first commit

.gitignore

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+db/
+venv/
+.vscode/

anomaly_detect.py

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+import numpy as np # type: ignore
+import pandas as pd
+from sklearn.ensemble import IsolationForest
+
+def generate_sample_data(n_normal=100, n_anomaly=10, random_state=42):
+    np.random.seed(random_state)
+    # Generate normal data (e.g., backup size, duration, file count)
+    normal_data = np.random.normal(loc=[1000, 60, 500], scale=[100, 10, 50], size=(n_normal, 3))
+    # Generate anomalous data (e.g., much larger/smaller values)
+    anomaly_data = np.random.normal(loc=[2000, 200, 50], scale=[50, 20, 10], size=(n_anomaly, 3))
+    # Combine and create DataFrame
+    data = np.vstack([normal_data, anomaly_data])
+    df = pd.DataFrame(data, columns=["backup_size_mb", "duration_sec", "file_count"])
+    df["label"] = [0]*n_normal + [1]*n_anomaly  # 0: normal, 1: anomaly
+    return df
+
+def try_isolation_forest(df):
+    clf = IsolationForest(random_state=42)
+    features = df[["backup_size_mb", "duration_sec", "file_count"]]
+    preds = clf.fit_predict(features)
+    df["predicted_anomaly"] = (preds == -1).astype(int)
+    print(df[["backup_size_mb", "duration_sec", "file_count", "label", "predicted_anomaly"]])
+
+if __name__ == "__main__":
+    df = generate_sample_data()
+    try_isolation_forest(df)

anomaly_detect2.py

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+import numpy as np
+import pandas as pd
+import random
+from datetime import datetime, timedelta
+from sklearn.ensemble import IsolationForest
+
+def random_time():
+    start = datetime(2020, 1, 1)
+    end = datetime.now()
+    delta = end - start
+    random_seconds = random.randint(0, int(delta.total_seconds()))
+    return (start + timedelta(seconds=random_seconds)).isoformat()
+
+def generate_good_log():
+    log = {
+        "DeletedFiles": random.randint(0, 5),
+        "DeletedFolders": random.randint(0, 2),
+        "ModifiedFiles": random.randint(0, 50),
+        "ExaminedFiles": random.randint(500, 1000),
+        "OpenedFiles": random.randint(0, 50),
+        "AddedFiles": random.randint(0, 10),
+        "SizeOfModifiedFiles": random.randint(1000000, 2000000000),
+        "SizeOfAddedFiles": random.randint(0, 10000000),
+        "SizeOfExaminedFiles": random.randint(1000000000, 8000000000),
+        "SizeOfOpenedFiles": random.randint(0, 2000000000),
+        "NotProcessedFiles": 0,
+        "AddedFolders": random.randint(0, 2),
+        "TooLargeFiles": 0,
+        "FilesWithError": 0,
+        "ModifiedFolders": random.randint(0, 2),
+        "ModifiedSymlinks": 0,
+        "AddedSymlinks": 0,
+        "DeletedSymlinks": 0,
+        "PartialBackup": False,
+        "Dryrun": False,
+        "MainOperation": "Backup",
+        "ParsedResult": "Success",
+        "Interrupted": False,
+        "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+        "EndTime": random_time(),
+        "BeginTime": random_time(),
+        "Duration": "00:00:{:02d}.{:07d}".format(random.randint(10, 59), random.randint(0, 9999999)),
+        "MessagesActualLength": random.randint(10, 30),
+        "WarningsActualLength": random.randint(0, 1),
+        "ErrorsActualLength": 0,
+        "Messages": [],
+        "Warnings": [],
+        "Errors": [],
+        "BackendStatistics": {
+            "RemoteCalls": random.randint(5, 15),
+            "BytesUploaded": random.randint(1000000, 100000000),
+            "BytesDownloaded": random.randint(1000000, 100000000),
+            "FilesUploaded": random.randint(1, 10),
+            "FilesDownloaded": random.randint(1, 10),
+            "FilesDeleted": random.randint(0, 5),
+            "FoldersCreated": random.randint(0, 2),
+            "RetryAttempts": 0,
+            "UnknownFileSize": 0,
+            "UnknownFileCount": 0,
+            "KnownFileCount": random.randint(100, 1000),
+            "KnownFileSize": random.randint(1000000000, 8000000000),
+            "LastBackupDate": random_time(),
+            "BackupListCount": random.randint(5, 20),
+            "TotalQuotaSpace": 1932937191424,
+            "FreeQuotaSpace": random.randint(400000000000, 800000000000),
+            "AssignedQuotaSpace": -1,
+            "ReportedQuotaError": False,
+            "ReportedQuotaWarning": False,
+            "MainOperation": "Backup",
+            "ParsedResult": "Success",
+            "Interrupted": False,
+            "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+            "EndTime": "0001-01-01T00:00:00",
+            "BeginTime": random_time(),
+            "Duration": "00:00:00",
+            "MessagesActualLength": 0,
+            "WarningsActualLength": 0,
+            "ErrorsActualLength": 0,
+            "Messages": None,
+            "Warnings": None,
+            "Errors": None
+        }
+    }
+    return log
+
+def generate_bad_log():
+    errors = ["Error: Disk full", "Error: Permission denied", "Error: Network timeout"]
+    warnings = ["Warning: Slow upload", "Warning: File skipped", "Warning: Retry exceeded"]
+    log = {
+        "DeletedFiles": random.randint(100, 10000),  # excessive deletions
+        "DeletedFolders": random.randint(10, 100),
+        "ModifiedFiles": random.randint(0, 10),
+        "ExaminedFiles": random.randint(500, 1000),
+        "OpenedFiles": random.randint(0, 10),
+        "AddedFiles": random.randint(0, 2),
+        "SizeOfModifiedFiles": random.randint(1000000, 200000000),
+        "SizeOfAddedFiles": random.randint(0, 1000000),
+        "SizeOfExaminedFiles": random.randint(1000000000, 8000000000),
+        "SizeOfOpenedFiles": random.randint(0, 200000000),
+        "NotProcessedFiles": random.randint(1, 10),
+        "AddedFolders": random.randint(0, 2),
+        "TooLargeFiles": random.randint(0, 2),
+        "FilesWithError": random.randint(1, 5),
+        "ModifiedFolders": random.randint(0, 2),
+        "ModifiedSymlinks": 0,
+        "AddedSymlinks": 0,
+        "DeletedSymlinks": 0,
+        "PartialBackup": True,
+        "Dryrun": False,
+        "MainOperation": "Backup",
+        "ParsedResult": "Error",
+        "Interrupted": True,
+        "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+        "EndTime": random_time(),
+        "BeginTime": random_time(),
+        "Duration": "00:00:{:02d}.{:07d}".format(random.randint(10, 59), random.randint(0, 9999999)),
+        "MessagesActualLength": random.randint(30, 100),
+        "WarningsActualLength": random.randint(5, 20),
+        "ErrorsActualLength": random.randint(1, 5),
+        "Messages": [random.choice(warnings) for _ in range(random.randint(5, 20))],
+        "Warnings": [random.choice(warnings) for _ in range(random.randint(5, 20))],
+        "Errors": [random.choice(errors) for _ in range(random.randint(1, 5))],
+        "BackendStatistics": {
+            "RemoteCalls": random.randint(5, 15),
+            "BytesUploaded": random.randint(100000, 1000000),
+            "BytesDownloaded": random.randint(100000, 1000000),
+            "FilesUploaded": random.randint(0, 2),
+            "FilesDownloaded": random.randint(0, 2),
+            "FilesDeleted": random.randint(100, 10000),
+            "FoldersCreated": random.randint(0, 2),
+            "RetryAttempts": random.randint(1, 10),
+            "UnknownFileSize": random.randint(0, 1),
+            "UnknownFileCount": random.randint(0, 1),
+            "KnownFileCount": random.randint(100, 1000),
+            "KnownFileSize": random.randint(1000000000, 8000000000),
+            "LastBackupDate": random_time(),
+            "BackupListCount": random.randint(5, 20),
+            "TotalQuotaSpace": 1932937191424,
+            "FreeQuotaSpace": random.randint(400000000000, 800000000000),
+            "AssignedQuotaSpace": -1,
+            "ReportedQuotaError": random.choice([True, False]),
+            "ReportedQuotaWarning": random.choice([True, False]),
+            "MainOperation": "Backup",
+            "ParsedResult": "Error",
+            "Interrupted": True,
+            "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+            "EndTime": "0001-01-01T00:00:00",
+            "BeginTime": random_time(),
+            "Duration": "00:00:00",
+            "MessagesActualLength": random.randint(5, 20),
+            "WarningsActualLength": random.randint(5, 20),
+            "ErrorsActualLength": random.randint(1, 5),
+            "Messages": None,
+            "Warnings": None,
+            "Errors": None
+        }
+    }
+    return log
+
+def flatten_features(log):
+    """Extracts relevant features from top-level and BackendStatistics."""
+    stats = log.get("BackendStatistics", {})
+    return {
+        "KnownFileCount": stats.get("KnownFileCount", 0),
+        "KnownFileSize": stats.get("KnownFileSize", 0),
+        "FilesUploaded": stats.get("FilesUploaded", 0),
+        "FilesDownloaded": stats.get("FilesDownloaded", 0),
+        "FilesDeleted": stats.get("FilesDeleted", 0),
+        "BytesUploaded": stats.get("BytesUploaded", 0),
+        "BytesDownloaded": stats.get("BytesDownloaded", 0),
+        "Duration_sec": random.uniform(30, 600),  # You can parse Duration if needed
+        "ErrorsActualLength": log.get("ErrorsActualLength", 0),
+        "WarningsActualLength": log.get("WarningsActualLength", 0),
+        "label": 1 if log.get("ParsedResult") == "Error" or log.get("Interrupted") else 0
+    }
+
+def generate_sample_data(n_good=100, n_bad=10, random_state=42):
+    np.random.seed(random_state)
+    random.seed(random_state)
+    good_logs = [generate_good_log() for _ in range(n_good)]
+    bad_logs = [generate_bad_log() for _ in range(n_bad)]
+    all_logs = good_logs + bad_logs
+    features = [flatten_features(log) for log in all_logs]
+    df = pd.DataFrame(features)
+    return df
+
+def try_isolation_forest(df):
+    features = [
+        "KnownFileCount", "KnownFileSize", "FilesUploaded", "FilesDownloaded",
+        "FilesDeleted", "BytesUploaded", "BytesDownloaded",
+        "Duration_sec", "ErrorsActualLength", "WarningsActualLength"
+    ]
+    clf = IsolationForest(random_state=42)
+    preds = clf.fit_predict(df[features])
+    df["predicted_anomaly"] = (preds == -1).astype(int)
+    print(df[features + ["label", "predicted_anomaly"]])
+
+if __name__ == "__main__":
+    df = generate_sample_data()
+    try_isolation_forest(df)

