diff --git a/.gitignore b/.gitignore
index 5d5134469d2..e2bc2c4bdb9 100644
--- a/.gitignore
+++ b/.gitignore
@@ -22,4 +22,20 @@ __pycache__/
*.DS_Store
Thumbs.db
-bankmanaging.db
\ No newline at end of file
+bankmanaging.db
+# Nifty Stock Analyzer specific
+nifty50_analysis_*.csv
+*.egg-info/
+build/
+dist/
+
+# Python testing
+.pytest_cache/
+.coverage
+htmlcov/
+.tox/
+
+# IDE
+*.swp
+*.swo
+*~
\ No newline at end of file
diff --git a/.kiro/specs/nifty-stock-analyzer/design.md b/.kiro/specs/nifty-stock-analyzer/design.md
new file mode 100644
index 00000000000..2ad0d7743e4
--- /dev/null
+++ b/.kiro/specs/nifty-stock-analyzer/design.md
@@ -0,0 +1,349 @@
+# Design Document: Nifty 50 Stock Analyzer
+
+## Overview
+
+The Nifty 50 Stock Analyzer is a Python CLI application that provides real-time technical analysis of India's top 50 stocks. The tool leverages the yfinance library to fetch market data, performs moving average calculations, and identifies momentum opportunities by highlighting stocks trading significantly above their 20-day moving average. Results are exported to CSV format for further analysis.
+
+The application follows a simple pipeline architecture: data retrieval → calculation → filtering → export, with robust error handling at each stage.
+
+## Architecture
+
+The application uses a modular, pipeline-based architecture:
+
+```
+┌─────────────┐ ┌──────────────┐ ┌─────────────┐ ┌──────────────┐
+│ CLI │────▶│ Data │────▶│ Analysis │────▶│ Export │
+│ Interface │ │ Fetcher │ │ Engine │ │ Handler │
+└─────────────┘ └──────────────┘ └─────────────┘ └──────────────┘
+ │ │ │
+ ▼ ▼ ▼
+ ┌──────────────┐ ┌─────────────┐ ┌──────────────┐
+ │ yfinance │ │ Calculator │ │ CSV Writer │
+ │ API │ │ Module │ │ │
+ └──────────────┘ └─────────────┘ └──────────────┘
+```
+
+### Key Design Principles
+
+1. **Separation of Concerns**: Each module has a single, well-defined responsibility
+2. **Fail-Safe Operation**: Errors in processing individual stocks don't halt the entire analysis
+3. **User Feedback**: Progress indicators and clear messaging throughout execution
+4. **Data Integrity**: Validation at each pipeline stage ensures reliable results
+
+## Components and Interfaces
+
+### 1. CLI Interface Module (`cli.py`)
+
+**Responsibility**: Entry point for the application, handles user interaction and orchestrates the pipeline.
+
+**Key Functions**:
+- `main()`: Entry point that coordinates the entire workflow
+- `display_progress(current, total, stock_symbol)`: Shows progress during data fetching
+- `display_summary(results)`: Prints analysis summary to console
+
+**Interface**:
+```python
+def main() -> int:
+ """
+ Main entry point for the CLI tool.
+ Returns: Exit code (0 for success, 1 for failure)
+ """
+```
+
+### 2. Stock Symbol Provider (`symbols.py`)
+
+**Responsibility**: Provides the list of Nifty 50 stock symbols with proper Yahoo Finance formatting.
+
+**Key Functions**:
+- `get_nifty50_symbols()`: Returns list of all Nifty 50 stock symbols
+
+**Interface**:
+```python
+def get_nifty50_symbols() -> List[str]:
+ """
+ Returns the list of Nifty 50 stock symbols in Yahoo Finance format.
+ Returns: List of stock symbols (e.g., ['RELIANCE.NS', 'TCS.NS', ...])
+ """
+```
+
+**Note**: Nifty 50 symbols require the `.NS` suffix for Yahoo Finance (National Stock Exchange of India).
