new fft algo with laplacian range of convergencemain

1 files changed, 20 insertions, 13 deletions

diff --git a/analysis.py b/analysis.py
index 912eb0a..ca409b0 100644
--- a/analysis.py
+++ b/analysis.py
@@ -1,6 +1,7 @@
 import json
 import datetime
 import os
+import pandas as pd
 
 def summarize_results(batch_name):
     """
@@ -34,17 +35,22 @@ def summarize_results(batch_name):
         if algo_key not in algos_to_results:
             algos_to_results[algo_key] = {
                 'percent_gains': [],
-                'stock_orders': []
+                'stock_orders': [],
+                'file_names': [],
+                'url_params': []
             }
 
         algos_to_results[algo_key]['percent_gains'].append(result['percent_gain'])
 
         stock_ticker = result_data['url_params']['ticker']
         algos_to_results[algo_key]['stock_orders'].append(stock_ticker)
-
-    # summarize the results for each algo
+        algos_to_results[algo_key]['file_names'].append(file_path.split('/')[-1])
+        algos_to_results[algo_key]['url_params'].append(result_data['url_params'])
+        
     for algo_name, result in algos_to_results.items():
         algo_params = algo_name.split('_')[1]  # Extract params from the key
+        algo_name_alone = algo_name.split('_')[0]  # Extract name from the key
+
         if not result['percent_gains'] and not result['percent_losses']:
             continue  # Skip if no gains or losses
 
@@ -60,7 +66,7 @@ def summarize_results(batch_name):
 
         # Append the summarized data
         results_summary.append([
-            algo_name, 
+            algo_name_alone, 
             algo_params,
             avg_percent_gain, 
             best_stock_order
@@ -69,9 +75,15 @@ def summarize_results(batch_name):
     # Sort the results by average percent gain in descending order
     results_summary.sort(key=lambda x: x[2], reverse=True)
 
-    print(algos_to_results)
-    
-    return results_summary 
+    print("Results Summary:")
+
+    # Return the summarized results as a DataFrame
+    return pd.DataFrame(results_summary, columns=[
+        'algo_name', 
+        'algo_params',
+        'avg_percent_gain', 
+        'best_stock_for_gain'
+    ]), algos_to_results
 
 def test():
     """
@@ -79,12 +91,7 @@ def test():
     """
     batch_name = 'test-1-ema'
     results = summarize_results(batch_name)
-    if results:
-        print("Results Summary:")
-        for row in results:
-            print(row)
-    else:
-        print("No results found.")    
+    print(results)
 
 test()