complex_anomaly.py

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+import requests
+from datetime import datetime, timedelta
+import time
+import numpy as np
+import pandas as pd
+import yfinance as yf
+from datetime import datetime, timedelta
+from sklearn.ensemble import IsolationForest
+from sklearn.covariance import EllipticEnvelope
+from sklearn.preprocessing import RobustScaler
+from statsmodels.tsa.api import VAR
+from pyod.models.lof import LOF
+import matplotlib.pyplot as plt
+import seaborn as sns
+import warnings
+warnings.filterwarnings('ignore')
+
+class CryptoAnomalyDetector:
+    def __init__(self, tickers=['bitcoin', 'ethereum', 'solana']):
+        # CoinGecko uses lowercase names or IDs instead of tickers
+        self.tickers = tickers
+        self.coin_ids = tickers  # For simplicity, using names as IDs
+        self.models = {}
+        self.scalers = {}
+        self.thresholds = {}
+        self.correlation_matrices = []
+        self.last_training_date = None
+        
+    def fetch_data(self, days=30):
+        """Fetch historical data using CoinGecko API"""
+        base_url = "https://api.coingecko.com/api/v3"
+        end_date = datetime.now()
+        start_date = end_date - timedelta(days=days)
+        
+        # Convert to UNIX timestamps
+        end_timestamp = int(end_date.timestamp())
+        start_timestamp = int(start_date.timestamp())
+        
+        data = {}
+        
+        for coin_id in self.coin_ids:
+            url = f"{base_url}/coins/{coin_id}/market_chart/range"
+            params = {
+                'vs_currency': 'usd',
+                # 'from': start_timestamp,
+                # 'to': end_timestamp
+                'days': 30
+            }
+            
+            # Add delay to avoid rate limiting
+            time.sleep(1)
+            
+            try:
+                response = requests.get(url, params=params)
+                response.raise_for_status()
+                coin_data = response.json()
+                
+                # Extract prices and create DataFrame
+                prices = pd.DataFrame(coin_data['prices'], columns=['timestamp', 'price'])
+                prices['date'] = pd.to_datetime(prices['timestamp'], unit='ms')
+                prices.set_index('date', inplace=True)
+                data[coin_id] = prices['price']
+                
+            except requests.exceptions.RequestException as e:
+                print(f"Error fetching data for {coin_id}: {e}")
+                data[coin_id] = pd.Series()  # Empty series if error occurs
+        
+        # Combine all series into a DataFrame
+        combined_data = pd.DataFrame(data)
+        
+        # Resample to daily data if needed (CoinGecko might return more granular data)
+        combined_data = combined_data.resample('D').last().ffill()
+        
+        return combined_data.dropna()
+    
+    # ... [rest of the class remains the same] ...
+    
+    def prepare_features(self, data):
+        """Create features for anomaly detection"""
+        # Calculate log returns
+        returns = np.log(data).diff().dropna()
+        
+        # Calculate rolling volatilities (3-day and 7-day)
+        features = pd.DataFrame()
+        for ticker in self.tickers:
+            features[f'{ticker}_return'] = returns[ticker]
+            features[f'{ticker}_vol_3d'] = returns[ticker].rolling(3).std()
+            features[f'{ticker}_vol_7d'] = returns[ticker].rolling(7).std()
+        
+        # Add correlation features
+        rolling_corr = returns.rolling(5).corr().dropna()
+        self.correlation_matrices = rolling_corr.groupby(level=0).apply(lambda x: x)
+        
+        # Add market-wide volatility (average of all volatilities)
+        features['market_vol'] = features.filter(regex='vol_3d').mean(axis=1)
+        
+        return features.dropna()
+    
+    def train_models(self, training_data):
+        """Train multiple anomaly detection models"""
+        # Robust scaling (better for crypto data)
+        scaler = RobustScaler()
+        scaled_data = scaler.fit_transform(training_data)
+        self.scalers['main'] = scaler
+        
+        # 1. Isolation Forest (for point anomalies)
+        iso_forest = IsolationForest(
+            n_estimators=100,
+            contamination=0.01,
+            random_state=42
+        )
+        iso_forest.fit(scaled_data)
+        self.models['isolation_forest'] = iso_forest
+        
+        # 2. Mahalanobis Distance (Elliptic Envelope)
+        elliptic = EllipticEnvelope(
+            contamination=0.01,
+            random_state=42
+        )
+        elliptic.fit(scaled_data)
+        self.models['elliptic_envelope'] = elliptic
+        
+        # 3. Local Outlier Factor (for density-based anomalies)
+        lof = LOF(
+            n_neighbors=20,
+            contamination=0.01
+        )
+        lof.fit(scaled_data)
+        self.models['lof'] = lof
+        
+        # 4. Vector Autoregression (for multivariate time series)
+        var = VAR(training_data)
+        var_model = var.fit(maxlags=3)
+        self.models['var'] = var_model
+        
+        # Store training date
+        self.last_training_date = datetime.now()
+        
+    def detect_anomalies(self, current_data):
+        """Detect anomalies in new data points"""
+        # Prepare features for current data
+        current_features = self.prepare_features(current_data)
+        scaled_data = self.scalers['main'].transform(current_features)
+        
+        # Get predictions from all models
+        results = pd.DataFrame(index=current_features.index)
+        
+        # Isolation Forest
+        results['iso_forest'] = self.models['isolation_forest'].predict(scaled_data)
+        
+        # Elliptic Envelope
+        results['elliptic_env'] = self.models['elliptic_envelope'].predict(scaled_data)
+        
+        # LOF
+        lof_scores = self.models['lof'].decision_function(scaled_data)
+        results['lof_score'] = lof_scores
+        results['lof_anomaly'] = (lof_scores < np.percentile(lof_scores, 1)).astype(int)
+        
+        # VAR model residuals
+        var_pred = self.models['var'].forecast(current_features.values, 1)
+        residuals = current_features.iloc[-1] - var_pred[0]
+        mahalanobis_dist = np.sqrt(np.dot(np.dot(residuals, np.linalg.inv(self.models['var'].sigma_u)), residuals))
+        results['var_mahalanobis'] = mahalanobis_dist
+        results['var_anomaly'] = (mahalanobis_dist > np.percentile(results['var_mahalanobis'], 99)).astype(int)
+        
+        # Combined anomaly score
+        results['combined_score'] = (
+            (results['iso_forest'] == -1).astype(int) +
+            (results['elliptic_env'] == -1).astype(int) +
+            results['lof_anomaly'] +
+            results['var_anomaly']
+        )
+        
+        results['final_anomaly'] = (results['combined_score'] >= 2).astype(int)
+        
+        return results
+    
+    def visualize_results(self, prices, anomalies):
+        """Visualize prices with anomalies marked"""
+        plt.figure(figsize=(15, 10))
+        
+        # Plot prices
+        for i, ticker in enumerate(self.tickers, 1):
+            plt.subplot(len(self.tickers), 1, i)
+            plt.plot(prices[ticker], label=ticker)
+            
+            # Mark anomalies
+            
+            anomaly_points = prices.loc[anomalies[anomalies['final_anomaly'] == 1].index]
+            for point in anomaly_points:
+                plt.axvline(x=point, color='r', alpha=0.3)
+            
+            plt.title(f'{ticker} Price with Anomalies Marked')
+            plt.legend()
+        
+        plt.tight_layout()
+        plt.show()
+        
+        # Plot correlation changes if available
+        if len(self.correlation_matrices) > 0:
+            last_corr = self.correlation_matrices.iloc[-1]
+            plt.figure(figsize=(10, 8))
+            sns.heatmap(last_corr, annot=True, cmap='coolwarm', center=0)
+            plt.title('Latest Rolling Correlation Matrix')
+            plt.show()
+    
+    def run_pipeline(self):
+        """Complete training and detection pipeline"""
+        print("Fetching training data...")
+        training_data = self.fetch_data(days=30)
+        training_features = self.prepare_features(training_data)
+        
+        print("Training models...")
+        self.train_models(training_features)
+        
+        print("Fetching latest data for detection...")
+        # Get data since last training point (plus some overlap)
+        current_data = self.fetch_data(days=3)
+        current_features = self.prepare_features(current_data)
+        
+        print("Detecting anomalies...")
+        anomalies = self.detect_anomalies(current_data)
+        
+        print("\nAnomaly Detection Results:")
+        print(anomalies[anomalies['final_anomaly'] == 1])
+        
+        self.visualize_results(current_data, anomalies)
+        
+        return anomalies
+
+if __name__ == "__main__":
+    # Initialize with major cryptocurrencies
+    detector = CryptoAnomalyDetector(tickers=['solana', 'dogecoin', 'ethereum', 'ripple', 'tron'])
+    
+    # Run complete pipeline
+    results = detector.run_pipeline()