+
+### 3. Data Fetcher Module (`fetcher.py`)
+
+**Responsibility**: Retrieves stock data from Yahoo Finance with retry logic and error handling.
+
+**Key Functions**:
+- `fetch_stock_data(symbol, days=30)`: Fetches historical data for a single stock
+- `fetch_all_stocks(symbols)`: Fetches data for all provided symbols with progress tracking
+
+**Interface**:
+```python
+@dataclass
+class StockData:
+ symbol: str
+ current_price: float
+ historical_prices: List[float] # Last 30 days of closing prices
+ fetch_success: bool
+ error_message: Optional[str] = None
+
+def fetch_stock_data(symbol: str, days: int = 30, retries: int = 3) -> StockData:
+ """
+ Fetches stock data with retry logic.
+ Args:
+ symbol: Stock symbol (e.g., 'RELIANCE.NS')
+ days: Number of days of historical data to fetch
+ retries: Number of retry attempts on failure
+ Returns: StockData object with fetched information
+ """
+```
+
+### 4. Analysis Engine (`analyzer.py`)
+
+**Responsibility**: Performs moving average calculations and identifies stocks meeting the threshold criteria.
+
+**Key Functions**:
+- `calculate_moving_average(prices, period=20)`: Computes moving average
+- `calculate_percentage_difference(current, average)`: Computes percentage difference
+- `analyze_stock(stock_data)`: Performs complete analysis on a single stock
+
+**Interface**:
+```python
+@dataclass
+class AnalysisResult:
+ symbol: str
+ current_price: float
+ moving_average_20d: Optional[float]
+ percentage_difference: Optional[float]
+ is_highlighted: bool # True if >= 5% above MA
+ error_message: Optional[str] = None
+
+def calculate_moving_average(prices: List[float], period: int = 20) -> Optional[float]:
+ """
+ Calculates simple moving average.
+ Args:
+ prices: List of closing prices (most recent last)
+ period: Number of periods for moving average
+ Returns: Moving average value or None if insufficient data
+ """
+
+def analyze_stock(stock_data: StockData) -> AnalysisResult:
+ """
+ Performs complete analysis on stock data.
+ Args:
+ stock_data: StockData object from fetcher
+ Returns: AnalysisResult with all calculated metrics
+ """
+```
+
+### 5. Export Handler (`exporter.py`)
+
+**Responsibility**: Writes analysis results to CSV file with proper formatting.
+
+**Key Functions**:
+- `export_to_csv(results, filename)`: Writes results to CSV file
+- `generate_filename()`: Creates timestamped filename
+
+**Interface**:
+```python
+def export_to_csv(results: List[AnalysisResult], filename: Optional[str] = None) -> str:
+ """
+ Exports analysis results to CSV file.
+ Args:
+ results: List of AnalysisResult objects
+ filename: Optional custom filename (generates timestamped name if None)
+ Returns: Path to created CSV file
+ """
+```
+
+**CSV Format**:
+```
+Symbol,Current Price,20-Day MA,Percentage Difference,Highlighted
+RELIANCE.NS,2450.50,2350.25,4.26,No
+TCS.NS,3500.00,3300.00,6.06,Yes
+...
+```
+
+## Data Models
+
+### StockData
+Represents raw data fetched from Yahoo Finance.
+
+```python
+@dataclass
+class StockData:
+ symbol: str # Stock symbol (e.g., 'RELIANCE.NS')
+ current_price: float # Most recent closing price
+ historical_prices: List[float] # Last 30 days of closing prices
+ fetch_success: bool # Whether data fetch succeeded
+ error_message: Optional[str] = None # Error details if fetch failed
+```
+
+### AnalysisResult
+Represents analyzed stock with calculated metrics.
