect_historical_data(self):
        for symbol in SYMBOLS:
            if symbol not in self.price_history:
                await self.fetch_price_history(None, symbol)

    def train_machine_learning_model(self):
        for symbol in SYMBOLS:
            df = self.price_history.get(symbol)
            if df is not None:
                data = df[['Close']].values
                scaler = MinMaxScaler(feature_range=(0, 1))
                scaled_data = scaler.fit_transform(data)
                self.scalers[symbol] = scaler
                X, y = [], []
                window_size = 60
                for i in range(window_size, len(scaled_data)):
                    X.append(scaled_data[i-window_size:i, 0])
                    y.append(scaled_data[i, 0])
                X, y = np.array(X), np.array(y)
                X = np.reshape(X, (X.shape[0], X.shape[1], 1))
                model = Sequential()
                model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
                model.add(LSTM(units=50))
                model.add(Dense(1))
                model.compile(optimizer='adam', loss='mean_squared_error')
                model.fit(X, y, epochs=5, batch_size=32)
                self.models[symbol] = model

    def make_prediction(self, symbol):
        model = self.models.get(symbol)
        scaler = self.scalers.get(symbol)
        df = self.price_history.get(symbol)
        if model and scaler and df is not None:
            data = df[['Close']].values
            last_60 = data[-60:]
            scaled_last_60 = scaler.transform(last_60)
            X_test = []
            X_test.append(scaled_last_60[:, 0])
            X_test = np.array(X_test)
            X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
            predicted_price = model.predict(X_test)
            predicted_price = scaler.inverse_transform(predicted_price)
            return predicted_price[0][0]
        else:
            return None

    async def analyze_market(self):
        for symbol in SYMBOLS:
            predicted_price = self.make_prediction(symbol)
            current_price = self.prices['binance'].get(symbol)
            if predicted_price and current_price:
                if predicted_price > current_price * 1.01:
                    logging.info(f"Prediction indicates upward trend for {symbol}")
                elif predicted_price < current_price * 0.99:
                    logging.info(f"Prediction indicates downward trend for {symbol}")

    async def fetch_sentiment_data(self):
        async with aiohttp.ClientSession() as session:
            try:
                url = 'https://api.alternative.me/fng/'
                async with session.get(url, timeout=5, ssl=self.ssl_context) as response:
                    data = await response.json()
                    self.sentiment_data = data['data'][0]
            except Exception as err:
                logging.error(f"Error fetching sentiment data: {err}")

    async def identify_arbitrage_opportunities(self):
        opportunities = []
        for symbol in SYMBOLS:
            try:
                exchange_prices = {}
                for exchange in self.exchanges:
                    price = self.prices.get(exchange, {}).get(symbol)
                    if price:
                        exchange_prices[exchange] = price
                dex_symbol = symbol.replace(BASE_CURRENCY, '')
                dex_price = self.prices['uniswap'].get(dex_symbol)
                if dex_price:
                    exchange_prices['uniswap'] = dex_price
                exchanges = list(exchange_prices.keys())
                for i in range(len(exchanges)):
                    for j in range(len(exchanges)):
                        if i != j:
                            buy_exchange = exchanges[i]
                            sell_exchange = exchanges[j]
                            buy_price = exchange_prices[buy_exchange]
                            sell_price = exchange_prices[sell_exchange]
                            price_diff = sell_price - buy_price
                            percentage_diff = (price_diff / buy_price) * 100
                            net_profit = price_diff - self.calculate_fees(buy_price, sell_price, symbol)
                            if percentage_diff > self.arbitrage_threshold and net_profit > 0:
                                opportunity = {
                                    'symbol': symbol,
                                    'buy_exchange': buy_exchange,
                                    'sell_exchange': sell_exchange,
                                    'buy_price': buy_price,
                                    'sell_price': sell_price,
                                    'price_diff': price_diff,
                                    'percentage_diff': percentage_diff,
                                    'net_profit': net_profit
                                }
                                opportunities.append(opportunity)
            except Exception as err:
                logging.error(f"Error identifying arbitrage for {symbol}: {err}")
        return opportunities

    def calculate_fees(self, buy_price, sell_price, symbol):
        base_fee = BASE_FEE
        buy_fee = buy_price * base_fee
        sell_fee = sell_price * base_fee
        total_fees = buy_fee + sell_fee
        return total_fees