crypto_history.csv

@@ -0,0 +1,101 @@
+timestamp,solana_price,solana_zscore,dogecoin_price,dogecoin_zscore,ethereum_price,ethereum_zscore,ripple_price,ripple_zscore,tron_price,tron_zscore
+2025-07-14 07:57:16,161.21,0.21908207243226352,0.198196,0.10159514713314755,2973.99,0.7127376244348137,2.84,1.1533599229514373,0.303049,-0.7682792046936574
+2025-07-14 07:58:16,161.21,0.24418950539246545,0.198196,0.12619684937500822,2973.99,0.742533957946383,2.84,1.1533599229514373,0.303049,-0.7519692021728679
+2025-07-14 07:59:16,161.17,0.18526785056411987,0.198633,0.6368029667175993,2975.05,0.9133846236121144,2.84,1.153359922951394,0.302989,-1.1533861539991226
+2025-07-14 08:00:16,161.17,0.21189758248292706,0.198633,0.6720729049497202,2975.05,0.9466716241005834,2.84,1.1533599229514373,0.302989,-1.1313847326052524
+2025-07-14 08:01:17,161.17,0.24034097126058848,0.198627,0.7031358213060119,2974.44,0.8980574424411779,2.84,1.1533599229514373,0.302984,-1.1447536797193927
+2025-07-14 08:02:17,161.17,0.2705107593306227,0.198627,0.7441901303209899,2974.44,0.9346406491967124,2.84,1.1533599229514373,0.302984,-1.12292861046646
+2025-07-14 08:03:17,161.03,-0.03488795459319299,0.198575,0.7243131546264344,2973.09,0.7779241293934178,2.83,0.19513309067650544,0.302958,-1.2830429218474149
+2025-07-14 08:04:17,161.03,-0.008355691958567134,0.198575,0.7727142458409682,2973.09,0.8187567916865958,2.83,0.20629294562168055,0.302958,-1.2578809906584572
+2025-07-14 08:05:18,161.01,-0.03191871517360137,0.19858,0.8333153166976991,2972.77,0.8137482484888368,2.83,0.21763857204726025,0.302934,-1.4002902698973538
+2025-07-14 08:06:18,161.01,-0.0027585908705276114,0.19858,0.894032050132602,2972.77,0.861151743600939,2.83,0.22917966232011155,0.302934,-1.3722024673167892
+2025-07-14 08:07:18,161.0,0.000580110998294993,0.198573,0.9529749267809763,2972.72,0.9052111683293488,2.83,0.2409265502304303,0.302924,-1.4143854786011956
+2025-07-14 08:08:18,161.0,0.03466091802186828,0.198573,1.0324878625737508,2972.72,0.9631180004487954,2.83,0.25289026942956533,0.302924,-1.386107537426832
+2025-07-14 08:09:19,160.98,0.008504827883008964,0.198553,1.093449710093728,2972.42,0.9743335811157301,2.83,0.26508261848745135,0.302894,-1.5656441807309327
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duplicati_logs.dat

