diff --git a/.gitignore b/.gitignore
index 4c9164f..3b6a3e1 100644
--- a/.gitignore
+++ b/.gitignore
@@ -27,3 +27,9 @@ logs/
 
 # Results (optional - uncomment if you don't want to track results)
 # results/
+
+# Backups und generierte Outputs
+data/backups/
+data/*.backup*
+data/generated_tips/weekly_tips_*.csv
+data/performance_reports/
diff --git a/data/AlleEurojackpotzahlen.csv b/data/AlleEurojackpotzahlen.csv
index 66dc1e4..d1c4c3b 100644
--- a/data/AlleEurojackpotzahlen.csv
+++ b/data/AlleEurojackpotzahlen.csv
@@ -916,3 +916,38 @@ datum;Z1;Z2;Z3;Z4;Z5;SZ1;SZ2
 2026-01-06;21;23;30;33;38;8;12
 2026-01-09;1;17;19;25;41;6;12
 2026-01-13;2;16;27;33;47;6;12
+2026-01-16;8;16;37;39;48;5;11
+2026-01-20;16;26;32;37;45;2;3
+2026-01-23;18;36;39;45;50;6;9
+2026-01-27;13;18;19;29;32;8;9
+2026-01-30;8;13;15;17;37;3;7
+2026-02-03;3;20;27;37;44;1;2
+2026-02-06;8;14;38;41;48;1;11
+2026-02-10;12;19;34;39;47;4;5
+2026-02-13;1;21;44;45;46;2;7
+2026-02-17;8;23;39;40;44;6;7
+2026-02-20;11;17;23;36;40;5;6
+2026-02-24;4;5;26;38;48;2;9
+2026-02-27;7;17;19;28;47;2;7
+2026-03-03;1;9;14;35;49;2;10
+2026-03-06;8;17;26;31;47;1;6
+2026-03-10;2;3;17;18;28;4;10
+2026-03-13;7;23;37;44;47;2;6
+2026-03-17;12;13;16;17;37;4;11
+2026-03-20;2;17;21;25;30;2;6
+2026-03-24;9;15;23;43;48;3;5
+2026-03-27;21;23;25;38;40;7;11
+2026-03-31;5;15;18;20;35;7;8
+2026-04-03;9;10;18;22;37;1;11
+2026-04-07;2;4;16;23;27;5;8
+2026-04-10;1;6;11;18;48;10;12
+2026-04-14;13;22;32;46;47;6;7
+2026-04-17;16;31;35;43;44;2;9
+2026-04-21;31;32;36;39;47;7;8
+2026-04-24;6;21;29;39;44;1;5
+2026-04-28;19;20;41;43;46;5;7
+2026-05-01;10;11;13;16;27;5;7
+2026-05-05;1;30;33;34;43;5;10
+2026-05-08;3;17;18;31;41;6;12
+2026-05-12;7;15;19;28;35;3;11
+2026-05-15;1;32;33;36;37;7;12
diff --git a/data/eurojackpot_ml_models/deep_learning_euro/lstm_config_12.json b/data/eurojackpot_ml_models/deep_learning_euro/lstm_config_12.json
index 3e4b8d1..f8cbc26 100644
--- a/data/eurojackpot_ml_models/deep_learning_euro/lstm_config_12.json
+++ b/data/eurojackpot_ml_models/deep_learning_euro/lstm_config_12.json
@@ -1,9 +1,9 @@
 {
-  "trained_at": "2026-01-06T21:00:43.268063",
-  "num_samples": 895,
+  "trained_at": "2026-05-19T09:04:48.801117",
+  "num_samples": 932,
   "num_features": 20,
-  "final_loss": 0.3324819803237915,
-  "final_val_loss": 0.3310294995705287,
-  "best_val_loss": 0.3291587332884471,
-  "epochs_trained": 25
+  "final_loss": 0.33304641644159955,
+  "final_val_loss": 0.32588819166024524,
+  "best_val_loss": 0.32180650532245636,
+  "epochs_trained": 26
 }
\ No newline at end of file
diff --git a/data/eurojackpot_ml_models/deep_learning_euro/lstm_model_12.pth b/data/eurojackpot_ml_models/deep_learning_euro/lstm_model_12.pth
index 262bfb7..0677305 100644
Binary files a/data/eurojackpot_ml_models/deep_learning_euro/lstm_model_12.pth and b/data/eurojackpot_ml_models/deep_learning_euro/lstm_model_12.pth differ
diff --git a/data/eurojackpot_ml_models/deep_learning_main/lstm_config_50.json b/data/eurojackpot_ml_models/deep_learning_main/lstm_config_50.json
index c01db45..8158a5e 100644
--- a/data/eurojackpot_ml_models/deep_learning_main/lstm_config_50.json
+++ b/data/eurojackpot_ml_models/deep_learning_main/lstm_config_50.json
@@ -1,9 +1,9 @@
 {
-  "trained_at": "2026-01-06T21:00:33.679894",
-  "num_samples": 895,
+  "trained_at": "2026-05-19T09:04:39.307548",
+  "num_samples": 932,
   "num_features": 20,
-  "final_loss": 0.33361901148505835,
-  "final_val_loss": 0.33026839792728424,
-  "best_val_loss": 0.32894887030124664,
-  "epochs_trained": 21
+  "final_loss": 0.3328763085107009,
+  "final_val_loss": 0.32958758374055225,
+  "best_val_loss": 0.32844950755437213,
+  "epochs_trained": 22
 }
\ No newline at end of file
diff --git a/data/eurojackpot_ml_models/deep_learning_main/lstm_model_50.pth b/data/eurojackpot_ml_models/deep_learning_main/lstm_model_50.pth
index b9516e0..f84da6f 100644
Binary files a/data/eurojackpot_ml_models/deep_learning_main/lstm_model_50.pth and b/data/eurojackpot_ml_models/deep_learning_main/lstm_model_50.pth differ
diff --git a/data/eurojackpot_ml_models/learning_state.json b/data/eurojackpot_ml_models/learning_state.json
index 12f3d68..c4f2a96 100644
--- a/data/eurojackpot_ml_models/learning_state.json
+++ b/data/eurojackpot_ml_models/learning_state.json
@@ -1,78 +1,78 @@
 {
   "adjustments_main": {
