It’s possible to pass the live data feed from Zorro Trader directly to a Deep Reinforcement Learning (DRL) model without having to call a function from Zorro each time. This can be achieved by running the DRL model in a separate Python script or process that continuously ingests the live data from Zorro in real-time, allowing it to learn and adapt independently. Here’s how to set this up:
Steps to Integrate Zorro’s Live Feed Directly into a DRL Model
Set Up a Data Stream from Zorro to Python:
Configure Zorro to save live market data into a continuously updated file or to use an inter-process communication (IPC) method, such as sockets or shared memory, to feed data into the Python DRL model.
Run the DRL Model as an Independent Python Process:
Implement the DRL model in a standalone Python script that continuously reads data from the Zorro feed and performs training in real-time.
The DRL model can process the data asynchronously and make trading decisions based on updated observations.
Implement Communication Back to Zorro for Trade Execution:
When the DRL model decides on a trade action, it can communicate this action back to Zorro, which will execute the trade.
Implementation Options
Option 1: Data Streaming Using File I/O
Zorro Script for Writing Data Feed:
Modify the Zorro run function to write the latest market data to a CSV file (e.g., live_data.csv) at each bar interval.
function run()
{
vars InputData = series(priceClose()); // Example data series
// Save data to CSV file for the DRL model
file_write("Data/live_data.csv", InputData, 0);
// Execute trades based on external signals
var signal = file_read("Data/drl_signal.txt"); // Read DRL action signal
if (signal > 0.5)
enterLong();
else if (signal <= 0.5)
enterShort();
}Python DRL Script (Independent Process):
In a separate Python script, the DRL model continuously monitors live_data.csv for new data and updates its learning and action policy accordingly.
import torch
import numpy as np
import time
from model import TradingModel # Define your model in model.py
model = TradingModel(input_size=8, hidden_size=256)
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = torch.nn.BCELoss()
def train_model(input_data, target):
model.train()
input_tensor = torch.tensor(input_data, dtype=torch.float32)
target_tensor = torch.tensor([target], dtype=torch.float32)
optimizer.zero_grad()
prediction = model(input_tensor)
loss = criterion(prediction, target_tensor)
loss.backward()
optimizer.step()
return loss.item()
while True:
# Read the latest data from live_data.csv
try:
data = np.loadtxt("Data/live_data.csv", delimiter=',')
input_data = data[-1] # Get the most recent row of data
prediction = model(torch.tensor(input_data, dtype=torch.float32)).item()
# Save the action decision back to a file that Zorro reads
with open("Data/drl_signal.txt", "w") as f:
f.write("1" if prediction > 0.5 else "0") # Save the signal for Zorro
# Optionally train model with feedback (e.g., profit from the last trade)
# target = get_trade_feedback() # You may define a function to get trade results
# train_model(input_data, target)
except Exception as e:
print(f"Error reading data or training model: {e}")
time.sleep(1) # Polling interval, adjust based on data frequencyOption 2: Using Sockets for Real-Time Data Streaming
Set Up a Socket Server in the Python DRL Script:
Run a socket server in Python to continuously receive data from Zorro and send back trade actions.
import socket
import torch
import numpy as np
from model import TradingModel
model = TradingModel(input_size=8, hidden_size=256)
# Initialize socket server
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) # Adjust buffer size as needed
if not data:
break
# Convert data to numpy array
input_data = np.frombuffer(data, dtype=np.float32)
# Make a prediction
prediction = model(torch.tensor(input_data)).item()
# Send decision to Zorro
decision = b'1' if prediction > 0.5 else b'0'
conn.sendall(decision)Modify the Zorro Script to Send Data via Socket:
In the Zorro script, connect to the Python socket server to send live data and receive the model’s action signal.
function run()
{
vars InputData = series(priceClose());
double Data[NumSignals];
for(int i = 0; i < NumSignals; i++)
Data[i] = InputData[i];
// Send data to Python via socket
int socket = socket_connect("localhost", 65432);
if (socket != 0) {
socket_send(socket, Data, sizeof(Data)); // Send data
int action = socket_receive(socket); // Receive action decision
socket_close(socket);
// Act on received decision
if (action == '1')
enterLong();
else if (action == '0')
enterShort();
}
}By running the DRL model as an independent process, it can continuously learn and adapt from real-time data while only sending trade decisions back to Zorro for execution. This setup provides the flexibility to train the DRL model in live environments without interrupting Zorro’s main trading operations.
In the Zorro platform, socket-based networking functions like socket_connect, socket_send, and socket_receive are not built-in, as Zorro’s lite-C scripting language does not natively support socket programming. However, you can achieve socket communication in Zorro using DLLs (Dynamic Link Libraries) to bridge between Zorro and Python for real-time data feeds.
