make an interactive presentation for the pasted analyze, make it visually appealing, use CSS, JS and HTML in a single HTML file.

```
Okay, let's analyze the optimization results across all the provided CSV files for March 2025 (from 20250303 to 20250331, excluding weekends if they weren't provided, but based on the dates given, it looks like a continuous block).

**Methodology:**

1.  **Load Data:** Read all CSV files into a single data structure (like a pandas DataFrame).
2.  **Identify Unique Parameter Sets:** Group the data by the input parameter columns: `Signal_AMA1_ER_Period`, `Min_Trend_AMA_Diff`, `Use_HTF_Trend_Filter`, `Low_RSI_Lots`, `Use_Equity_Guard`.
3.  **Calculate Monthly Gross Profit:** For each unique parameter set, sum the `Profit` column across all the days it appeared in the optimization files.
4.  **Rank:** Sort the parameter sets in descending order based on their calculated total monthly profit.
5.  **Analyze Top Performers:** Examine the parameters of the highest-ranking sets to find common characteristics.

**Analysis:**

Let's process the data according to the methodology.

*   **Days Analyzed:** Based on the filenames provided (20250303 to 20250331), we have data for 21 trading days in March 2025.
*   **Parameter Grouping:** Many passes within a single day's file share the same `Min_Trend_AMA_Diff`, `Use_HTF_Trend_Filter`, `Low_RSI_Lots`, and `Use_Equity_Guard`. The `Signal_AMA1_ER_Period` (values 1, 3, 5, 7, 9) often produces *identical* results for these fixed combinations, suggesting this parameter might be less influential *when the other parameters dictate the trading logic more strongly* (e.g., when `Min_Trend_AMA_Diff > 0` or `Use_HTF_Trend_Filter = true`).

**Calculating Monthly Gross Profit for Each Parameter Set:**

We need to aggregate the daily profits for each unique combination of (`Min_Trend_AMA_Diff`, `Use_HTF_Trend_Filter`, `Low_RSI_Lots`, `Use_Equity_Guard`), acknowledging that `Signal_AMA1_ER_Period` often leads to redundant results within these groups.

**(Simulated Aggregation - Key Results Extraction)**

By examining the files, we can see recurring high-profit days ($200+) for certain configurations and loss days (-$100 or smaller losses) for others. The goal is to find the combination with the highest *sum* of these daily profits over the 21 days.

Let's focus on the consistently profitable configurations observed across multiple files:

*   **Set A:** `Min_Trend_AMA_Diff = 0.00025`, `Use_HTF_Trend_Filter = false`, `Low_RSI_Lots = 0.03`, `Use_Equity_Guard = false/true` (often identical results for both guards).
    *   Examples: `20250303` (Passes 5-9): ~$200 profit. `20250304` (Passes 5-9): ~$200 profit. `20250310` (Passes 5-9): ~$200 profit. `20250314` (Passes 5-9): ~$200 profit. `20250318` (Passes 5-9): ~$100 profit (Equity Guard likely kicked in or near miss). `20250319` (Passes 5-9): ~$200 profit. `20250320` (Passes 5-9): ~$200 profit. `20250321` (Passes 5-9): ~$200 profit. `20250325` (Passes 3, 8): ~$200 profit. `20250326` (Passes 5-9): ~$200 profit. `20250327` (Passes 5-9): ~$200 profit. `20250328` (Passes 5-9): ~-$90 profit.
*   **Set B:** `Min_Trend_AMA_Diff = 0.00025`, `Use_HTF_Trend_Filter = true`, `Low_RSI_Lots = 0.06`, `Use_Equity_Guard = false/true`
    *   Examples: `20250304` (Passes 35-39): ~$200 profit. `20250311` (Passes 35-39): ~$200 profit. `20250313` (Passes 35-39): ~$200 profit. `20250317` (Passes 25-29): ~$200 profit. `20250318` (Passes 25-29): ~$200 profit. `20250324` (Passes 25-29): ~$200 profit. `20250325` (Passes 28): ~$200 profit. `20250326` (Passes 25-29): ~$200 profit. `20250327` (Passes 25-29): ~$200 profit.
*   **Set C:** `Min_Trend_AMA_Diff = 0.00025`, `Use_HTF_Trend_Filter = true`, `Low_RSI_Lots = 0.09`, `Use_Equity_Guard = false/true`
    *   Examples: `20250304` (Passes 55-59): ~$200 profit. `20250311` (Passes 55-59): ~$200 profit. `20250313` (Passes 55-59): ~$200 profit. `20250317` (Passes 45-49): ~$200 profit. `20250318` (Passes 45-49): ~$200 profit. `20250319` (Passes 45-49): ~$200 profit. `20250324` (Passes 45-49): ~$200 profit. `20250326` (Passes 45-49): ~$200 profit. `20250327` (Passes 45-49): ~$200 profit. `20250328` (Passes 45-49): ~$200 profit.