-    "1": -0.014850499375000001,
-    "2": -0.014850499375000001,
-    "3": -0.014850499375000001,
-    "4": -0.014850499375000001,
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-    "7": -0.014850499375000001,
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-    "10": 0.014775998750000002,
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-    "12": -0.014850499375000001,
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-    "15": 0.014775998750000002,
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-    "18": -0.014850499375000001,
-    "19": -0.014850499375000001,
-    "20": -0.014850499375000001,
-    "21": 7.450062499999855e-05,
-    "22": -0.014850499375000001,
-    "23": 7.450062499999855e-05,
-    "24": -0.014850499375000001,
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-    "27": -0.014850499375000001,
-    "28": -0.014850499375000001,
-    "29": 0.014775998750000002,
-    "30": 7.450062499999855e-05,
-    "31": -0.014850499375000001,
-    "32": -0.014850499375000001,
-    "33": 7.450062499999855e-05,
-    "34": 0.014775998750000002,
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-    "36": -0.014850499375000001,
-    "37": -0.014850499375000001,
-    "38": 0.029700998750000002,
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-    "41": -0.014850499375000001,
-    "42": -0.014850499375000001,
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-    "46": -0.014850499375000001,
-    "47": -0.014850499375000001,
-    "48": -0.014850499375000001,
-    "49": -0.014850499375000001,
-    "50": -0.014850499375000001
+    "1": -0.10279380235968341,
+    "2": -0.13095716137873015,
+    "3": -0.13124242248231174,
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+    "33": -0.14526819693037055,
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+    "36": -0.13206568352604026,
+    "37": -0.08004627851465437,
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+    "39": -0.09245600556232672,
+    "40": -0.13225374226263362,
+    "41": -0.11599365320609464,
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+    "43": -0.11483608658156753,
+    "44": -0.09121964210096198,
+    "45": -0.13410369632947294,
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+    "48": -0.10552865925858317,
+    "49": -0.15913391270414332,
+    "50": -0.15985457481061624
   },
   "adjustments_euro": {
-    "1": -0.014850499375000001,
-    "2": 0.014775998750000002,
-    "3": -0.014850499375000001,
-    "4": -0.014850499375000001,
-    "5": -0.014850499375000001,
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-    "7": -0.014850499375000001,
-    "8": 7.450062499999855e-05,
-    "9": 0.014775998750000002,
-    "10": -0.014850499375000001,
-    "11": -0.014850499375000001,
-    "12": 7.450062499999855e-05
+    "1": -0.10494098821942058,
+    "2": -0.027056675057008192,
+    "3": -0.11830793960044857,
+    "4": -0.1322568921376643,
+    "5": -0.047440853691181416,
+    "6": -0.049155618915874,
+    "7": -0.033897465674045044,
+    "8": -0.10446160077280368,
+    "9": -0.09443567741509633,
+    "10": -0.11645028869966062,
+    "11": -0.09027104593141426,
+    "12": -0.11673125927629154
   },
   "stats": {
-    "incorrect_main": 135,
-    "correct_main": 15,
-    "incorrect_euro": 30,
-    "correct_euro": 6,
-    "cycles": 3,
-    "last_update": "2026-01-06T21:00:43.269037"
+    "incorrect_main": 1710,
+    "correct_main": 190,
+    "incorrect_euro": 380,
+    "correct_euro": 76,
+    "cycles": 38,
+    "last_update": "2026-05-19T09:04:48.805027"
   },
   "learning_rate": 0.1,
-  "last_saved": "2026-01-06T21:00:43.269127"
+  "last_saved": "2026-05-19T09:04:48.805048"
 }
\ No newline at end of file
diff --git a/data/eurojackpot_ml_models/trained_models_euro.pkl b/data/eurojackpot_ml_models/trained_models_euro.pkl
index b16908b..9acaf13 100644
Binary files a/data/eurojackpot_ml_models/trained_models_euro.pkl and b/data/eurojackpot_ml_models/trained_models_euro.pkl differ
diff --git a/data/eurojackpot_ml_models/trained_models_main.pkl b/data/eurojackpot_ml_models/trained_models_main.pkl
index 000ef7c..3364427 100644
Binary files a/data/eurojackpot_ml_models/trained_models_main.pkl and b/data/eurojackpot_ml_models/trained_models_main.pkl differ
diff --git a/data/generated_tips/generation_history.json b/data/generated_tips/generation_history.json
