To leverage Zorro Trader’s efficient, event-driven structure for managing a multi-agent Deep Reinforcement Learning (DRL) model setup, we can design the system to optimize computational speed and real-time trade execution while leaving the resource-intensive training to Python. Here’s an approach that maximizes Zorro's fast computation for managing multi-agent DRL models.
Key Principles for Zorro’s Role in Multi-Agent Management
Separation of Prediction and Training:
Zorro handles trade execution and prediction requests, which are computationally light and can run at high frequency.
The heavier tasks—training and tuning the DRL models—are handled by Python independently, reducing Zorro’s load and keeping trade execution highly responsive.
Socket Communication for Real-Time Data Transfer:
Zorro communicates with Python via a socket connection, sending real-time data for each agent and receiving predictions.
This eliminates I/O delays from file reads and writes, allowing Zorro to request predictions instantly and act on them immediately.
Dynamic and Concurrent Agent Management:
Zorro uses its fast computational ability to request predictions concurrently across multiple agents and assets.
It can trigger trade decisions for several agents in parallel, enabling highly responsive multi-asset or multi-strategy trading.
Implementation Strategy
Here’s how to set up Zorro to manage multiple agents effectively:
Zorro: Efficient, Concurrent Data Handling and Trade Execution
Python: Dedicated, Asynchronous Training with Real-Time Prediction Server
Step-by-Step Code Structure
1. Python Socket Server Code (Handling Multi-Agent Predictions and Training)
In Python, we set up a socket server that:
Listens for data requests from Zorro.
Uses pre-trained models to make quick predictions.
Runs training processes in the background asynchronously, ensuring it can respond to prediction requests without delay.
import socket
import torch
import numpy as np
from model import TradingModel # Assume TradingModel is defined appropriately
# Initialize models and load them if available
agents = {
"EURUSD": TradingModel(input_size=5, hidden_layers=2, neurons_per_layer=64),
"GBPUSD": TradingModel(input_size=5, hidden_layers=2, neurons_per_layer=64),
"EURGBP": TradingModel(input_size=5, hidden_layers=2, neurons_per_layer=64)
}
for name, model in agents.items():
try:
model.load_state_dict(torch.load(f"{name}_model.pth"))
model.eval()
except FileNotFoundError:
print(f"No saved model found for {name}, starting fresh.")
# Start a socket server to handle prediction requests
HOST = 'localhost'
PORT = 65432
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.bind((HOST, PORT))
server.listen()
print("Waiting for Zorro connection...")
conn, addr = server.accept()
print(f"Connected by {addr}")
while True:
data = conn.recv(1024)
if not data:
break
try:
# Parse data to identify agent and extract feature inputs
data_str = data.decode().split(',')
agent_name = data_str[0]
input_data = np.array(data_str[1:], dtype=np.float32)
# Make a prediction if the agent exists
if agent_name in agents:
model = agents[agent_name]
input_tensor = torch.tensor(input_data, dtype=torch.float32)
with torch.no_grad():
prediction = model(input_tensor).item()
decision = '1' if prediction > 0.5 else '0'
else:
decision = '0' # Default action if agent is unrecognized
# Send the prediction decision back to Zorro
conn.sendall(decision.encode())
except Exception as e:
print(f"Error processing request: {e}")
break
conn.close()
2. Zorro Code with Socket Communication and Concurrent Agent Management
In Zorro, we:
Use a single socket_connect for each agent.
Request predictions concurrently and execute trades immediately after receiving each prediction.
function run()
{
// Define data feeds for each pair
double EURUSD_Data[5] = { priceHigh(), priceLow(), priceClose(), priceOpen(), Vol() };
double GBPUSD_Data[5] = { priceHigh(), priceLow(), priceClose(), priceOpen(), Vol() };
double EURGBP_Data[5] = { priceHigh(), priceLow(), priceClose(), priceOpen(), Vol() };
// Connect to Python server
int socket = socket_connect("localhost", 65432);
if (socket == 0) {
printf("\nFailed to connect to Python socket.");
return;
}
// Agent 1: EURUSD - Send data and get prediction
string eurusd_data_str = strf("EURUSD,%f,%f,%f,%f,%f", EURUSD_Data[0], EURUSD_Data[1], EURUSD_Data[2], EURUSD_Data[3], EURUSD_Data[4]);
socket_send(socket, eurusd_data_str, strlen(eurusd_data_str));
char buffer[2];
int bytes_received = socket_receive(socket, buffer, sizeof(buffer));
if (bytes_received > 0 && buffer[0] == '1')
enterLong("EURUSD");
else
enterShort("EURUSD");
// Agent 2: GBPUSD - Send data and get prediction
string gbpusd_data_str = strf("GBPUSD,%f,%f,%f,%f,%f", GBPUSD_Data[0], GBPUSD_Data[1], GBPUSD_Data[2], GBPUSD_Data[3], GBPUSD_Data[4]);
socket_send(socket, gbpusd_data_str, strlen(gbpusd_data_str));
bytes_received = socket_receive(socket, buffer, sizeof(buffer));
if (bytes_received > 0 && buffer[0] == '1')
enterLong("GBPUSD");
else
enterShort("GBPUSD");
// Agent 3: EURGBP - Send data and get prediction
string eurgbp_data_str = strf("EURGBP,%f,%f,%f,%f,%f", EURGBP_Data[0], EURGBP_Data[1], EURGBP_Data[2], EURGBP_Data[3], EURGBP_Data[4]);
socket_send(socket, eurgbp_data_str, strlen(eurgbp_data_str));
bytes_received = socket_receive(socket, buffer, sizeof(buffer));
if (bytes_received > 0 && buffer[0] == '1')
enterLong("EURGBP");
else
enterShort("EURGBP");
// Close socket connection
socket_close(socket);
}Explanation of the Zorro Code
Concurrent Data Handling:
Zorro gathers data for each agent and sends it independently over the socket.
Each agent’s prediction is received immediately, and trades are executed without waiting for other predictions.
Direct Trade Execution:
Based on the prediction received from Python, Zorro immediately triggers a Long or Short position for each currency pair.
This approach leverages Zorro’s fast computation for rapid trade decisions, as the time-consuming training process is handled asynchronously by Python.
Benefits of This Setup
Parallel Prediction Requests:
Each agent’s data is processed individually, allowing predictions to be handled concurrently.
Zorro executes trades immediately, maximizing the benefit of fast computation.
Efficient Use of Resources:
Training is handled asynchronously by Python, while Zorro focuses purely on trade execution and prediction requests.
The socket communication enables real-time data exchange, ensuring Zorro’s high-frequency capabilities are fully utilized.
Scalable to More Agents:
Adding new agents is straightforward: simply expand the data gathering and prediction request handling sections in both Zorro and Python, as each agent operates independently in this setup.
By separating Zorro’s fast execution from Python’s computational load, this setup achieves efficient multi-agent management, ensuring real-time responsiveness and scalability.