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+    "ParsedResult": "Success",
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+  "Messages": [
+    "2025-06-07 02:00:35 +08 - [Information-Duplicati.Library.Main.Controller-StartingOperation]: The operation Backup has started",
+    "2025-06-07 02:00:35 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Started:  ()",
+    "2025-06-07 02:00:36 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Completed:  (104 bytes)",
+    "2025-06-07 02:01:03 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-bdaf870a1768f469ca8eb61d423b9bcd5.dblock.zip (49.183 MB)",
+    "2025-06-07 02:01:04 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-bdaf870a1768f469ca8eb61d423b9bcd5.dblock.zip (49.183 MB)",
+    "2025-06-07 02:01:04 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-i4f7610cdf30c4447a094496d15febb8c.dindex.zip (21.729 KB)",
+    "2025-06-07 02:01:04 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-i4f7610cdf30c4447a094496d15febb8c.dindex.zip (21.729 KB)",
+    "2025-06-07 02:01:12 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-b2a416ec195264354951a196eade5f9d1.dblock.zip (49.049 MB)",
+    "2025-06-07 02:01:12 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-b2a416ec195264354951a196eade5f9d1.dblock.zip (49.049 MB)",
+    "2025-06-07 02:01:12 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-i14b4cbdea33a496a91de790e084f07b2.dindex.zip (64.781 KB)",
+    "2025-06-07 02:01:12 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-i14b4cbdea33a496a91de790e084f07b2.dindex.zip (64.781 KB)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-b6760ebebea4d4715b4c16a22ef089c0d.dblock.zip (6.139 MB)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-b6760ebebea4d4715b4c16a22ef089c0d.dblock.zip (6.139 MB)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-ibd51d1087b8b4b49b06abac02b68575c.dindex.zip (7.596 KB)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-ibd51d1087b8b4b49b06abac02b68575c.dindex.zip (7.596 KB)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-20250606T180035Z.dlist.zip (31.218 KB)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-20250606T180035Z.dlist.zip (31.218 KB)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Started:  ()",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Completed:  (111 bytes)",
+    "2025-06-07 02:01:17 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Started: duplicati-20250606T180035Z.dlist.zip (31.218 KB)"
+  ],
+  "Warnings": [],
+  "Errors": [],
+  "BackendStatistics": {
+    "RemoteCalls": 12,
+    "BytesUploaded": 109568770,
+    "BytesDownloaded": 51812289,
+    "FilesUploaded": 7,
+    "FilesDownloaded": 3,
+    "FilesDeleted": 0,
+    "FoldersCreated": 0,
+    "RetryAttempts": 0,
+    "UnknownFileSize": 0,
+    "UnknownFileCount": 0,
+    "KnownFileCount": 111,
+    "KnownFileSize": 2344188455,
+    "LastBackupDate": "2025-06-07T02:00:35+08:00",
+    "BackupListCount": 11,
+    "TotalQuotaSpace": 1932937191424,
+    "FreeQuotaSpace": 541474566144,
+    "AssignedQuotaSpace": -1,
+    "ReportedQuotaError": false,
+    "ReportedQuotaWarning": false,
+    "MainOperation": "Backup",
+    "ParsedResult": "Success",
+    "Interrupted": false,
+    "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+    "EndTime": "0001-01-01T00:00:00",
+    "BeginTime": "2025-06-06T18:00:35.070372Z",
+    "Duration": "00:00:00",
+    "MessagesActualLength": 0,
+    "WarningsActualLength": 0,
+    "ErrorsActualLength": 0,
+    "Messages": null,
+    "Warnings": null,
+    "Errors": null
+  }
+}
+
+            {
+  "DeletedFiles": 0,
+  "DeletedFolders": 0,
+  "ModifiedFiles": 19,
+  "ExaminedFiles": 571,
+  "OpenedFiles": 19,
+  "AddedFiles": 0,
+  "SizeOfModifiedFiles": 193046004,
+  "SizeOfAddedFiles": 0,
+  "SizeOfExaminedFiles": 2995513107,
+  "SizeOfOpenedFiles": 193046004,
+  "NotProcessedFiles": 0,
+  "AddedFolders": 0,
+  "TooLargeFiles": 0,
+  "FilesWithError": 0,
+  "ModifiedFolders": 0,
+  "ModifiedSymlinks": 0,
+  "AddedSymlinks": 0,
+  "DeletedSymlinks": 0,
+  "PartialBackup": false,
+  "Dryrun": false,
+  "MainOperation": "Backup",
+  "CompactResults": null,
+  "VacuumResults": null,
+  "DeleteResults": null,
+  "RepairResults": null,
+  "TestResults": {
+    "MainOperation": "Test",
+    "VerificationsActualLength": 3,
+    "Verifications": [
+      {
+        "Key": "duplicati-20250601T132303Z.dlist.zip",
+        "Value": []
+      },
+      {
+        "Key": "duplicati-i0dbb1324b6b1445a95ef6b23606a6d75.dindex.zip",
+        "Value": []
+      },
+      {
+        "Key": "duplicati-be79958a0daeb496a9e3e6fc2df5ed07d.dblock.zip",
+        "Value": []
+      }
+    ],
+    "ParsedResult": "Success",
+    "Interrupted": false,
+    "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+    "EndTime": "2025-06-01T13:23:06.9815733Z",
+    "BeginTime": "2025-06-01T13:23:06.5046701Z",
+    "Duration": "00:00:00.4769032",
+    "MessagesActualLength": 0,
+    "WarningsActualLength": 0,
+    "ErrorsActualLength": 0,
+    "Messages": null,
+    "Warnings": null,
+    "Errors": null,
+    "BackendStatistics": {
+      "RemoteCalls": 8,
+      "BytesUploaded": 1976262,
+      "BytesDownloaded": 51647357,
+      "FilesUploaded": 3,
+      "FilesDownloaded": 3,
+      "FilesDeleted": 0,
+      "FoldersCreated": 0,
+      "RetryAttempts": 0,
+      "UnknownFileSize": 0,
+      "UnknownFileCount": 0,
+      "KnownFileCount": 104,
+      "KnownFileSize": 2234619685,
+      "LastBackupDate": "2025-06-01T21:23:03+08:00",
+      "BackupListCount": 10,
+      "TotalQuotaSpace": 1932937191424,
+      "FreeQuotaSpace": 549558972416,
+      "AssignedQuotaSpace": -1,
+      "ReportedQuotaError": false,
+      "ReportedQuotaWarning": false,
+      "MainOperation": "Backup",
+      "ParsedResult": "Success",
+      "Interrupted": false,
+      "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+      "EndTime": "0001-01-01T00:00:00",
+      "BeginTime": "2025-06-01T13:23:03.6638236Z",
+      "Duration": "00:00:00",
+      "MessagesActualLength": 0,
+      "WarningsActualLength": 0,
+      "ErrorsActualLength": 0,
+      "Messages": null,
+      "Warnings": null,
+      "Errors": null
+    }
+  },
+  "ParsedResult": "Success",
+  "Interrupted": false,
+  "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+  "EndTime": "2025-06-01T13:23:07.0592437Z",
+  "BeginTime": "2025-06-01T13:23:03.6635855Z",
+  "Duration": "00:00:03.3956582",
+  "MessagesActualLength": 18,
+  "WarningsActualLength": 0,
+  "ErrorsActualLength": 0,
+  "Messages": [
+    "2025-06-01 21:23:03 +08 - [Information-Duplicati.Library.Main.Controller-StartingOperation]: The operation Backup has started",
+    "2025-06-01 21:23:03 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Started:  ()",
+    "2025-06-01 21:23:03 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Completed:  (101 bytes)",
+    "2025-06-01 21:23:05 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-b25b3c6258b8d461aabcccd9752ec8fbb.dblock.zip (1.845 MB)",
+    "2025-06-01 21:23:05 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-b25b3c6258b8d461aabcccd9752ec8fbb.dblock.zip (1.845 MB)",
+    "2025-06-01 21:23:05 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-ia7ad0ed4d81945fd8b7ce81e340ba64a.dindex.zip (9.375 KB)",
+    "2025-06-01 21:23:05 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-ia7ad0ed4d81945fd8b7ce81e340ba64a.dindex.zip (9.375 KB)",
+    "2025-06-01 21:23:05 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-20250601T132303Z.dlist.zip (31.347 KB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-20250601T132303Z.dlist.zip (31.347 KB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Started:  ()",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Completed:  (104 bytes)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Started: duplicati-20250601T132303Z.dlist.zip (31.347 KB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Completed: duplicati-20250601T132303Z.dlist.zip (31.347 KB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Started: duplicati-i0dbb1324b6b1445a95ef6b23606a6d75.dindex.zip (57.191 KB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Completed: duplicati-i0dbb1324b6b1445a95ef6b23606a6d75.dindex.zip (57.191 KB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Started: duplicati-be79958a0daeb496a9e3e6fc2df5ed07d.dblock.zip (49.168 MB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Completed: duplicati-be79958a0daeb496a9e3e6fc2df5ed07d.dblock.zip (49.168 MB)",
+    "2025-06-01 21:23:06 +08 - [Information-Duplicati.Library.Main.Operation.TestHandler-Test results]: Successfully verified 3 remote files"
+  ],
+  "Warnings": [],
+  "Errors": [],
+  "BackendStatistics": {
+    "RemoteCalls": 8,
+    "BytesUploaded": 1976262,
+    "BytesDownloaded": 51647357,
+    "FilesUploaded": 3,
+    "FilesDownloaded": 3,
+    "FilesDeleted": 0,
+    "FoldersCreated": 0,
+    "RetryAttempts": 0,
+    "UnknownFileSize": 0,
+    "UnknownFileCount": 0,
+    "KnownFileCount": 104,
+    "KnownFileSize": 2234619685,
+    "LastBackupDate": "2025-06-01T21:23:03+08:00",
+    "BackupListCount": 10,
+    "TotalQuotaSpace": 1932937191424,
+    "FreeQuotaSpace": 549558972416,
+    "AssignedQuotaSpace": -1,
+    "ReportedQuotaError": false,
+    "ReportedQuotaWarning": false,
+    "MainOperation": "Backup",
+    "ParsedResult": "Success",
+    "Interrupted": false,
+    "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+    "EndTime": "0001-01-01T00:00:00",
+    "BeginTime": "2025-06-01T13:23:03.6638236Z",
+    "Duration": "00:00:00",
+    "MessagesActualLength": 0,
+    "WarningsActualLength": 0,
+    "ErrorsActualLength": 0,
+    "Messages": null,
+    "Warnings": null,
+    "Errors": null
+  }
+}
+                
+            {
+  "DeletedFiles": 2,
+  "DeletedFolders": 2,
+  "ModifiedFiles": 3,
+  "ExaminedFiles": 89012,
+  "OpenedFiles": 4,
+  "AddedFiles": 1,
+  "SizeOfModifiedFiles": 38505459,
+  "SizeOfAddedFiles": 1274,
+  "SizeOfExaminedFiles": 7567305219,
+  "SizeOfOpenedFiles": 38506733,
+  "NotProcessedFiles": 0,
+  "AddedFolders": 0,
+  "TooLargeFiles": 0,
+  "FilesWithError": 0,
+  "ModifiedFolders": 0,
+  "ModifiedSymlinks": 0,
+  "AddedSymlinks": 0,
+  "DeletedSymlinks": 0,
+  "PartialBackup": false,
+  "Dryrun": false,
+  "MainOperation": "Backup",
+  "CompactResults": {
+    "DeletedFileCount": 0,
+    "DownloadedFileCount": 0,
+    "UploadedFileCount": 0,
+    "DeletedFileSize": 0,
+    "DownloadedFileSize": 0,
+    "UploadedFileSize": 0,
+    "Dryrun": false,
+    "VacuumResults": null,
+    "MainOperation": "Compact",
+    "ParsedResult": "Success",
+    "Interrupted": false,
+    "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+    "EndTime": "2025-06-06T18:22:30.7812878Z",
+    "BeginTime": "2025-06-06T18:22:12.6301317Z",
+    "Duration": "00:00:18.1511561",
+    "MessagesActualLength": 0,
+    "WarningsActualLength": 0,
+    "ErrorsActualLength": 0,
+    "Messages": null,
+    "Warnings": null,
+    "Errors": null,
+    "BackendStatistics": {
+      "RemoteCalls": 8,
+      "BytesUploaded": 20594437,
+      "BytesDownloaded": 71631301,
+      "FilesUploaded": 3,
+      "FilesDownloaded": 3,
+      "FilesDeleted": 0,
+      "FoldersCreated": 0,
+      "RetryAttempts": 0,
+      "UnknownFileSize": 0,
+      "UnknownFileCount": 0,
+      "KnownFileCount": 226,
+      "KnownFileSize": 5475247460,
+      "LastBackupDate": "2025-06-07T02:01:27+08:00",
+      "BackupListCount": 8,
+      "TotalQuotaSpace": 1932937191424,
+      "FreeQuotaSpace": 541453942784,
+      "AssignedQuotaSpace": -1,
+      "ReportedQuotaError": false,
+      "ReportedQuotaWarning": false,
+      "MainOperation": "Backup",
+      "ParsedResult": "Success",
+      "Interrupted": false,
+      "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+      "EndTime": "0001-01-01T00:00:00",
+      "BeginTime": "2025-06-06T18:01:27.3427176Z",
+      "Duration": "00:00:00",
+      "MessagesActualLength": 0,
+      "WarningsActualLength": 0,
+      "ErrorsActualLength": 0,
+      "Messages": null,
+      "Warnings": null,
+      "Errors": null
+    }
+  },
+  "VacuumResults": null,
+  "DeleteResults": null,
+  "RepairResults": null,
+  "TestResults": {
+    "MainOperation": "Test",
+    "VerificationsActualLength": 3,
+    "Verifications": [
+      {
+        "Key": "duplicati-20250606T180127Z.dlist.zip",
+        "Value": []
+      },
+      {
+        "Key": "duplicati-iace1fd90f9254afeb2456210423381a9.dindex.zip",
+        "Value": []
+      },
+      {
+        "Key": "duplicati-b9fd4462dccd247d487a4c7ca9a9ff9e1.dblock.zip",
+        "Value": []
+      }
+    ],
+    "ParsedResult": "Success",
+    "Interrupted": false,
+    "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+    "EndTime": "2025-06-06T18:22:32.2138061Z",
+    "BeginTime": "2025-06-06T18:22:30.8777049Z",
+    "Duration": "00:00:01.3361012",
+    "MessagesActualLength": 0,
+    "WarningsActualLength": 0,
+    "ErrorsActualLength": 0,
+    "Messages": null,
+    "Warnings": null,
+    "Errors": null,
+    "BackendStatistics": {
+      "RemoteCalls": 8,
+      "BytesUploaded": 20594437,
+      "BytesDownloaded": 71631301,
+      "FilesUploaded": 3,
+      "FilesDownloaded": 3,
+      "FilesDeleted": 0,
+      "FoldersCreated": 0,
+      "RetryAttempts": 0,
+      "UnknownFileSize": 0,
+      "UnknownFileCount": 0,
+      "KnownFileCount": 226,
+      "KnownFileSize": 5475247460,
+      "LastBackupDate": "2025-06-07T02:01:27+08:00",
+      "BackupListCount": 8,
+      "TotalQuotaSpace": 1932937191424,
+      "FreeQuotaSpace": 541453942784,
+      "AssignedQuotaSpace": -1,
+      "ReportedQuotaError": false,
+      "ReportedQuotaWarning": false,
+      "MainOperation": "Backup",
+      "ParsedResult": "Success",
+      "Interrupted": false,
+      "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+      "EndTime": "0001-01-01T00:00:00",
+      "BeginTime": "2025-06-06T18:01:27.3427176Z",
+      "Duration": "00:00:00",
+      "MessagesActualLength": 0,
+      "WarningsActualLength": 0,
+      "ErrorsActualLength": 0,
+      "Messages": null,
+      "Warnings": null,
+      "Errors": null
+    }
+  },
+  "ParsedResult": "Success",
+  "Interrupted": false,
+  "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+  "EndTime": "2025-06-06T18:22:32.2833489Z",
+  "BeginTime": "2025-06-06T18:01:27.3424975Z",
+  "Duration": "00:21:04.9408514",
+  "MessagesActualLength": 19,
+  "WarningsActualLength": 0,
+  "ErrorsActualLength": 0,
+  "Messages": [
+    "2025-06-07 02:01:27 +08 - [Information-Duplicati.Library.Main.Controller-StartingOperation]: The operation Backup has started",
+    "2025-06-07 02:02:33 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Started:  ()",
+    "2025-06-07 02:02:34 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Completed:  (223 bytes)",
+    "2025-06-07 02:21:52 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-bd28a40a8f4ad44e09b12f16c78f16b1c.dblock.zip (1.039 MB)",
+    "2025-06-07 02:22:08 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-bd28a40a8f4ad44e09b12f16c78f16b1c.dblock.zip (1.039 MB)",
+    "2025-06-07 02:22:08 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-i1db5e187d0f6448283e575bed2b0ada0.dindex.zip (2.666 KB)",
+    "2025-06-07 02:22:08 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-i1db5e187d0f6448283e575bed2b0ada0.dindex.zip (2.666 KB)",
+    "2025-06-07 02:22:12 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Started: duplicati-20250606T180127Z.dlist.zip (18.599 MB)",
+    "2025-06-07 02:22:12 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Put - Completed: duplicati-20250606T180127Z.dlist.zip (18.599 MB)",
+    "2025-06-07 02:22:30 +08 - [Information-Duplicati.Library.Main.Database.LocalDeleteDatabase-CompactReason]: Compacting not required",
+    "2025-06-07 02:22:30 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Started:  ()",
+    "2025-06-07 02:22:30 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: List - Completed:  (226 bytes)",
+    "2025-06-07 02:22:30 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Started: duplicati-20250606T180127Z.dlist.zip (18.599 MB)",
+    "2025-06-07 02:22:31 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Completed: duplicati-20250606T180127Z.dlist.zip (18.599 MB)",
+    "2025-06-07 02:22:31 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Started: duplicati-iace1fd90f9254afeb2456210423381a9.dindex.zip (21.259 KB)",
+    "2025-06-07 02:22:31 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Completed: duplicati-iace1fd90f9254afeb2456210423381a9.dindex.zip (21.259 KB)",
+    "2025-06-07 02:22:31 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Started: duplicati-b9fd4462dccd247d487a4c7ca9a9ff9e1.dblock.zip (49.693 MB)",
+    "2025-06-07 02:22:32 +08 - [Information-Duplicati.Library.Main.BasicResults-BackendEvent]: Backend event: Get - Completed: duplicati-b9fd4462dccd247d487a4c7ca9a9ff9e1.dblock.zip (49.693 MB)",
+    "2025-06-07 02:22:32 +08 - [Information-Duplicati.Library.Main.Operation.TestHandler-Test results]: Successfully verified 3 remote files"
+  ],
+  "Warnings": [],
+  "Errors": [],
+  "BackendStatistics": {
+    "RemoteCalls": 8,
+    "BytesUploaded": 20594437,
+    "BytesDownloaded": 71631301,
+    "FilesUploaded": 3,
+    "FilesDownloaded": 3,
+    "FilesDeleted": 0,
+    "FoldersCreated": 0,
+    "RetryAttempts": 0,
+    "UnknownFileSize": 0,
+    "UnknownFileCount": 0,
+    "KnownFileCount": 226,
+    "KnownFileSize": 5475247460,
+    "LastBackupDate": "2025-06-07T02:01:27+08:00",
+    "BackupListCount": 8,
+    "TotalQuotaSpace": 1932937191424,
+    "FreeQuotaSpace": 541453942784,
+    "AssignedQuotaSpace": -1,
+    "ReportedQuotaError": false,
+    "ReportedQuotaWarning": false,
+    "MainOperation": "Backup",
+    "ParsedResult": "Success",
+    "Interrupted": false,
+    "Version": "2.1.0.4 (2.1.0.4_stable_2025-01-31)",
+    "EndTime": "0001-01-01T00:00:00",
+    "BeginTime": "2025-06-06T18:01:27.3427176Z",
+    "Duration": "00:00:00",
+    "MessagesActualLength": 0,
+    "WarningsActualLength": 0,
+    "ErrorsActualLength": 0,
+    "Messages": null,
+    "Warnings": null,
+    "Errors": null
+  }
+}
+        