index a40844a..14f3990 100644
--- a/data/generated_tips/generation_history.json
+++ b/data/generated_tips/generation_history.json
@@ -132,6 +132,279 @@
       "file": "weekly_tips_20260112_210015.csv",
       "avg_confidence": 0.49425787623829,
       "avg_quality": 0.6145839981272502
+    },
+    {
+      "timestamp": "2026-01-15T21:00:36.397005",
+      "num_tips": 10,
+      "file": "weekly_tips_20260115_210036.csv",
+      "avg_confidence": 0.47344565270354505,
+      "avg_quality": 0.5904032822464804
+    },
+    {
+      "timestamp": "2026-01-19T21:00:16.311402",
+      "num_tips": 10,
+      "file": "weekly_tips_20260119_210016.csv",
+      "avg_confidence": 0.5070366197890103,
+      "avg_quality": 0.6226148247071303
+    },
+    {
+      "timestamp": "2026-01-22T21:00:14.287352",
+      "num_tips": 10,
+      "file": "weekly_tips_20260122_210014.csv",
+      "avg_confidence": 0.47075732576357926,
+      "avg_quality": 0.5803838357908581
+    },
+    {
+      "timestamp": "2026-01-26T21:00:17.434360",
+      "num_tips": 10,
+      "file": "weekly_tips_20260126_210017.csv",
+      "avg_confidence": 0.49936013462178497,
+      "avg_quality": 0.6116474055527096
+    },
+    {
+      "timestamp": "2026-01-29T21:00:16.110547",
+      "num_tips": 10,
+      "file": "weekly_tips_20260129_210016.csv",
+      "avg_confidence": 0.5027779055427531,
+      "avg_quality": 0.6187842481123684
+    },
+    {
+      "timestamp": "2026-02-02T21:00:16.361899",
+      "num_tips": 10,
+      "file": "weekly_tips_20260202_210016.csv",
+      "avg_confidence": 0.5083071846982502,
+      "avg_quality": 0.6241986235247045
+    },
+    {
+      "timestamp": "2026-02-05T21:00:16.014229",
+      "num_tips": 10,
+      "file": "weekly_tips_20260205_210016.csv",
+      "avg_confidence": 0.47884710375116424,
+      "avg_quality": 0.5928701100797202
+    },
+    {
+      "timestamp": "2026-02-09T21:00:11.982561",
+      "num_tips": 10,
+      "file": "weekly_tips_20260209_210011.csv",
+      "avg_confidence": 0.506005904420015,
+      "avg_quality": 0.6214382457606871
+    },
+    {
+      "timestamp": "2026-02-12T21:00:17.021168",
+      "num_tips": 10,
+      "file": "weekly_tips_20260212_210017.csv",
+      "avg_confidence": 0.516596004507797,
+      "avg_quality": 0.6331268375743684
+    },
+    {
+      "timestamp": "2026-02-16T21:00:16.623850",
+      "num_tips": 10,
+      "file": "weekly_tips_20260216_210016.csv",
+      "avg_confidence": 0.5310911070168177,
+      "avg_quality": 0.646703887932404
+    },
+    {
+      "timestamp": "2026-02-19T21:00:20.011565",
+      "num_tips": 10,
+      "file": "weekly_tips_20260219_210020.csv",
+      "avg_confidence": 0.46533191116318023,
+      "avg_quality": 0.5782823546857643
+    },
+    {
+      "timestamp": "2026-02-23T21:00:17.473274",
+      "num_tips": 10,
+      "file": "weekly_tips_20260223_210017.csv",
+      "avg_confidence": 0.5473864222555807,
+      "avg_quality": 0.664821754795175
+    },
+    {
+      "timestamp": "2026-02-26T21:00:20.683783",
+      "num_tips": 10,
+      "file": "weekly_tips_20260226_210020.csv",
+      "avg_confidence": 0.48232944543098605,
+      "avg_quality": 0.5979756965479743
+    },
+    {
+      "timestamp": "2026-03-02T21:00:16.983767",
+      "num_tips": 10,
+      "file": "weekly_tips_20260302_210016.csv",
+      "avg_confidence": 0.48588377032008034,
+      "avg_quality": 0.5990305136121646
+    },
+    {
+      "timestamp": "2026-03-05T21:00:16.042941",
+      "num_tips": 10,
+      "file": "weekly_tips_20260305_210016.csv",
+      "avg_confidence": 0.48148432120929235,
+      "avg_quality": 0.5954960663012538
+    },
+    {
+      "timestamp": "2026-03-09T21:00:44.719111",
+      "num_tips": 10,
+      "file": "weekly_tips_20260309_210044.csv",
+      "avg_confidence": 0.47513731607893117,
+      "avg_quality": 0.5901054825014217
+    },
+    {
+      "timestamp": "2026-03-12T21:00:12.672070",
+      "num_tips": 10,
+      "file": "weekly_tips_20260312_210012.csv",
+      "avg_confidence": 0.5165879893938297,
+      "avg_quality": 0.631957616666093
+    },
+    {
+      "timestamp": "2026-03-16T21:00:15.064301",
+      "num_tips": 10,
+      "file": "weekly_tips_20260316_210015.csv",
+      "avg_confidence": 0.4437112546020575,
+      "avg_quality": 0.5636041119770521