    async def execute_arbitrage_trade(self, opportunity):
        current_time = time.time()
        if self.last_trade_time and current_time - self.last_trade_time < self.trade_interval:
            return
        trade_amount = min(self.max_trade_amount, self.portfolio.get(self.base_currency, 0))
        if trade_amount <= 0:
            return
        if opportunity['buy_exchange'] == 'uniswap':
            await self.swap_tokens(opportunity['symbol'].replace(BASE_CURRENCY, ''), trade_amount, 'BUY')
        else:
            await self.place_order(opportunity['buy_exchange'], opportunity['symbol'], 'BUY', trade_amount)
        if opportunity['sell_exchange'] == 'uniswap':
            await self.swap_tokens(opportunity['symbol'].replace(BASE_CURRENCY, ''), trade_amount, 'SELL')
        else:
            await self.place_order(opportunity['sell_exchange'], opportunity['symbol'], 'SELL', trade_amount)
        self.last_trade_time = current_time

    async def swap_tokens(self, token_symbol, amount, side):
        try:
            token_address = Web3.toChecksumAddress(TOKENS[token_symbol])
            amount_in = self.web3.toWei(amount, 'ether')
            nonce = self.web3.eth.getTransactionCount(self.account.address)
            if side == 'BUY':
                path = [Web3.toChecksumAddress(WETH_ADDRESS), token_address]
                deadline = int(time.time()) + 60
                tx = self.uniswap_router.functions.swapExactETHForTokens(
                    0,
                    path,
                    self.account.address,
                    deadline
                ).buildTransaction({
                    'from': self.account.address,
                    'value': amount_in,
                    'gas': 200000,
                    'gasPrice': self.web3.toWei('50', 'gwei'),
                    'nonce': nonce
                })
            elif side == 'SELL':
                token_contract = self.token_contracts[token_symbol]
                allowance = token_contract.functions.allowance(self.account.address, UNISWAP_ROUTER_ADDRESS).call()
                if allowance < amount_in:
                    approve_tx = token_contract.functions.approve(UNISWAP_ROUTER_ADDRESS, amount_in).buildTransaction({
                        'from': self.account.address,
                        'gas': 60000,
                        'gasPrice': self.web3.toWei('50', 'gwei'),
                        'nonce': nonce
                    })
                    signed_approve_tx = self.account.sign_transaction(approve_tx)
                    tx_hash = self.web3.eth.sendRawTransaction(signed_approve_tx.rawTransaction)
                    self.web3.eth.waitForTransactionReceipt(tx_hash)
                    nonce += 1
                path = [token_address, Web3.toChecksumAddress(WETH_ADDRESS)]
                deadline = int(time.time()) + 60
                tx = self.uniswap_router.functions.swapExactTokensForETH(
                    amount_in,
                    0,
                    path,
                    self.account.address,
                    deadline
                ).buildTransaction({
                    'from': self.account.address,
                    'gas': 200000,
                    'gasPrice': self.web3.toWei('50', 'gwei'),
                    'nonce': nonce
                })
            signed_tx = self.account.sign_transaction(tx)
            tx_hash = self.web3.eth.sendRawTransaction(signed_tx.rawTransaction)
            self.web3.eth.waitForTransactionReceipt(tx_hash)
        except Exception as err:
            logging.error(f"Error swapping tokens on Uniswap: {err}")

    async def fetch_order_book(self, exchange, symbol):
        async with aiohttp.ClientSession() as session:
            try:
                if exchange == 'binance':
                    url = f'https://api.binance.com/api/v3/depth'
                    params = {'symbol': symbol, 'limit': 100}
                    async with session.get(url, params=params, timeout=5, ssl=self.ssl_context) as response:
                        data = await response.json()
                        return data
            except Exception as err:
                logging.error(f"Error fetching order book from {exchange}: {err}")
                return None

    def adjust_trade_amount(self, buy_order_book, sell_order_book, desired_amount):
        buy_liquidity = sum(float(order[1]) for order in buy_order_book.get('asks', []))
        sell_liquidity = sum(float(order[1]) for order in sell_order_book.get('bids', []))
        max_trade_amount = min(buy_liquidity, sell_liquidity, desired_amount)
        return max_trade_amount