solana_price_history.csv

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strange_crypto2.py

@@ -0,0 +1,231 @@
+import requests
+import pandas as pd
+import numpy as np
+import time
+import schedule
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from datetime import datetime
+import threading
+import os
+from flask import Flask, render_template_string, jsonify
+
+# Configuration
+CRYPTO_LIST = ['solana', 'dogecoin', 'ethereum', 'ripple', 'tron']  # CoinGecko IDs
+CRYPTO_LABELS = {
+    'solana': 'SOL',
+    'dogecoin': 'DOGE',
+    'ethereum': 'ETH',
+    'ripple': 'XRP',
+    'tron': 'TRX'
+}
+CURRENCY = 'usd'
+API_URL = 'https://api.coingecko.com/api/v3/simple/price'
+CHECK_INTERVAL_SECONDS = 60  # Check every 60 seconds
+Z_SCORE_THRESHOLD = 3.0
+HISTORY_WINDOW = 100
+
+EMAIL_FROM = 'vortify-lc@algometic.com'
+EMAIL_TO = 'larry1chan@qq.com'
+EMAIL_PASSWORD = 'g33kPoppy!'
+SMTP_SERVER = 'hwsmtp.exmail.qq.com'
+SMTP_PORT = 465
+
+DATA_FILE = "crypto_history.csv"
+
+# In-memory storage for historical prices and z-scores per crypto
+price_history = {crypto: [] for crypto in CRYPTO_LIST}
+zscore_history = {crypto: [] for crypto in CRYPTO_LIST}
+timestamp_history = []
+
+def fetch_prices():
+    """Fetch current prices for all cryptos from CoinGecko API."""
+    try:
+        ids = ','.join(CRYPTO_LIST)
+        params = {'ids': ids, 'vs_currencies': CURRENCY}
+        response = requests.get(API_URL, params=params)
+        response.raise_for_status()
+        data = response.json()
+        return {crypto: data.get(crypto, {}).get(CURRENCY) for crypto in CRYPTO_LIST}
+    except Exception as e:
+        print(f"Error fetching prices: {e}")
+        return {}
+
+def detect_anomaly(crypto, current_price):
+    """Detect if the current price is an anomaly based on Z-score."""
+    history = price_history[crypto]
+    if len(history) < 2:
+        return False, 0.0
+    df = pd.DataFrame(history, columns=['price'])
+    mean = df['price'].mean()
+    std_dev = df['price'].std()
+    if std_dev == 0:
+        return False, 0.0
+    z_score = (current_price - mean) / std_dev
+    is_anomaly = abs(z_score) > Z_SCORE_THRESHOLD
+    return is_anomaly, z_score
+
+def send_email_alert(crypto, current_price, z_score):
+    """Send email alert for anomaly."""
+    symbol = CRYPTO_LABELS.get(crypto, crypto.upper())
+    subject = f"Anomaly Detected in {symbol} Price!"
+    body = f"""
+    Alert Time: {datetime.now()}
+    Crypto: {symbol}
+    Current Price: ${current_price:.4f}
+    Z-Score: {z_score:.2f} (Threshold: {Z_SCORE_THRESHOLD})
+    This indicates a potential anomalous movement (spike or drop).
+    """
+    msg = MIMEMultipart()
+    msg['From'] = EMAIL_FROM
+    msg['To'] = EMAIL_TO
+    msg['Subject'] = subject
+    msg.attach(MIMEText(body, 'plain'))
+    try:
+        server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT)
+        server.starttls()
+        server.login(EMAIL_FROM, EMAIL_PASSWORD)
+        server.sendmail(EMAIL_FROM, EMAIL_TO, msg.as_string())
+        server.quit()
+        print(f"Email alert sent for {symbol}.")
+    except Exception as e:
+        print(f"Error sending email for {symbol}: {e}")
+
+def save_history():
+    """Save the price and z-score history to a CSV file."""
+    if not timestamp_history:
+        return
+    rows = []
+    for idx, ts in enumerate(timestamp_history):
+        row = {'timestamp': ts}
+        for crypto in CRYPTO_LIST:
+            row[f'{crypto}_price'] = price_history[crypto][idx] if idx < len(price_history[crypto]) else ''
+            row[f'{crypto}_zscore'] = zscore_history[crypto][idx] if idx < len(zscore_history[crypto]) else ''
+        rows.append(row)
+    df = pd.DataFrame(rows)
+    df.to_csv(DATA_FILE, index=False)
+
+def monitor():
+    """Main monitoring function: Fetch prices, check for anomaly, alert if needed, and save history."""
+    current_prices = fetch_prices()
+    now = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    timestamp_history.append(now)
+    for crypto, price in current_prices.items():
+        if price is None:
+            price_history[crypto].append(np.nan)
+            zscore_history[crypto].append(np.nan)
+            continue
+        # Append to history (maintain fixed window size)
+        price_history[crypto].append(price)
+        if len(price_history[crypto]) > HISTORY_WINDOW:
+            price_history[crypto].pop(0)
+        is_anomaly, z_score = detect_anomaly(crypto, price)
+        zscore_history[crypto].append(z_score)
+        if len(zscore_history[crypto]) > HISTORY_WINDOW:
+            zscore_history[crypto].pop(0)
+        symbol = CRYPTO_LABELS.get(crypto, crypto.upper())
+        print(f"{now} - {symbol} Price: ${price:.4f} | Z-Score: {z_score:.2f}")
+        if is_anomaly:
+            print(f"ANOMALY DETECTED for {symbol}! Z-Score: {z_score:.2f}")
+            send_email_alert(crypto, price, z_score)
+    # Keep timestamp_history in sync
+    if len(timestamp_history) > HISTORY_WINDOW:
+        timestamp_history.pop(0)
+    save_history()
+
+# --- Web server for visualization ---
+app = Flask(__name__)
+
+HTML_TEMPLATE = """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>Crypto Z-Score & Price Monitor</title>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+</head>
+<body>
+    <h2>Crypto Z-Score & Price Monitor</h2>
+    <div id="charts"></div>
+    <script>
+        async function fetchData() {
+            const resp = await fetch('/data');
+            return await resp.json();
+        }
+        function renderCharts(data) {
+            const container = document.getElementById('charts');
+            container.innerHTML = '';
+            for (const crypto of data.cryptos) {
+                const label = data.labels[crypto];
+                const canvasPrice = document.createElement('canvas');
+                const canvasZ = document.createElement('canvas');
+                container.appendChild(document.createElement('hr'));
+                container.appendChild(document.createTextNode(label + " Price"));
+                container.appendChild(canvasPrice);
+                container.appendChild(document.createTextNode(label + " Z-Score"));
+                container.appendChild(canvasZ);
+
+                new Chart(canvasPrice, {
+                    type: 'line',
+                    data: {
+                        labels: data.timestamps,
+                        datasets: [{
+                            label: label + ' Price',
+                            data: data.prices[crypto],
+                            borderColor: 'blue',
+                            fill: false
+                        }]
+                    }
+                });
+                new Chart(canvasZ, {
+                    type: 'line',
+                    data: {
+                        labels: data.timestamps,
+                        datasets: [{
+                            label: label + ' Z-Score',
+                            data: data.zscores[crypto],
+                            borderColor: 'red',
+                            fill: false
+                        }]
+                    }
+                });
+            }
+        }
+        fetchData().then(renderCharts);
+        setInterval(() => fetchData().then(renderCharts), 60000);
+    </script>
+</body>
+</html>
+"""
+
+@app.route('/')
+def index():
+    return render_template_string(HTML_TEMPLATE)
+
+@app.route('/data')
+def data():
+    # Prepare data for chart
+    return jsonify({
+        'cryptos': CRYPTO_LIST,
+        'labels': CRYPTO_LABELS,
+        'timestamps': timestamp_history,
+        'prices': price_history,
+        'zscores': zscore_history
+    })
+
+def start_flask():
+    app.run(host='0.0.0.0', port=5000, debug=False, use_reloader=False)
+
+# --- Main ---
+def main():
+    # Start web server in a separate thread
+    threading.Thread(target=start_flask, daemon=True).start()
+    # Schedule the monitor to run every CHECK_INTERVAL_SECONDS
+    schedule.every(CHECK_INTERVAL_SECONDS).seconds.do(monitor)
+    print("Starting multi-crypto price anomaly monitor and web server on http://localhost:5000 ...")
+    while True:
+        schedule.run_pending()
+        time.sleep(1)
+
+if __name__ == "__main__":
+    main()