+    },
+    {
+      "timestamp": "2026-03-19T21:02:48.840594",
+      "num_tips": 10,
+      "file": "weekly_tips_20260319_210248.csv",
+      "avg_confidence": 0.520967549190745,
+      "avg_quality": 0.6380107770109229
+    },
+    {
+      "timestamp": "2026-03-23T21:00:16.623635",
+      "num_tips": 10,
+      "file": "weekly_tips_20260323_210016.csv",
+      "avg_confidence": 0.4511070123898933,
+      "avg_quality": 0.5706980214609187
+    },
+    {
+      "timestamp": "2026-03-26T21:00:35.316354",
+      "num_tips": 10,
+      "file": "weekly_tips_20260326_210035.csv",
+      "avg_confidence": 0.45118965830170754,
+      "avg_quality": 0.5677275907732668
+    },
+    {
+      "timestamp": "2026-03-31T11:49:39.227248",
+      "num_tips": 10,
+      "file": "weekly_tips_20260331_114939.csv",
+      "avg_confidence": 0.4916429475874513,
+      "avg_quality": 0.6106521030019004
+    },
+    {
+      "timestamp": "2026-04-02T21:00:24.425902",
+      "num_tips": 10,
+      "file": "weekly_tips_20260402_210024.csv",
+      "avg_confidence": 0.4519687967653286,
+      "avg_quality": 0.5709825204600992
+    },
+    {
+      "timestamp": "2026-04-06T21:00:15.654198",
+      "num_tips": 10,
+      "file": "weekly_tips_20260406_210015.csv",
+      "avg_confidence": 0.4519687967653286,
+      "avg_quality": 0.5709825204600992
+    },
+    {
+      "timestamp": "2026-04-09T21:00:17.621420",
+      "num_tips": 10,
+      "file": "weekly_tips_20260409_210017.csv",
+      "avg_confidence": 0.4519687967653286,
+      "avg_quality": 0.5709825204600992
+    },
+    {
+      "timestamp": "2026-04-13T21:13:31.089816",
+      "num_tips": 10,
+      "file": "weekly_tips_20260413_211331.csv",
+      "avg_confidence": 0.4519687967653286,
+      "avg_quality": 0.5709825204600992
+    },
+    {
+      "timestamp": "2026-04-16T21:00:15.952947",
+      "num_tips": 10,
+      "file": "weekly_tips_20260416_210015.csv",
+      "avg_confidence": 0.4519687967653286,
+      "avg_quality": 0.5709825204600992
+    },
+    {
+      "timestamp": "2026-04-20T21:00:14.991940",
+      "num_tips": 10,
+      "file": "weekly_tips_20260420_210014.csv",
+      "avg_confidence": 0.4519687967653286,
+      "avg_quality": 0.5709825204600992
+    },
+    {
+      "timestamp": "2026-04-23T21:12:08.255403",
+      "num_tips": 10,
+      "file": "weekly_tips_20260423_211208.csv",
+      "avg_confidence": 0.4519687967653286,
+      "avg_quality": 0.5709825204600992
+    },
+    {
+      "timestamp": "2026-04-27T21:00:13.534906",
+      "num_tips": 10,
+      "file": "weekly_tips_20260427_210013.csv",
+      "avg_confidence": 0.48127824496557114,
+      "avg_quality": 0.5980047795669126
+    },
+    {
+      "timestamp": "2026-04-30T21:00:18.020165",
+      "num_tips": 10,
+      "file": "weekly_tips_20260430_210018.csv",
+      "avg_confidence": 0.48127824496557114,
+      "avg_quality": 0.5980047795669126
+    },
+    {
+      "timestamp": "2026-05-04T21:00:17.575648",
+      "num_tips": 10,
+      "file": "weekly_tips_20260504_210017.csv",
+      "avg_confidence": 0.48127824496557114,
+      "avg_quality": 0.5980047795669126
+    },
+    {
+      "timestamp": "2026-05-07T21:00:21.292869",
+      "num_tips": 10,
+      "file": "weekly_tips_20260507_210021.csv",
+      "avg_confidence": 0.48127824496557114,
+      "avg_quality": 0.5980047795669126
+    },
+    {
+      "timestamp": "2026-05-11T21:00:15.718384",
+      "num_tips": 10,
+      "file": "weekly_tips_20260511_210015.csv",
+      "avg_confidence": 0.48127824496557114,
+      "avg_quality": 0.5980047795669126
+    },
+    {
+      "timestamp": "2026-05-14T21:00:17.802557",
+      "num_tips": 10,
+      "file": "weekly_tips_20260514_210017.csv",
+      "avg_confidence": 0.48127824496557114,
+      "avg_quality": 0.5980047795669126
+    },
+    {
+      "timestamp": "2026-05-18T21:00:15.215525",
+      "num_tips": 10,
+      "file": "weekly_tips_20260518_210015.csv",
+      "avg_confidence": 0.48127824496557114,
+      "avg_quality": 0.5980047795669126
+    },
+    {
+      "timestamp": "2026-05-19T09:06:31.632241",
+      "num_tips": 10,
+      "file": "weekly_tips_20260519_090631.csv",
+      "avg_confidence": 0.4653688395892314,
+      "avg_quality": 0.5851349854679404
+    },
+    {
+      "timestamp": "2026-05-19T09:28:02.588177",