    async def place_order(self, exchange, symbol, side, amount):
        try:
            if exchange == 'binance':
                await self.place_binance_order(symbol, side, amount)
        except Exception as err:
            logging.error(f"Error placing order on {exchange}: {err}")
            raise

    async def place_binance_order(self, symbol, side, amount):
        api_key = self.api_keys['binance']['api_key']
        api_secret = self.api_keys['binance']['api_secret']
        timestamp = int(time.time() * 1000)
        params = {
            'symbol': symbol,
            'side': side,
            'type': 'MARKET',
            'quantity': amount,
            'timestamp': timestamp
        }
        query_string = urlencode(params)
        signature = hmac.new(api_secret.encode(), query_string.encode(), hashlib.sha256).hexdigest()
        params['signature'] = signature
        headers = {'X-MBX-APIKEY': api_key}
        url = 'https://api.binance.com/api/v3/order'
        async with aiohttp.ClientSession() as session:
            async with session.post(url, params=params, headers=headers, timeout=5, ssl=self.ssl_context) as response:
                data = await response.json()
                logging.info(f"Order placed on Binance: {data}")

    def get_portfolio_value(self):
        total = 0
        for asset, amount in self.portfolio.items():
            if asset == self.base_currency:
                price = 1
            else:
                price = self.prices['binance'].get(asset + self.base_currency, 0)
                if price == 0:
                    price = self.prices['uniswap'].get(asset, 0)
            total += amount * price
        return total

    async def manage_risk(self):
        total_value = self.get_portfolio_value()
        max_drawdown = MAX_DRAWDOWN
        peak_value = self.portfolio.get('peak_value', total_value)
        if peak_value - total_value > max_drawdown * peak_value:
            logging.info("Max drawdown exceeded, executing risk management protocols")
            await self.rebalance_portfolio(forced=True)
        self.portfolio['peak_value'] = max(peak_value, total_value)

    async def rebalance_portfolio(self, forced=False):
        targets = TARGET_ALLOCATIONS
        total_value = self.get_portfolio_value()
        for asset, target_pct in targets.items():
            current_value = self.get_asset_value(asset)
            target_value = total_value * target_pct
            difference_ratio = (current_value - target_value) / total_value
            if abs(difference_ratio) > 0.05 or forced:
                amount_difference = (current_value - target_value)
                if amount_difference > 0:
                    amount_to_sell = amount_difference / self.get_asset_price(asset)
                    if amount_to_sell > 0:
                        await self.place_order('binance', asset + self.base_currency, 'SELL', amount_to_sell)
                else:
                    amount_to_buy = -amount_difference / self.get_asset_price(asset)
                    if amount_to_buy > 0 and self.portfolio.get(self.base_currency, 0) >= amount_to_buy:
                        await self.place_order('binance', asset + self.base_currency, 'BUY', amount_to_buy)

    def get_asset_price(self, asset):
        if asset == self.base_currency:
            return 1
        price = self.prices['binance'].get(asset + self.base_currency, 0)
        if price == 0:
            price = self.prices['uniswap'].get(asset, 0)
        return price

    def get_asset_value(self, asset):
        amount = self.portfolio.get(asset, 0)
        price = self.get_asset_price(asset)
        return amount * price

    async def update_strategy(self):
        market_volatility = self.calculate_market_volatility()
        if market_volatility > 2.0:
            self.arbitrage_threshold = 0.7
        else:
            self.arbitrage_threshold = 0.5

    def calculate_market_volatility(self):
        volatilities = []
        for symbol in SYMBOLS:
            df = self.price_history.get(symbol)
            if df is not None:
                df['Returns'] = df['Close'].astype(float).pct_change()
                volatility = df['Returns'].std() * np.sqrt(1440)
                volatilities.append(volatility)
        return np.mean(volatilities) if volatilities else 0

    async def run(self):
        await self.get_current_prices()
        await self.collect_historical_data()
        self.train_machine_learning_model()
        await self.fetch_sentiment_data()
        await self.analyze_market()
        opportunities = await self.identify_arbitrage_opportunities()
        for opportunity in opportunities:
            await self.execute_arbitrage_trade(opportunity)
        await self.manage_risk()
        await self.rebalance_portfolio()
        await self.update_strategy()

async def main():
    traders = [CryptoTrader() for _ in range(CONCURRENT_AGENTS)]
    await asyncio.gather(*(trader.run() for trader in traders))

if __name__ == "__main__":
    asyncio.run(main())