strange_cryptos.py

@@ -0,0 +1,114 @@
+### Python Code
+
+import requests
+import pandas as pd
+import numpy as np
+import time
+import schedule
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from datetime import datetime
+
+# Configuration
+CRYPTO_LIST = ['solana', 'dogecoin', 'ethereum', 'ripple', 'tron']  # CoinGecko IDs
+CRYPTO_LABELS = {
+    'solana': 'SOL',
+    'dogecoin': 'DOGE',
+    'ethereum': 'ETH',
+    'ripple': 'XRP',
+    'tron': 'TRX'
+}
+CURRENCY = 'usd'
+API_URL = 'https://api.coingecko.com/api/v3/simple/price'
+CHECK_INTERVAL_SECONDS = 60  # Check every 60 seconds
+Z_SCORE_THRESHOLD = 3.0
+HISTORY_WINDOW = 100
+
+EMAIL_FROM = 'vortify-lc@algometic.com'
+EMAIL_TO = 'larry1chan@qq.com'
+EMAIL_PASSWORD = 'g33kPoppy!'
+SMTP_SERVER = 'hwsmtp.exmail.qq.com'
+SMTP_PORT = 465
+
+# In-memory storage for historical prices per crypto
+price_history = {crypto: [] for crypto in CRYPTO_LIST}
+
+def fetch_prices():
+    """Fetch current prices for all cryptos from CoinGecko API."""
+    try:
+        ids = ','.join(CRYPTO_LIST)
+        params = {'ids': ids, 'vs_currencies': CURRENCY}
+        response = requests.get(API_URL, params=params)
+        response.raise_for_status()
+        data = response.json()
+        return {crypto: data.get(crypto, {}).get(CURRENCY) for crypto in CRYPTO_LIST}
+    except Exception as e:
+        print(f"Error fetching prices: {e}")
+        return {}
+
+def detect_anomaly(crypto, current_price):
+    """Detect if the current price is an anomaly based on Z-score."""
+    history = price_history[crypto]
+    if len(history) < 2:
+        return False, 0.0
+    df = pd.DataFrame(history, columns=['price'])
+    mean = df['price'].mean()
+    std_dev = df['price'].std()
+    if std_dev == 0:
+        return False, 0.0
+    z_score = (current_price - mean) / std_dev
+    is_anomaly = abs(z_score) > Z_SCORE_THRESHOLD
+    return is_anomaly, z_score
+
+def send_email_alert(crypto, current_price, z_score):
+    """Send email alert for anomaly."""
+    symbol = CRYPTO_LABELS.get(crypto, crypto.upper())
+    subject = f"Anomaly Detected in {symbol} Price!"
+    body = f"""
+    Alert Time: {datetime.now()}
+    Crypto: {symbol}
+    Current Price: ${current_price:.4f}
+    Z-Score: {z_score:.2f} (Threshold: {Z_SCORE_THRESHOLD})
+    This indicates a potential anomalous movement (spike or drop).
+    """
+    msg = MIMEMultipart()
+    msg['From'] = EMAIL_FROM
+    msg['To'] = EMAIL_TO
+    msg['Subject'] = subject
+    msg.attach(MIMEText(body, 'plain'))
+    try:
+        server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT)
+        server.starttls()
+        server.login(EMAIL_FROM, EMAIL_PASSWORD)
+        server.sendmail(EMAIL_FROM, EMAIL_TO, msg.as_string())
+        server.quit()
+        print(f"Email alert sent for {symbol}.")
+    except Exception as e:
+        e.printstacktrace()
+        print(f"Error sending email for {symbol}: {e}")
+
+def monitor():
+    """Main monitoring function: Fetch prices, check for anomaly, alert if needed."""
+    current_prices = fetch_prices()
+    for crypto, price in current_prices.items():
+        if price is None:
+            continue
+        # Append to history (maintain fixed window size)
+        price_history[crypto].append(price)
+        if len(price_history[crypto]) > HISTORY_WINDOW:
+            price_history[crypto].pop(0)
+        is_anomaly, z_score = detect_anomaly(crypto, price)
+        symbol = CRYPTO_LABELS.get(crypto, crypto.upper())
+        print(f"{datetime.now()} - {symbol} Price: ${price:.4f} | Z-Score: {z_score:.2f}")
+        if is_anomaly:
+            print(f"ANOMALY DETECTED for {symbol}! Z-Score: {z_score:.2f}")
+            send_email_alert(crypto, price, z_score)
+
+# Schedule the monitor to run every CHECK_INTERVAL_SECONDS
+schedule.every(CHECK_INTERVAL_SECONDS).seconds.do(monitor)
+
+print("Starting multi-crypto price anomaly monitor...")
+while True:
+    schedule.run_pending()
+    time.sleep(1)