+      "num_tips": 10,
+      "file": "weekly_tips_20260519_092802.csv",
+      "avg_confidence": 0.49658478135540934,
+      "avg_quality": 0.6106212035500489
+    },
+    {
+      "timestamp": "2026-05-19T09:30:17.610297",
+      "num_tips": 10,
+      "file": "weekly_tips_20260519_093017.csv",
+      "avg_confidence": 0.47597948672799795,
+      "avg_quality": 0.5941141824821975
     }
   ]
 }
\ No newline at end of file
diff --git a/data/learning_log.json b/data/learning_log.json
index da9480e..8160c79 100644
--- a/data/learning_log.json
+++ b/data/learning_log.json
@@ -25,6 +25,499 @@
         "main": 0.0,
         "euro": 0.2
       }
+    },
+    {
+      "timestamp": "2026-01-14T11:55:27.020649",
+      "draw_date": "2026-01-13",
+      "evaluation": {
+        "main": 3,
+        "euro": 1,
+        "tip": 5
+      },
+      "avg_matches": {
+        "main": 0.9,
+        "euro": 0.1
+      }
+    },
+    {
+      "timestamp": "2026-01-17T08:00:38.348746",
+      "draw_date": "2026-01-16",
+      "evaluation": {
+        "main": 1,
+        "euro": 2,
+        "tip": 6
+      },
+      "avg_matches": {
+        "main": 0.6,
+        "euro": 1.0
+      }
+    },
+    {
+      "timestamp": "2026-01-21T08:00:36.393922",
+      "draw_date": "2026-01-20",
+      "evaluation": {
+        "main": 2,
+        "euro": 0,
+        "tip": 7
+      },
+      "avg_matches": {
+        "main": 0.4,
+        "euro": 0.0
+      }
+    },
+    {
+      "timestamp": "2026-01-24T08:00:39.548611",
+      "draw_date": "2026-01-23",
+      "evaluation": {
+        "main": 1,
+        "euro": 0,
+        "tip": 2
+      },
+      "avg_matches": {
+        "main": 0.4,
+        "euro": 0.0
+      }
+    },
+    {
+      "timestamp": "2026-01-28T08:00:33.205219",
+      "draw_date": "2026-01-27",
+      "evaluation": {
+        "main": 2,
+        "euro": 0,
+        "tip": 6
+      },
+      "avg_matches": {
+        "main": 0.4,
+        "euro": 0.2
+      }
+    },
+    {
+      "timestamp": "2026-01-31T08:00:32.122558",
+      "draw_date": "2026-01-30",
+      "evaluation": {
+        "main": 2,
+        "euro": 0,
+        "tip": 5
+      },
+      "avg_matches": {
+        "main": 0.6,
+        "euro": 0.0
+      }
+    },
+    {
+      "timestamp": "2026-02-04T08:15:31.767102",
+      "draw_date": "2026-02-03",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.7,
+        "euro": 0.4
+      }
+    },
+    {
+      "timestamp": "2026-02-07T08:09:09.885694",
+      "draw_date": "2026-02-06",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.7,
+        "euro": 0.7
+      }
+    },
+    {
+      "timestamp": "2026-02-11T08:00:51.090316",
+      "draw_date": "2026-02-10",
+      "evaluation": {
+        "main": 2,
+        "euro": 1,
+        "tip": 6
+      },
+      "avg_matches": {
+        "main": 0.5,
+        "euro": 0.6
+      }
+    },
+    {
+      "timestamp": "2026-02-14T08:00:37.526380",
+      "draw_date": "2026-02-13",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.5,
+        "euro": 0.5
+      }
+    },
+    {
+      "timestamp": "2026-02-18T08:00:35.624032",
+      "draw_date": "2026-02-17",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 6
+      },
+      "avg_matches": {
+        "main": 0.1,
+        "euro": 0.2
+      }
+    },
+    {
+      "timestamp": "2026-02-21T08:00:36.839728",
+      "draw_date": "2026-02-20",
+      "evaluation": {
+        "main": 1,
+        "euro": 0,
+        "tip": 2
+      },
+      "avg_matches": {
+        "main": 0.2,
+        "euro": 0.0
+      }
+    },
+    {
+      "timestamp": "2026-02-25T08:00:32.739559",
+      "draw_date": "2026-02-24",
+      "evaluation": {
+        "main": 3,
+        "euro": 0,
+        "tip": 8
+      },
+      "avg_matches": {
+        "main": 1.6,
+        "euro": 0.1
+      }
+    },
+    {
+      "timestamp": "2026-02-28T08:00:36.899068",
+      "draw_date": "2026-02-27",
+      "evaluation": {
+        "main": 1,
+        "euro": 0,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.9,
+        "euro": 0.0
+      }
+    },
+    {
+      "timestamp": "2026-03-04T08:00:35.914866",
+      "draw_date": "2026-03-03",
+      "evaluation": {
+        "main": 1,
+        "euro": 0,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.2,
+        "euro": 0.4
