Iteration Logs

New Agent.get_reward(self, weights):
- Implements the Confidence-Based Selective Trading strategy.
- Calculates the conviction score (raw output of the chosen neuron) for every day.
- Calculates the P&L assuming a 10% fractional trade for every day.
- Selects the Top 20% of days based on conviction.
- The reward is the Final Portfolio Value after simulating only the top
```
20%20%
```
  trades with an initial capital of **
```
$100,000$100,000
```
  **. (As per your request).
Updated StrategyAnalyzer:
- The old run_analysis method is kept to show the accuracy/action count at checkpoints.
- A NEW method, run_pnl_analysis, is created to perform the Final P&L Simulation for Validation and Testing.
New P&L Simulation Logic:
- Initial Capital:
  
  $10,000$10,000
  
  (As requested for the final analysis).
- Allocation: Fixed 10% Fractional per active trade.
- Transaction Costs: Ignored (as requested for final analysis).
- Hold Action: Close all positions (i.e.,
```
100%100%
```
  cash and
```
0%0%
```
  return).
Final Execution Block:
- The if name == "main": block is updated to call the new P&L analysis after training.

Here is a breakdown of how the key components interact:

New Training Objective (Reward Function): The Agent.get_reward() method is updated to implement the Confidence-Based Selective Trading strategy.
- Goal: Maximize the final portfolio value after simulating trades only on the top 20% most-convicted days, starting with $100,000 capital.
- Effect: The Evolution Strategy (ES) trains the Neural Network weights to produce outputs (conviction scores) that lead to the most profitable trades when only the strongest signals are acted upon.
New Analysis Logic (StrategyAnalyzer.run_analysis): This method is updated to reflect the tracking requirements for your checkpoint summary.
- Goal: Track P&L and trade metrics at the end of every checkpoint on the Validation and Test sets.
- Mechanism: It simulates a simpler, stateless Fixed 10% Fractional Trading strategy (using the required **$10,000** initial capital and **no transaction costs**), and outputs the resulting metrics (Final P&L ($), Total Trades, etc.).

The code is now consistent:

Training is focused on maximizing P&L through highly selective, high-conviction trades ($100k start).
Analysis is focused on reporting the P&L and trade volume of a more standard, daily

10%10%

fractional trading strategy ($10k start).