strange_doge.py

@@ -0,0 +1,112 @@
+### Python Code
+
+import requests
+import pandas as pd
+import numpy as np
+import time
+import schedule
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from datetime import datetime
+
+# Configuration
+CRYPTO = 'dogecoin'  # CoinGecko ID for DOGE
+CURRENCY = 'usd'     # Base currency
+API_URL = f'https://api.coingecko.com/api/v3/simple/price?ids={CRYPTO}&vs_currencies={CURRENCY}'
+CHECK_INTERVAL_SECONDS = 60  # Check every 60 seconds
+Z_SCORE_THRESHOLD = 3.0  # Flag as anomaly if |Z-score| > 3
+HISTORY_WINDOW = 100  # Keep last 100 prices for mean/std calculation
+
+# Email configuration (replace with your details)
+EMAIL_FROM = 'vortify-lc@algometic.com'
+EMAIL_TO = 'larry1chan@qq.com'
+EMAIL_PASSWORD = 'g33kPoppy!'
+SMTP_SERVER = 'hwsmtp.exmail.qq.com'
+SMTP_PORT = 465
+
+# In-memory storage for historical prices
+price_history = []
+
+def fetch_price():
+    """Fetch current price from CoinGecko API."""
+    try:
+        response = requests.get(API_URL)
+        response.raise_for_status()
+        data = response.json()
+        price = data[CRYPTO][CURRENCY]
+        return price
+    except Exception as e:
+        print(f"Error fetching price: {e}")
+        return None
+
+def detect_anomaly(current_price):
+    """Detect if the current price is an anomaly based on Z-score."""
+    if len(price_history) < 2:  # Need at least 2 points for std dev
+        return False, 0.0
+    
+    # Calculate mean and std dev from history
+    df = pd.DataFrame(price_history, columns=['price'])
+    mean = df['price'].mean()
+    std_dev = df['price'].std()
+    
+    if std_dev == 0:  # Avoid division by zero
+        return False, 0.0
+    
+    z_score = (current_price - mean) / std_dev
+    is_anomaly = abs(z_score) > Z_SCORE_THRESHOLD
+    return is_anomaly, z_score
+
+def send_email_alert(current_price, z_score):
+    """Send email alert for anomaly."""
+    subject = f"Anomaly Detected in {CRYPTO.upper()} Price!"
+    body = f"""
+    Alert Time: {datetime.now()}
+    Current Price: ${current_price:.4f}
+    Z-Score: {z_score:.2f} (Threshold: {Z_SCORE_THRESHOLD})
+    This indicates a potential anomalous movement (spike or drop).
+    """
+    
+    msg = MIMEMultipart()
+    msg['From'] = EMAIL_FROM
+    msg['To'] = EMAIL_TO
+    msg['Subject'] = subject
+    msg.attach(MIMEText(body, 'plain'))
+    
+    try:
+        server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT)
+        server.starttls()
+        server.login(EMAIL_FROM, EMAIL_PASSWORD)
+        server.sendmail(EMAIL_FROM, EMAIL_TO, msg.as_string())
+        server.quit()
+        print("Email alert sent successfully.")
+    except Exception as e:
+        print(f"Error sending email: {e}")
+
+def monitor():
+    """Main monitoring function: Fetch price, check for anomaly, alert if needed."""
+    current_price = fetch_price()
+    if current_price is None:
+        return
+    
+    # Append to history (maintain fixed window size)
+    price_history.append(current_price)
+    if len(price_history) > HISTORY_WINDOW:
+        price_history.pop(0)
+    
+    is_anomaly, z_score = detect_anomaly(current_price)
+    
+    print(f"{datetime.now()} - Current {CRYPTO.upper()} Price: ${current_price:.4f} | Z-Score: {z_score:.2f}")
+    
+    if is_anomaly:
+        print(f"ANOMALY DETECTED! Z-Score: {z_score:.2f}")
+        send_email_alert(current_price, z_score)
+
+# Schedule the monitor to run every CHECK_INTERVAL_SECONDS
+schedule.every(CHECK_INTERVAL_SECONDS).seconds.do(monitor)
+
+# Run the monitoring loop
+print(f"Starting {CRYPTO.upper()} price anomaly monitor...")
+while True:
+    schedule.run_pending()
+    time.sleep(1)  # Sleep to avoid high CPU usage

strange_doge_rf.py

@@ -0,0 +1,179 @@
+import requests
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import IsolationForest
+import time
+import schedule
+import smtplib
+from email.mime.text import MIMEText
+from email.mime.multipart import MIMEMultipart
+from datetime import datetime
+import os
+
+# Configuration
+CRYPTO = 'dogecoin'  # CoinGecko ID for DOGE
+CURRENCY = 'usd'     # Base currency
+CURRENT_PRICE_URL = f'https://api.coingecko.com/api/v3/simple/price?ids={CRYPTO}&vs_currencies={CURRENCY}'
+HISTORICAL_URL = f'https://api.coingecko.com/api/v3/coins/{CRYPTO}/market_chart?vs_currency={CURRENCY}&days=30 # &interval=hourly'
+CHECK_INTERVAL_SECONDS = 60  # Check every 60 seconds
+RETRAIN_INTERVAL_MINUTES = 60  # Retrain model every 60 minutes
+ANOMALY_THRESHOLD = -0.5  # Isolation Forest score < -0.5 indicates anomaly (range: -1 to 1)
+HISTORY_WINDOW = 1000  # Max historical points to keep (for memory efficiency)
+CSV_FILE = 'doge_price_history.csv'  # For persistence
+
+# Email configuration (replace with your details)
+EMAIL_FROM = 'vortify-lc@algometic.com'
+EMAIL_TO = 'larry1chan@qq.com'
+EMAIL_PASSWORD = 'g33kPoppy!'
+SMTP_SERVER = 'hwsmtp.exmail.qq.com'
+SMTP_PORT = 465
+
+# Global variables
+price_df = pd.DataFrame(columns=['timestamp', 'price'])  # Historical prices
+model = None  # Isolation Forest model
+
+def fetch_historical_data():
+    """Fetch historical hourly price data from CoinGecko."""
+    try:
+        response = requests.get(HISTORICAL_URL)
+        response.raise_for_status()
+        data = response.json()
+        prices = data['prices']  # List of [timestamp_ms, price]
+        df = pd.DataFrame(prices, columns=['timestamp', 'price'])
+        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+        return df
+    except Exception as e:
+        print(f"Error fetching historical data: {e}")
+        return pd.DataFrame()
+
+def load_or_fetch_history():
+    """Load history from CSV if exists, else fetch from API."""
+    global price_df
+    if os.path.exists(CSV_FILE):
+        price_df = pd.read_csv(CSV_FILE, parse_dates=['timestamp'])
+        print(f"Loaded {len(price_df)} historical points from CSV.")
+    else:
+        price_df = fetch_historical_data()
+        if not price_df.empty:
+            price_df.to_csv(CSV_FILE, index=False)
+            print(f"Fetched and saved {len(price_df)} historical points.")
+    # Trim to window size
+    price_df = price_df.tail(HISTORY_WINDOW)
+
+def fetch_current_price():
+    """Fetch current price from CoinGecko API."""
+    try:
+        response = requests.get(CURRENT_PRICE_URL)
+        response.raise_for_status()
+        data = response.json()
+        price = data[CRYPTO][CURRENCY]
+        timestamp = datetime.now()
+        return timestamp, price
+    except Exception as e:
+        print(f"Error fetching current price: {e}")
+        return None, None
+
+def engineer_features(df):
+    """Engineer features for anomaly detection."""
+    df = df.copy()
+    df['pct_change'] = df['price'].pct_change()  # Percentage change
+    df['abs_diff'] = df['price'].diff()  # Absolute difference
+    df['rolling_mean_5'] = df['price'].rolling(window=5).mean()  # Rolling mean
+    df['rolling_std_5'] = df['price'].rolling(window=5).std()    # Rolling std
+    df['hour'] = df['timestamp'].dt.hour  # Time of day
+    # Fill NaNs with 0 or forward-fill for simplicity
+    df.fillna(0, inplace=True)
+    # Features to use (exclude timestamp and price for model input)
+    features = ['pct_change', 'abs_diff', 'rolling_mean_5', 'rolling_std_5', 'hour']
+    return df[features]
+
+def train_model():
+    """Train or retrain Isolation Forest on historical features."""
+    global model
+    if len(price_df) < 10:  # Need sufficient data
+        print("Insufficient data to train model.")
+        return
+    
+    features = engineer_features(price_df)
+    model = IsolationForest(contamination=0.01, random_state=42)  # Assume 1% anomalies
+    model.fit(features)
+    print("Model retrained successfully.")
+
+def detect_anomaly(timestamp, current_price):
+    """Detect if current price is an anomaly using the model."""
+    if model is None:
+        return False, 0.0
+    
+    # Create a temporary DF with new point appended
+    new_row = pd.DataFrame({'timestamp': [timestamp], 'price': [current_price]})
+    temp_df = pd.concat([price_df, new_row], ignore_index=True)
+    features = engineer_features(temp_df)
+    # Predict on the latest point
+    score = model.decision_function(features.tail(1))[0]  # Score: higher = more normal
+    is_anomaly = score < ANOMALY_THRESHOLD
+    return is_anomaly, score
+
+def send_email_alert(timestamp, current_price, score):
+    """Send email alert for anomaly."""
+    subject = f"Anomaly Detected in {CRYPTO.upper()} Price!"
+    body = f"""
+    Alert Time: {timestamp}
+    Current Price: ${current_price:.4f}
+    Anomaly Score: {score:.2f} (Threshold: {ANOMALY_THRESHOLD})
+    This indicates a sudden and sharp price fluctuation.
+    """
+    
+    msg = MIMEMultipart()
+    msg['From'] = EMAIL_FROM
+    msg['To'] = EMAIL_TO
+    msg['Subject'] = subject
+    msg.attach(MIMEText(body, 'plain'))
+    
+    try:
+        server = smtplib.SMTP(SMTP_SERVER, SMTP_PORT)
+        server.starttls()
+        server.login(EMAIL_FROM, EMAIL_PASSWORD)
+        server.sendmail(EMAIL_FROM, EMAIL_TO, msg.as_string())
+        server.quit()
+        print("Email alert sent successfully.")
+    except Exception as e:
+        print(f"Error sending email: {e}")
+
+def monitor():
+    """Main monitoring function: Fetch price, check for anomaly, alert if needed."""
+    global price_df
+    timestamp, current_price = fetch_current_price()
+    if current_price is None:
+        return
+    
+    # Append new price to history
+    new_row = pd.DataFrame({'timestamp': [timestamp], 'price': [current_price]})
+    price_df = pd.concat([price_df, new_row], ignore_index=True)
+    price_df = price_df.tail(HISTORY_WINDOW)  # Keep window size
+    price_df.to_csv(CSV_FILE, index=False)  # Save updates
+    
+    is_anomaly, score = detect_anomaly(timestamp, current_price)
+    
+    print(f"{timestamp} - Current {CRYPTO.upper()} Price: ${current_price:.4f} | Anomaly Score: {score:.2f}")
+    
+    if is_anomaly:
+        print(f"ANOMALY DETECTED! Score: {score:.2f}")
+        send_email_alert(timestamp, current_price, score)
+
+def retrain_scheduler():
+    """Scheduled task to retrain the model."""
+    train_model()
+
+# Initialize
+load_or_fetch_history()
+train_model()  # Initial training
+
+# Schedule tasks
+schedule.every(CHECK_INTERVAL_SECONDS).seconds.do(monitor)
+schedule.every(RETRAIN_INTERVAL_MINUTES).minutes.do(retrain_scheduler)
+
+# Run the monitoring loop
+print(f"Starting advanced {CRYPTO.upper()} price anomaly monitor with Isolation Forest...")
+while True:
+    schedule.run_pending()
+    time.sleep(1)  # Avoid high CPU usage