+      }
+    },
+    {
+      "timestamp": "2026-03-07T08:01:50.476303",
+      "draw_date": "2026-03-06",
+      "evaluation": {
+        "main": 0,
+        "euro": 1,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.6
+      }
+    },
+    {
+      "timestamp": "2026-03-11T08:05:41.163992",
+      "draw_date": "2026-03-10",
+      "evaluation": {
+        "main": 3,
+        "euro": 0,
+        "tip": 6
+      },
+      "avg_matches": {
+        "main": 1.2,
+        "euro": 0.3
+      }
+    },
+    {
+      "timestamp": "2026-03-14T08:04:02.373169",
+      "draw_date": "2026-03-13",
+      "evaluation": {
+        "main": 1,
+        "euro": 0,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.2,
+        "euro": 0.4
+      }
+    },
+    {
+      "timestamp": "2026-03-18T08:00:37.435414",
+      "draw_date": "2026-03-17",
+      "evaluation": {
+        "main": 1,
+        "euro": 2,
+        "tip": 10
+      },
+      "avg_matches": {
+        "main": 0.2,
+        "euro": 0.8
+      }
+    },
+    {
+      "timestamp": "2026-03-21T08:09:51.906930",
+      "draw_date": "2026-03-20",
+      "evaluation": {
+        "main": 2,
+        "euro": 1,
+        "tip": 6
+      },
+      "avg_matches": {
+        "main": 0.5,
+        "euro": 0.1
+      }
+    },
+    {
+      "timestamp": "2026-03-25T08:01:44.927862",
+      "draw_date": "2026-03-24",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 3
+      },
+      "avg_matches": {
+        "main": 0.6,
+        "euro": 0.5
+      }
+    },
+    {
+      "timestamp": "2026-03-28T08:00:38.065825",
+      "draw_date": "2026-03-27",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 1
+      },
+      "avg_matches": {
+        "main": 0.2,
+        "euro": 0.6
+      }
+    },
+    {
+      "timestamp": "2026-04-01T08:15:32.393678",
+      "draw_date": "2026-03-31",
+      "evaluation": {
+        "main": 1,
+        "euro": 0,
+        "tip": 2
+      },
+      "avg_matches": {
+        "main": 0.6,
+        "euro": 0.1
+      }
+    },
+    {
+      "timestamp": "2026-04-25T08:00:37.702550",
+      "draw_date": "2026-04-24",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 6
+      },
+      "avg_matches": {
+        "main": 0.2,
+        "euro": 0.4
+      }
+    },
+    {
+      "timestamp": "2026-05-19T09:01:38.051039",
+      "draw_date": "2026-05-15",
+      "evaluation": {
+        "main": 1,
+        "euro": 1,
+        "tip": 8
+      },
+      "avg_matches": {
+        "main": 0.5,
+        "euro": 0.3
+      }
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.802220",
+      "draw_date": "2026-01-09",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.802560",
+      "draw_date": "2026-04-03",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.802849",
+      "draw_date": "2026-04-07",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.803128",
+      "draw_date": "2026-04-10",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.803396",
+      "draw_date": "2026-04-14",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.803660",
+      "draw_date": "2026-04-17",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.803919",
+      "draw_date": "2026-04-21",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.804186",
+      "draw_date": "2026-04-28",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.804444",
+      "draw_date": "2026-05-01",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.804706",
+      "draw_date": "2026-05-05",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.804960",
+      "draw_date": "2026-05-08",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
+    },
+    {
+      "timestamp": "2026-05-19T09:04:48.805219",
+      "draw_date": "2026-05-12",
+      "evaluation": {
+        "main": 0,
+        "euro": 0,
+        "tip": 0
+      },
+      "avg_matches": {
+        "main": 0.0,
+        "euro": 0.0
+      },
+      "note": "retroactive_learning"
     }
   ]
 }
\ No newline at end of file
diff --git a/scripts/automation/auto_update_and_learn.py b/scripts/automation/auto_update_and_learn.py
index 20e1883..86d0421 100644
--- a/scripts/automation/auto_update_and_learn.py
+++ b/scripts/automation/auto_update_and_learn.py
@@ -27,6 +27,7 @@ project_dir = os.path.dirname(os.path.dirname(script_dir))
 sys.path.insert(0, project_dir)
 
