Deploying an Ensembled Forecasting Model (Linear SVR + SFN)

This post takes you under the hood of a custom-built forecasting engine—a hybrid model that blends the precision of a Linear Support Vector Regressor (SVR) with the pattern-recognition strength of a Simple Feedforward Network (SFN), both implemented in scikit-learn. Think of it like pairing a tactical marksman with a perceptive analyst: the SVR locks onto linear patterns with precision, while the SFN explores nonlinear terrains often invisible to traditional models. Together, they team up to forecast the next median candlestick price in the USD/JPY forex market. The inputs? A carefully engineered set of signals drawn from thousands of lines of historical trading data—each feature acting like a sensor feeding real-time battlefield intelligence into the model’s decision-making core.

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🔹 Step 1: Import Dependencies

Before we can fire up our forecasting engine, we need to gather our toolkit. These libraries function like a command squad:

• pandas for data logistics
• tensorflow (optional, for future deep learning extensions)
• matplotlib.pyplot for visual diagnostics
• re and sys for command-line interface parsing and automation

import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt
import re
import sys

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🔹 Step 2: Load and Preprocess Market Data

• Accept a CSV file via command line
• Extract date for versioning
• Reverse data chronologically
• Limit to most recent 10,000 rows
• Engineer features including Future_Median, percent changes, volume averages, and lagged medians

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🔹 Step 3: Feature Selection

We define our feature matrix X and target y:

features = ["Open", "High", "Low", "Close", "Price_Difference", "Open_Close_Change_Pct",
            "High_Low_Change_Pct", "Volume", "Volume_MA_20", "Volume_Change_Pct",
            "median_t-1", "median_t-2"]

Missing values are dropped to maintain integrity.

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🔹 Step 4: Scaling and Splitting

Data is scaled and partitioned into training and testing sets:

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

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🔹 Step 5: Train SVR and SFN with Grid Search

LinearSVR Parameters:

param_grid = {'C': [1, 10, 50], 'epsilon': [0.01, 0.1, 1]}

SFN Parameters (MLPRegressor):

param_grid_sfn = {
    'hidden_layer_sizes': [(50,), (100,), (50, 50), (100, 50)],
    'activation': ['relu', 'tanh'],
    'solver': ['adam'],
    'alpha': [0.0001, 0.001, 0.01],
    'learning_rate': ['constant', 'adaptive']
}

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🔹 Step 6: Ensemble the Models

Using VotingRegressor to blend predictions:

from sklearn.ensemble import VotingRegressor
ensemble_model = VotingRegressor(estimators=[('linear_svr', best_linear_svr_model),
                                             ('sfn', best_sfn_model)],
                                 weights=[0.7, 0.3])
ensemble_model.fit(X_train, y_train)

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🔹 Step 7: Evaluate Model Performance

We compute the following metrics:

• MSE, RMSE, MAE
• R² (Coefficient of Determination)
• Explained Variance

Visual plots compare actual vs. predicted medians for both training and testing sets.

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🔹 Step 8: Save Logs and Export to ONNX

• Save performance logs to JSON
• Include model version, feature list, and scaling parameters

Export model to ONNX:

from skl2onnx import convert_sklearn
from skl2onnx.common.data_types import FloatTensorType
onnx_model = convert_sklearn(ensemble_model, initial_types=[('input', FloatTensorType([None, X_test.shape[1]]))])

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🔹 Step 9: Send Model Log to Arasaka Neural Bastion (Optional API Endpoint)

If online, performance metadata can be sent to a remote API for archival and monitoring:

result = SendToArasaka(log_data)
print(result)

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Summary

This ensemble marks a step forward in tactical forecasting—balancing interpretability with flexibility. Exporting to ONNX means the model can now serve in non-Python environments (like trading terminals or embedded agents). Stay tuned for Part 2: Advanced Feature Engineering + Hyperparameter Tuning.