*   **Set D:** `Min_Trend_AMA_Diff = 0.00025`, `Use_HTF_Trend_Filter = true`, `Low_RSI_Lots = 0.12`, `Use_Equity_Guard = false/true`
    *   Examples: `20250304` (Passes 75-79): ~$200 profit. `20250307` (Passes 65-69): ~$200 profit. `20250311` (Passes 75-79): ~$200 profit. `20250313` (Passes 65-69): ~$200 profit. `20250317` (Passes 65-69): ~$200 profit. `20250318` (Passes 65-69): ~$200 profit. `20250319` (Passes 65-69): ~$200 profit. `20250320` (Passes 65-69): ~$200 profit. `20250321` (Passes 65-69): ~$200 profit. `20250325` (Passes 65-69): ~$200 profit. `20250326` (Passes 65-69): ~$200 profit. `20250327` (Passes 65-69): ~$200 profit. `20250328` (Passes 65-69): ~$200 profit.
*   **Set E:** `Min_Trend_AMA_Diff = 0.00025`, `Use_HTF_Trend_Filter = true`, `Low_RSI_Lots = 0.15`, `Use_Equity_Guard = false/true`
    *   Examples: `20250304` (Passes 95-99): ~$200 profit. `20250307` (Passes 85-89): ~$200 profit. `20250311` (Passes 95-99): ~$200 profit. `20250313` (Passes 85-89): ~$200 profit. `20250317` (Passes 85-89): ~$200 profit. `20250318` (Passes 85-89): ~$200 profit. `20250319` (Passes 85-89): ~$200 profit. `20250320` (Passes 85-89): ~$200 profit. `20250321` (Passes 85-89): ~$200 profit. `20250325` (Passes 85-89): ~$200 profit. `20250326` (Passes 85-89): ~$200 profit. `20250327` (Passes 85-89): ~$200 profit. `20250328` (Passes 85-89): ~$200 profit.

**(Manual/Estimated Ranking based on inspection)**

It appears sets using `Min_Trend_AMA_Diff = 0.00025` and `Use_HTF_Trend_Filter = true` consistently hit the $200 profit target on many days across different `Low_RSI_Lots` values (0.06, 0.09, 0.12, 0.15). These seem likely to have the highest cumulative profit.

Let's calculate more precisely for the top contenders (assuming $200 profit for Result >= 300 and -$100 for Result < 10, and actual profit otherwise):

*   **Set D (HTF=true, MinDiff=0.00025, Lots=0.12, Guard=true/false):**
    *   Consistently hits ~$200 profit on most days shown. Let's check days it *didn't*:
        *   `20250303`: Pass 75-79 -> -$89 profit
        *   `20250305`: Pass 75-79 -> 0 profit (no trades)
        *   `20250306`: Pass 75-79 -> $200 profit
        *   `20250310`: Pass 75-79 -> $200 profit
        *   `20250312`: Pass 75-79 -> $200 profit
        *   `20250320`: Pass 75-79 -> $200 profit
        *   `20250324`: Pass 75-79 -> $200 profit
    *   Estimate: 18 days * $200 - 1 day * $89 = $3600 - $89 = **$3511** (Approx)

*   **Set E (HTF=true, MinDiff=0.00025, Lots=0.15, Guard=true/false):**
    *   Consistently hits ~$200 profit on most days shown. Let's check days it *didn't*:
        *   `20250303`: Pass 95-99 -> -$83 profit
        *   `20250305`: Pass 95-99 -> 0 profit (no trades)
        *   `20250306`: Pass 95-99 -> -$91 profit
        *   `20250310`: Pass 95-99 -> $201 profit
        *   `20250312`: Pass 95-99 -> $202 profit
        *   `20250320`: Pass 95-99 -> $201 profit
        *   `20250324`: Pass 95-99 -> $200 profit
    *   Estimate: 18 days * $200 - 1 day * $83 - 1 day * $91 = $3600 - $174 = **$3426** (Approx)

*   **Set C (HTF=true, MinDiff=0.00025, Lots=0.09, Guard=true/false):**
    *   Consistently hits ~$200 profit on most days shown. Let's check days it *didn't*:
        *   `20250303`: Pass 55-59 -> -$91 profit
        *   `20250305`: Pass 55-59 -> 0 profit (no trades)
        *   `20250306`: Pass 55-59 -> $200 profit
        *   `20250310`: Pass 55-59 -> $200 profit
        *   `20250312`: Pass 55-59 -> $200 profit
        *   `20250320`: Pass 55-59 -> $200 profit
        *   `20250324`: Pass 55-59 -> $200 profit
    *   Estimate: 19 days * $200 - 1 day * $91 = $3800 - $91 = **$3709** (Approx)

*   **Set B (HTF=true, MinDiff=0.00025, Lots=0.06, Guard=true/false):**
    *   Consistently hits ~$200 profit on most days shown. Let's check days it *didn't*:
        *   `20250303`: Pass 35-39 -> $40 profit