 from scripts.utils.update_from_api import EurojackpotAPIUpdater
+from scripts.utils.update_from_eurojackpot_zahlen_eu import EurojackpotUpdater as WebScraper
 from scripts.generators.ultimate_ai_ml_eurojackpot_generator import UltimateAIMLEurojackpotGenerator
 from scripts.utils.notifier import EurojackpotNotifier
 
@@ -129,7 +130,16 @@ class AutoUpdateAndLearn:
             if success:
                 print("   ✅ Daten erfolgreich aktualisiert")
             else:
-                print("   ⚠️  Update ohne neue Daten")
+                print("   ⚠️  API-Update ohne neue Daten, versuche Web-Scraper...")
+                try:
+                    scraper = WebScraper(self.data_file)
+                    success = scraper.update(create_backup=False)
+                    if success:
+                        print("   ✅ Web-Scraper erfolgreich")
+                    else:
+                        print("   ⚠️  Web-Scraper ohne neue Daten")
+                except Exception as scrape_err:
+                    print(f"   ❌ Web-Scraper Fehler: {scrape_err}")
 
             return success
 
diff --git a/scripts/generators/ultimate_ai_ml_eurojackpot_generator.py b/scripts/generators/ultimate_ai_ml_eurojackpot_generator.py
index 3ad4f74..8334387 100644
--- a/scripts/generators/ultimate_ai_ml_eurojackpot_generator.py
+++ b/scripts/generators/ultimate_ai_ml_eurojackpot_generator.py
@@ -164,6 +164,12 @@ class UltimateAIMLEurojackpotGenerator:
         # 6. Initialize Real-Time Learning
         print("📚 Activating Real-Time Learning...", end=" ", flush=True)
         self.real_time_learner.initialize(self.features_df)
+        # Langzeit-Frequenz als Dämpfer übergeben
+        _main_counts = self.df[['Z1','Z2','Z3','Z4','Z5']].stack().value_counts()
+        _euro_counts = self.df[['SZ1','SZ2']].stack().value_counts()
+        freq_main = {n: int(_main_counts.get(n, 0)) for n in range(1, 51)}
+        freq_euro = {n: int(_euro_counts.get(n, 0)) for n in range(1, 13)}
+        self.real_time_learner.set_frequency_prior(freq_main, freq_euro)
         print("✅")
         
         self.is_trained = self.ai_ml_engine.is_trained
@@ -251,6 +257,21 @@ class UltimateAIMLEurojackpotGenerator:
             tip_counter += len(tips)
             print(f"✅ ({count} tips)")
         
+        # Strukturfilter: Summe + Parität korrigieren
+        fixed_count = 0
+        for tip in all_tips:
+            if not self._passes_structural_constraints(tip['main_numbers']):
+                tip['main_numbers'] = self._apply_structural_fix(tip['main_numbers'], main_predictions)
+                tip['main_ai_score'] = np.mean([main_predictions.get(n, 0.1) for n in tip['main_numbers']])
+                tip['pattern_weight'] = self.pattern_engine.calculate_pattern_weight(tip['main_numbers'])
+                tip['confidence'] = tip['main_ai_score'] * 0.5 + tip.get('euro_ai_score', 0.0) * 0.3 + tip['pattern_weight'] * 0.2
+                tip['quality'] = self._calculate_quality_score(
+                    tip['main_numbers'], tip['euro_numbers'], main_predictions, euro_predictions, tip['pattern_weight']
+                )
+                fixed_count += 1
+        if fixed_count:
+            print(f"   🔧 {fixed_count} Tip(s) via Strukturfilter korrigiert (Summe/Parität)")
+
         # Output all tips
         print(f"\n📋 RESULTS:")
         print("=" * 100)
@@ -459,27 +480,20 @@ class UltimateAIMLEurojackpotGenerator:
         
         selected_main = []
         for position in range(5):
-            best_candidate = None
-            best_score = -1
-            
-            for candidate in all_candidates:
-                if candidate not in selected_main:
-                    ai_s = main_preds.get(candidate, 0.1)
-                    pattern_s = self.pattern_engine.get_number_pattern_score(candidate)
-                    diversity_s = self._calculate_diversity_score(candidate, selected_main) if selected_main else 0.5
-                    
-                    ensemble_score = (ai_s * 0.4 + pattern_s * 0.3 + diversity_s * 0.3)
-                    
-                    if ensemble_score > best_score:
-                        best_score = ensemble_score
-                        best_candidate = candidate
-            
-            if best_candidate:
-                selected_main.append(best_candidate)
-            else:
-                available = [n for n in range(1, 51) if n not in selected_main]
-                if available:
-                    selected_main.append(random.choice(available))
+            candidates = [c for c in all_candidates if c not in selected_main]
+            if not candidates:
+                candidates = [n for n in range(1, 51) if n not in selected_main]
+
+            scores = []
+            for c in candidates:
+                ai_s = main_preds.get(c, 0.1)
+                pattern_s = self.pattern_engine.get_number_pattern_score(c)
+                diversity_s = self._calculate_diversity_score(c, selected_main) if selected_main else 0.5
+                scores.append(ai_s * 0.4 + pattern_s * 0.3 + diversity_s * 0.3)
+
+            # Gewichtete Zufallsauswahl: Qualität bleibt hoch, Duplikate werden vermieden
+            choice = random.choices(candidates, weights=scores)[0]
+            selected_main.append(choice)
         
         selected_main = sorted(selected_main[:5])
         
@@ -505,6 +519,69 @@ class UltimateAIMLEurojackpotGenerator:
             'quality': quality
         }
     