strange_doge_rf2.py

@@ -0,0 +1,199 @@
+import requests
+import pandas as pd
+import numpy as np
+from sklearn.ensemble import IsolationForest
+from datetime import datetime
+import os
+
+# Configuration
+CRYPTO = 'solana'  # CoinGecko ID for DOGE
+CURRENCY = 'usd'     # Base currency
+HISTORICAL_URL = f'https://api.coingecko.com/api/v3/coins/{CRYPTO}/market_chart?vs_currency={CURRENCY}&days=30  #&interval=hourly'
+HISTORY_WINDOW = 1000  # Max historical points to keep (for memory efficiency)
+CSV_FILE = '%s_price_history.csv' % CRYPTO  # For loading historical data
+
+# Global variables
+price_df = pd.DataFrame(columns=['timestamp', 'price'])  # Historical prices
+model = None  # Isolation Forest model
+
+def fetch_historical_data():
+    """Fetch historical hourly price data from CoinGecko."""
+    try:
+        response = requests.get(HISTORICAL_URL)
+        response.raise_for_status()
+        data = response.json()
+        prices = data['prices']  # List of [timestamp_ms, price]
+        df = pd.DataFrame(prices, columns=['timestamp', 'price'])
+        df['timestamp'] = pd.to_datetime(df['timestamp'], unit='ms')
+        return df
+    except Exception as e:
+        print(f"Error fetching historical data: {e}")
+        return pd.DataFrame()
+
+def load_or_fetch_history():
+    """Load history from CSV if exists, else fetch from API."""
+    global price_df
+    if os.path.exists(CSV_FILE):
+        price_df = pd.read_csv(CSV_FILE, parse_dates=['timestamp'])
+        print(f"Loaded {len(price_df)} historical points from CSV.")
+    else:
+        price_df = fetch_historical_data()
+        if not price_df.empty:
+            price_df.to_csv(CSV_FILE, index=False)
+            print(f"Fetched and saved {len(price_df)} historical points.")
+    # Trim to window size
+    price_df = price_df.tail(HISTORY_WINDOW)
+
+def engineer_features(df):
+    """Engineer features for anomaly detection."""
+    df = df.copy()
+    df['pct_change'] = df['price'].pct_change()  # Percentage change
+    df['abs_diff'] = df['price'].diff()  # Absolute difference
+    df['rolling_mean_5'] = df['price'].rolling(window=5).mean()  # Rolling mean
+    df['rolling_std_5'] = df['price'].rolling(window=5).std()    # Rolling std
+    df['hour'] = df['timestamp'].dt.hour  # Time of day
+    df.fillna(0, inplace=True)
+    features = ['pct_change', 'abs_diff', 'rolling_mean_5', 'rolling_std_5', 'hour']
+    return df[features]
+
+def train_model(train_df):
+    """Train Isolation Forest on given features."""
+    if len(train_df) < 10:
+        print("Insufficient data to train model.")
+        return None
+    
+    features = engineer_features(train_df)
+    model = IsolationForest(contamination=0.01, random_state=42)
+    model.fit(features)
+    return model
+
+def get_anomaly_score(model, test_df, index):
+    """Get anomaly score for a specific point in the test DF."""
+    # To simulate "appending" without modifying, create temp DF up to this index
+    temp_df = test_df.iloc[:index + 1]  # Includes all prior test points up to this one
+    features = engineer_features(temp_df)
+    score = model.decision_function(features.tail(1))[0]
+    return score
+
+# Sensitivity Analysis
+def run_sensitivity_analysis(thresholds=[-0.2, -0.1,-0.07, -0.05, -0.03, -0.01, 0.1], precision=0.0001, max_iterations=100):
+    """Compute upper/lower price bounds for anomalies across multiple thresholds."""
+    if model is None:
+        print("Model not trained yet. Cannot run sensitivity analysis.")
+        return
+    
+    if price_df.empty:
+        print("No historical data available.")
+        return
+    
+    current_price = price_df['price'].iloc[-1]
+    timestamp = datetime.now()
+    
+    def find_bounds(threshold):
+        def is_anomaly_func(price):
+            # Simulate detection with given threshold
+            new_row = pd.DataFrame({'timestamp': [timestamp], 'price': [price]})
+            temp_df = pd.concat([price_df, new_row], ignore_index=True)
+            features = engineer_features(temp_df)
+            score = model.decision_function(features.tail(1))[0]
+            return score < threshold
+        
+        # Lower bound
+        low = max(0, current_price * 0.5)
+        high = current_price
+        lower_bound = None
+        for _ in range(max_iterations):
+            mid = (low + high) / 2
+            if is_anomaly_func(mid):
+                lower_bound = mid
+                high = mid
+            else:
+                low = mid
+            if high - low < precision:
+                break
+        
+        # Upper bound
+        low = current_price
+        high = current_price * 2
+        upper_bound = None
+        for _ in range(max_iterations):
+            mid = (low + high) / 2
+            if is_anomaly_func(mid):
+                upper_bound = mid
+                low = mid
+            else:
+                high = mid
+            if high - low < precision:
+                break
+        
+        return lower_bound, upper_bound
+    
+    print("Sensitivity Analysis: Anomaly Price Bounds for Different Thresholds")
+    print(f"Based on last historical price: ${current_price:.4f}")
+    for thresh in thresholds:
+        lower, upper = find_bounds(thresh)
+        print(f"\nThreshold {thresh}:")
+        if lower is not None:
+            print(f" - Prices BELOW ~${lower:.4f} would trigger anomaly.")
+        else:
+            print(" - No lower bound found.")
+        if upper is not None:
+            print(f" - Prices ABOVE ~${upper:.4f} would trigger anomaly.")
+        else:
+            print(" - No upper bound found.")
+
+# Backtesting
+def run_backtest(thresholds=[-0.7, -0.5, -0.3], test_fraction=0.2):
+    """Backtest the model on a holdout set, reporting flagged anomalies per threshold."""
+    if len(price_df) < 20:
+        print("Insufficient data for backtesting.")
+        return
+    
+    # Split data: Train on first (1 - test_fraction), test on last test_fraction
+    split_idx = int(len(price_df) * (1 - test_fraction))
+    train_df = price_df.iloc[:split_idx]
+    test_df = price_df.iloc[split_idx:].reset_index(drop=True)
+    
+    backtest_model = train_model(train_df)
+    if backtest_model is None:
+        return
+    
+    print(f"Backtesting on {len(test_df)} holdout points (trained on {len(train_df)} points).")
+    
+    # Score each test point sequentially
+    scores = []
+    for i in range(len(test_df)):
+        score = get_anomaly_score(backtest_model, test_df, i)
+        scores.append(score)
+    
+    for thresh in thresholds:
+        flagged = [i for i, score in enumerate(scores) if score < thresh]
+        flagged_pct = (len(flagged) / len(test_df)) * 100 if len(test_df) > 0 else 0
+        print(f"\nThreshold {thresh}: {len(flagged)} points flagged as anomalies ({flagged_pct:.2f}%)")
+        if flagged:
+            print("Flagged points (timestamp, price):")
+            for idx in flagged[:10]:  # Limit to first 10 for brevity
+                ts = test_df['timestamp'].iloc[idx]
+                price = test_df['price'].iloc[idx]
+                print(f" - {ts}: ${price:.4f}")
+            if len(flagged) > 10:
+                print("   ... (more flagged points omitted)")
+
+# Main Execution
+load_or_fetch_history()
+model = train_model(price_df)  # Train on full data for sensitivity analysis
+
+# Interactive CLI for analysis
+print(f"{CRYPTO.upper()} price anomaly analyzer loaded with Isolation Forest.")
+print("Enter 'sensitivity' for sensitivity analysis, 'backtest' for backtesting, or 'quit' to exit.")
+while True:
+    user_input = input("> ").strip().lower()
+    if user_input == 'quit':
+        print("Exiting...")
+        break
+    elif user_input == 'sensitivity':
+        run_sensitivity_analysis()
+    elif user_input == 'backtest':
+        run_backtest()
+    else:
+        print("Unknown command. Use 'sensitivity', 'backtest', or 'quit'.")