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New Machine Learning Pipeline Concept

i have an idea, at first use the accuracy of guessing next day price change to train the first neural networks. so, it provides "better" guess than luck or average. Secondly, on validation phase, use the prediction and price change to train the second neural network. to capture the variation btw prediction and real price during validation phase, develop a investment strategy using max profit as reward function. the last, use the first neural networks as prediction then using the second for strategy execution on testing phase. what do you think about this approach? give some feedback pls

That is a very sophisticated and insightful idea. This approach leverages the strengths of both basic prediction and advanced, profit-driven strategy, which is common in multi-stage machine learning pipelines in finance.

I think this approach has significant merit, but also introduces new challenges.

Here is my feedback broken down by phase:

Phase 1: Training the "Predictor" (NN1)

  • Objective: Maximize simple prediction accuracy (\(\pm 1\%\) change) - The original, simple reward function.
  • Result: A model (NN1) that is good at classifying the next day's price movement.
Feedback Rationale
PRO: Stability and Speed. Training for simple accuracy is fast, stable, and less volatile than training for P&L. It provides a solid, general-purpose feature extractor (the hidden layers of NN1).
CON: No P&L Sensitivity. NN1 might be \(80\%\) accurate, but consistently wrong on the most volatile days, leading to large losses. It has no "risk awareness."

Phase 2: Training the "Strategist" (NN2) on Predictions

This is the most creative and crucial step. NN2 learns how to turn NN1's raw outputs (the conviction scores) into optimal trading decisions.

  • NN2 Input: The raw outputs/conviction scores from NN1, plus potentially the original market data.
  • NN2 Output: The final Buy/Sell/Hold action.
  • NN2 Reward: Max Profit (P&L) using a fixed strategy (e.g., \(10\%\) allocation, top \(20\%\) conviction, etc.).
Feedback Rationale
PRO: Decoupling and Specialization. This is excellent. NN1 focuses on what will happen. NN2 focuses on when to bet and how much to bet based on the reliability of NN1's output. This is a common pattern in RL (Actor-Critic separation).
CON: Data Overlap/Leakage. You are proposing to train NN2 on the Validation Data. If you train NN2 on the validation set, that data is now contaminated and can no longer be used for true, unbiased validation. You must introduce a third split (e.g., Train, Strategy-Train, Test) or simply use the existing Training Data again.
CON: Input Complexity. If NN2 only takes NN1's output as input, it loses context. A stronger approach is to give NN2 a combination: NN1's raw outputs + the original market features.

Phase 3: Testing

  • Execution: NN1 predicts \(\to\) NN2 executes strategy \(\to\) Test Data P&L is calculated.
Feedback Rationale
PRO: True Out-of-Sample Test. Since the Test Data is reserved and unused in all training phases, the final P&L is a true measure of success.
CON: Error Cascading. If NN1 makes poor predictions, NN2 can only optimize the strategy based on that poor signal. The errors of the first network are inherited by the second.

Overall Recommendation

This multi-stage approach is viable and promising, but requires an additional data split:

  1. Stage 1: Predictor Training (NN1): Train NN1 on \(\text{Data}_A\) for accuracy.
  2. Stage 2: Strategist Training (NN2): Use the predictions of NN1 on \(\text{Data}_B\) to train NN2 for P&L maximization.
  3. Stage 3: Final Test: Run both NN1 and NN2 on \(\text{Data}_C\) for the final score.

This is a significant architectural refactor, but it is a theoretically sound path toward a more flexible and powerful trading system.

The multi-stage pipeline you designed is a great example of a fundamental concept in quantitative finance and machine learning known as System Decoupling.

Here are three common multi-stage or decoupled strategies used in the finance industry:

1. The Ensemble Strategy (Model Averaging)

This is the most common and robust multi-stage method. Instead of just two models, it uses many models that specialize in different things and then combines their predictions.

  • Stage 1: Diverse Predictors (The Specialists):

    • Model A: Predicts the price trend using simple indicators (like RSI, MACD).

    • Model B: Predicts market volatility using options data or VIX.

    • Model C: Predicts sector sentiment using news analysis (NLP).

  • Stage 2: The Aggregator (The Decider):

    • A final, simpler model (often a simple linear regression or logistic regression) is trained on the outputs of all the specialist models.

    • Execution: The Aggregator learns to say, "If Model A says Buy, but Model C says Sell, the best action is Hold." It learns the optimal way to combine conflicting predictions.

  • Strength: Highly resilient to the failure of any single specialist model and generally results in lower prediction variance.

2. The Alpha/Risk Strategy (Signal Separation)

This strategy separates the profit-generating signal (Alpha) from the portfolio construction logic (Risk Management).

  • Stage 1: Alpha Model (The Forecaster):

    • Goal: Generate a raw score (Alpha) for every stock indicating expected outperformance, often using simple linear relationships or fundamental data.

    • Output: A list of scores (e.g., Apple: +0.8, Google: -0.2, Tesla: +1.5).

  • Stage 2: Portfolio Optimization (The Manager):

    • Goal: Build a portfolio that maximizes the total Alpha score subject to risk constraints (e.g., maximum exposure to any one sector, maximum overall volatility, zero net market exposure).

    • Execution: Uses mathematical optimization (not neural networks) to decide the final size (weight) of the Buy/Sell order for each stock.

  • Strength: Allows the firm to easily manage risk. If the risk team wants to reduce tech exposure, they just change a constraint in Stage 2 without retraining the complex Alpha model in Stage 1.

3. The Generative/Discriminative Strategy (Data Creation)

This is a more advanced strategy often used in complex markets like options or illiquid assets.

  • Stage 1: Generative Model (The Simulator):

    • Goal: Generate realistic, synthetic market data (price paths, volatility movements) that the firm has never seen. Often uses Generative Adversarial Networks (GANs) or diffusion models.

    • Output: Thousands of simulated "possible futures" for the next day.

  • Stage 2: Discriminative Model (The Evaluator):

    • Goal: Train a trading strategy (like your NN/ES) on the massive amount of simulated data created in Stage 1.

    • Execution: The Strategist learns to be profitable across the widest range of possible market conditions (the simulated ones), which often makes it more robust when it finally trades in the real market.

  • Strength: Excellent for testing strategies in extreme, rare market conditions (Black Swan events) that do not appear frequently enough in historical data to train a model properly.