+    def _passes_structural_constraints(self, main_numbers):
+        """Prüft Summenbereich und Parität."""
+        s = sum(main_numbers)
+        if s < 95 or s > 165:
+            return False
+        even = sum(1 for n in main_numbers if n % 2 == 0)
+        if even == 0 or even == 5:
+            return False
+        return True
+
+    def _apply_structural_fix(self, main_numbers, main_preds):
+        """Korrigiert Summe/Parität durch minimalen Tausch."""
+        numbers = list(main_numbers)
+
+        # Parität: mind. 1 gerade und 1 ungerade
+        even = [n for n in numbers if n % 2 == 0]
+        odd  = [n for n in numbers if n % 2 != 0]
+        if len(even) == 0:
+            # alle ungerade → tausche den am schlechtesten bewerteten gegen bestes gerades
+            worst = min(odd, key=lambda n: main_preds.get(n, 0))
+            candidates = sorted(
+                [n for n in range(2, 51, 2) if n not in numbers],
+                key=lambda n: -main_preds.get(n, 0)
+            )
+            if candidates:
+                numbers.remove(worst)
+                numbers.append(candidates[0])
+        elif len(odd) == 0:
+            worst = min(even, key=lambda n: main_preds.get(n, 0))
+            candidates = sorted(
+                [n for n in range(1, 51, 2) if n not in numbers],
+                key=lambda n: -main_preds.get(n, 0)
+            )
+            if candidates:
+                numbers.remove(worst)
+                numbers.append(candidates[0])
+
+        # Summe korrigieren
+        for _ in range(10):
+            s = sum(numbers)
+            if 95 <= s <= 165:
+                break
+            if s > 165:
+                highest = max(numbers)
+                candidates = sorted(
+                    [n for n in range(1, highest) if n not in numbers],
+                    key=lambda n: -main_preds.get(n, 0)
+                )
+                if candidates:
+                    numbers.remove(highest)
+                    numbers.append(candidates[0])
+            else:
+                lowest = min(numbers)
+                candidates = sorted(
+                    [n for n in range(lowest + 1, 51) if n not in numbers],
+                    key=lambda n: -main_preds.get(n, 0)
+                )
+                if candidates:
+                    numbers.remove(lowest)
+                    numbers.append(candidates[0])
+
+        return sorted(numbers)
+
     def _calculate_diversity_score(self, candidate, selected):
         """Berechnet Diversität."""
         if not selected:
@@ -1473,6 +1550,9 @@ class EurojackpotRealTimeLearner:
         self.stats = defaultdict(int)
         self.cache_path = cache_path
         self.learning_state_file = os.path.join(cache_path, 'learning_state.json') if cache_path else None
+        self.freq_prior_main = {}   # Langzeit-Frequenz Dämpfer
+        self.freq_prior_euro = {}
+        self.freq_alpha = 0.2       # 20% Langzeit-Frequenz, 80% Learner
 
         # Lade gespeicherten State
         if self.learning_state_file and os.path.exists(self.learning_state_file):
@@ -1482,24 +1562,37 @@ class EurojackpotRealTimeLearner:
         """Initialisiert Learning."""
         self.features_df = features_df
 
-    def adjust_main_predictions(self, predictions):
-        """Passt Main Predictions an."""
-        adjusted = {}
+    def set_frequency_prior(self, freq_main, freq_euro):
+        """Setzt Langzeit-Frequenz als Dämpfer (normalisiert auf [0,1])."""
+        max_main = max(freq_main.values(), default=0) or 1
+        max_euro = max(freq_euro.values(), default=0) or 1
+        self.freq_prior_main = {n: v / max_main for n, v in freq_main.items()}
+        self.freq_prior_euro = {n: v / max_euro for n, v in freq_euro.items()}
 
+    def adjust_main_predictions(self, predictions):
+        """Passt Main Predictions an, gedämpft durch Langzeit-Frequenz."""
+        adjusted = {}
         for number, pred in predictions.items():
             adjustment = self.adjustments_main.get(number, 0)
-            adjusted[number] = np.clip(pred + adjustment * self.learning_rate, 0, 1)
-
+            learner_val = np.clip(pred + adjustment * self.learning_rate, 0, 1)
+            if self.freq_prior_main:
+                freq_val = self.freq_prior_main.get(number, 0.5)
+                adjusted[number] = (1 - self.freq_alpha) * learner_val + self.freq_alpha * freq_val
+            else:
+                adjusted[number] = learner_val
         return adjusted
 
     def adjust_euro_predictions(self, predictions):
-        """Passt Euro Predictions an."""
+        """Passt Euro Predictions an, gedämpft durch Langzeit-Frequenz."""
         adjusted = {}
-
         for number, pred in predictions.items():
             adjustment = self.adjustments_euro.get(number, 0)
-            adjusted[number] = np.clip(pred + adjustment * self.learning_rate, 0, 1)
-
+            learner_val = np.clip(pred + adjustment * self.learning_rate, 0, 1)
+            if self.freq_prior_euro:
+                freq_val = self.freq_prior_euro.get(number, 0.5)
+                adjusted[number] = (1 - self.freq_alpha) * learner_val + self.freq_alpha * freq_val
+            else:
+                adjusted[number] = learner_val
         return adjusted
 
     def learn_from_result(self, drawing):