Steps to Set Up Socket Communication Between Zorro and Python Using a DLL
Create a C/C++ Socket DLL:
Write a C/C++ library that implements socket functions (connect, send, receive, close).
Compile the library into a DLL that Zorro can load and call directly from the lite-C script.
Use the DLL in Zorro:
Load the DLL in your Zorro script.
Use the DLL functions to send data to and receive data from a Python socket server.
Example of a Simple C/C++ Socket DLL
Here’s a minimal example to create a C++ DLL for socket communication. This example provides functions for connecting, sending, receiving, and closing a socket.
C++ Socket DLL Code
Save this file as SocketDLL.cpp, and then compile it into a DLL.
#include <winsock2.h>
#include <ws2tcpip.h>
#pragma comment(lib, "ws2_32.lib")
extern "C" {
SOCKET sock = INVALID_SOCKET;
__declspec(dllexport) int socket_connect(const char* ip, int port) {
WSADATA wsaData;
WSAStartup(MAKEWORD(2,2), &wsaData);
sock = socket(AF_INET, SOCK_STREAM, IPPROTO_TCP);
if (sock == INVALID_SOCKET) return -1;
sockaddr_in clientService;
clientService.sin_family = AF_INET;
clientService.sin_addr.s_addr = inet_addr(ip);
clientService.sin_port = htons(port);
if (connect(sock, (SOCKADDR*)&clientService, sizeof(clientService)) == SOCKET_ERROR) {
closesocket(sock);
WSACleanup();
return -1;
}
return 0;
}
__declspec(dllexport) int socket_send(const char* data, int length) {
if (sock == INVALID_SOCKET) return -1;
return send(sock, data, length, 0);
}
__declspec(dllexport) int socket_receive(char* buffer, int length) {
if (sock == INVALID_SOCKET) return -1;
return recv(sock, buffer, length, 0);
}
__declspec(dllexport) void socket_close() {
if (sock != INVALID_SOCKET) {
closesocket(sock);
WSACleanup();
}
}
}Explanation:
socket_connect: Connects to the given IP and port.
socket_send: Sends data through the connected socket.
socket_receive: Receives data from the socket into a buffer.
socket_close: Closes the socket and cleans up resources.
Compile the DLL
Use a C++ compiler, like Microsoft Visual Studio, to compile this code into a DLL (e.g., SocketDLL.dll). Ensure it’s a 64-bit DLL if you’re using the 64-bit version of Zorro.
Zorro Script to Use the DLL
Place the compiled DLL (SocketDLL.dll) in the same directory as your Zorro script.
Use the DLL functions in your Zorro script to connect, send, and receive data.
Zorro lite-C Script Example
function run()
{
// Load the socket DLL
int connect_result = call("SocketDLL.dll", "socket_connect", "localhost", 65432);
if (connect_result == 0) {
printf("\nConnected to socket.");
// Example data to send
double data[8] = { 1.2, 2.3, 3.4, 4.5, 5.6, 6.7, 7.8, 8.9 };
call("SocketDLL.dll", "socket_send", data, sizeof(data));
// Buffer for receiving data
char buffer[8];
int bytes_received = call("SocketDLL.dll", "socket_receive", buffer, sizeof(buffer));
if (bytes_received > 0) {
printf("\nReceived data: %s", buffer); // Example printout
}
// Close the socket connection
call("SocketDLL.dll", "socket_close");
} else {
printf("\nFailed to connect to socket.");
}
}Explanation of the Zorro Script
call() Function: This function is used to call functions from the DLL in Zorro.
call("SocketDLL.dll", "socket_connect", "localhost", 65432) connects to the Python server running on localhost at port 65432.
call("SocketDLL.dll", "socket_send", data, sizeof(data)) sends the data array to the Python server.
call("SocketDLL.dll", "socket_receive", buffer, sizeof(buffer)) receives data from the server into the buffer.
call("SocketDLL.dll", "socket_close") closes the socket connection.
Python Socket Server to Receive Data from Zorro
import socket
HOST = 'localhost'
PORT = 65432
server = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
server.bind((HOST, PORT))
server.listen()
print("Server listening...")
conn, addr = server.accept()
print(f"Connected by {addr}")
while True:
data = conn.recv(1024)
if not data:
break
print("Received data:", data)
# Send response
response = b"1" # For example, send a '1' signal for buy
conn.sendall(response)
conn.close()One important note for all traders and programmers, As previously noted, we all want final solutions without having any bugs, scripts that are unlogical, and hard-to-understand code, but the whole point of these examples, it to learn to use ZorroGPT and find faster solutions for your imaginations.