        *   `20250305`: Pass 35-39 -> 0 profit (no trades)
        *   `20250306`: Pass 35-39 -> -$91 profit
        *   `20250310`: Pass 35-39 -> $201 profit
        *   `20250312`: Pass 35-39 -> $200 profit
        *   `20250320`: Pass 35-39 -> $200 profit
        *   `20250324`: Pass 35-39 -> $200 profit
    *   Estimate: 18 days * $200 + 1 day * $40 - 1 day * $91 = $3600 + $40 - $91 = **$3549** (Approx)

*   **Set A (HTF=false, MinDiff=0.00025, Lots=0.03, Guard=true/false):**
    *   Examples: `20250303`: $200. `20250304`: $200. `20250305`: -$96. `20250306`: -$93. `20250307`: -$92. `20250310`: $200. `20250311`: $200. `20250312`: -$92. `20250313`: $200. `20250314`: $200. `20250317`: $200. `20250318`: $100. `20250319`: $200. `20250320`: $200. `20250321`: $200. `20250324`: $201. `20250325`: $201. `20250326`: $200. `20250327`: $201. `20250328`: -$90. `20250331`: -$94.
    *   Estimate: (15 * $200) + $100 - (6 * ~$93) = $3000 + $100 - $558 = **$2542** (Approx)

**Top Ranked Sets (Estimated Monthly Profit):**

1.  **Set C (Lots=0.09, MinDiff=0.00025, HTF=true, Guard=true/false):** ~$3709
2.  **Set B (Lots=0.06, MinDiff=0.00025, HTF=true, Guard=true/false):** ~$3549
3.  **Set D (Lots=0.12, MinDiff=0.00025, HTF=true, Guard=true/false):** ~$3511
4.  **Set E (Lots=0.15, MinDiff=0.00025, HTF=true, Guard=true/false):** ~$3426
5.  **Set A (Lots=0.03, MinDiff=0.00025, HTF=false, Guard=true/false):** ~$2542

*(Note: The exact profit calculation depends on how intermediate results like Pass 115 in `20250304.csv` (-$0.96 profit) are handled, but the ranking should be robust as the $200/-$100 days dominate).*

**Common Factors Among Top Performers:**

Based on the estimated top 4 sets:

1.  **`Min_Trend_AMA_Diff`:** Consistently **0.00025**. Parameter sets where this was 0 performed very poorly overall for the month.
2.  **`Use_HTF_Trend_Filter`:** Consistently **`true`**. The best set identified without the HTF filter (Set A) performed significantly worse over the month than the top sets using the filter.
3.  **`Use_Equity_Guard`:** Appears to have **little impact** on the *overall monthly profit* for these top sets. Both `true` and `false` versions within the same parameter group often show identical or near-identical performance across the month. This suggests the guard rarely triggered in a way that significantly altered the outcome *for these specific profitable parameter sets* (they either hit $300 quickly or lost without reaching the $250 threshold often).
4.  **`Low_RSI_Lots`:** The top 4 sets span a range from **0.06 to 0.15**. While 0.09 came out slightly ahead in this estimation, 0.06, 0.12, and 0.15 were also very strong performers. The value 0.03 performed much worse when combined with `HTF=true` (see files like `20250303` Pass 15/16, `20250306` Pass 15-19, `20250317` Pass 15-19) or with `HTF=false` (Set A). This suggests that a lot size between 0.06 and 0.15 is preferable *when using the HTF filter and the specified Min_Trend_AMA_Diff*.
5.  **`Signal_AMA1_ER_Period`:** As noted, this parameter seems **largely irrelevant** for the top-performing sets, given that `Min_Trend_AMA_Diff` is 0.00025 and `Use_HTF_Trend_Filter` is `true`. The results are nearly always identical across the different values (1, 3, 5, 7, 9) within these groups.

**Conclusion:**

The analysis strongly indicates that the most profitable and consistent parameter combinations for March 2025 involved:

*   **`Min_Trend_AMA_Diff` = 0.00025**
*   **`Use_HTF_Trend_Filter` = `true`**
*   **`Low_RSI_Lots` between 0.06 and 0.15** (with 0.09 potentially having a slight edge, but all within this range performed well).

The `Use_Equity_Guard` setting and the `Signal_AMA1_ER_Period` value did not appear to be critical differentiating factors for *these* top-performing combinations during this specific month.
```