+
+```python
+@dataclass
+class AnalysisResult:
+ symbol: str # Stock symbol
+ current_price: float # Current trading price
+ moving_average_20d: Optional[float] # 20-day moving average (None if insufficient data)
+ percentage_difference: Optional[float] # Percentage above/below MA
+ is_highlighted: bool # True if >= 5% above MA
+ error_message: Optional[str] = None # Error details if analysis failed
+```
+
+## Correctness Properties
+
+*A property is a characteristic or behavior that should hold true across all valid executions of a system—essentially, a formal statement about what the system should do. Properties serve as the bridge between human-readable specifications and machine-verifiable correctness guarantees.*
+
+
+### Property 1: Error isolation
+*For any* list of stock symbols containing both valid and invalid symbols, processing failures for invalid symbols should not prevent the successful processing of valid symbols, and all failed symbols should be marked with error information.
+**Validates: Requirements 1.4, 6.2**
+
+### Property 2: Current price inclusion
+*For any* stock that is successfully fetched, the resulting StockData object should contain a valid current_price value (non-negative float).
+**Validates: Requirements 1.5**
+
+### Property 3: Sufficient historical data
+*For any* stock that is successfully fetched, the historical_prices list should contain at least 20 data points to enable moving average calculation.
+**Validates: Requirements 2.1**
+
+### Property 4: Moving average correctness
+*For any* list of 20 or more closing prices, the calculated 20-day moving average should equal the arithmetic mean of the most recent 20 prices: sum(prices[-20:]) / 20.
+**Validates: Requirements 2.2**
+
+### Property 5: Percentage difference formula
+*For any* current price and moving average pair, the calculated percentage difference should equal ((current_price - moving_average) / moving_average) * 100.
+**Validates: Requirements 3.1, 3.4**
+
+### Property 6: Highlight threshold accuracy
+*For any* analyzed stock, is_highlighted should be True if and only if percentage_difference >= 5.0.
+**Validates: Requirements 3.2**
+
+### Property 7: CSV column completeness
+*For any* exported CSV file, parsing the header row should reveal exactly these columns in order: Symbol, Current Price, 20-Day MA, Percentage Difference, Highlighted.
+**Validates: Requirements 4.2**
+
+### Property 8: Filename timestamp format
+*For any* auto-generated CSV filename, it should match the pattern "nifty50_analysis_YYYYMMDD_HHMMSS.csv" where the timestamp represents a valid date and time.
+**Validates: Requirements 4.4**
+
+### Property 9: Failed stock tracking
+*For any* analysis execution where some stocks fail to fetch, the final results should include entries for failed stocks with error_message populated and is_highlighted set to False.
+**Validates: Requirements 6.4**
+
+## Error Handling
+
+### Network Errors
+- **Retry Strategy**: Exponential backoff with 3 retry attempts (delays: 1s, 2s, 4s)
+- **Timeout**: 10-second timeout per request to prevent hanging
+- **Graceful Degradation**: Failed stocks are logged but don't halt execution
+
+### Data Validation Errors
+- **Insufficient Historical Data**: If fewer than 20 days available, set moving_average_20d to None
+- **Invalid Price Data**: If current price is negative or zero, mark as error
+- **Missing Data**: If yfinance returns empty dataset, mark as fetch failure
+
+### File System Errors
+- **Write Permissions**: Check write permissions before attempting CSV export
+- **Disk Space**: Basic validation that output directory is writable
+- **Path Errors**: Use absolute paths and validate directory existence
+
+### Error Reporting
+All errors are captured in the respective data structures (StockData.error_message, AnalysisResult.error_message) and reported in:
+1. Console output during execution
+2. Final summary statistics
+3. CSV output (error stocks included with error indicators)
+
+## Testing Strategy
+
+### Unit Testing Framework
+- **Framework**: pytest
+- **Coverage Target**: 80% code coverage minimum
+- **Test Organization**: Mirror source structure in `tests/` directory
+
+### Unit Tests
+Unit tests will cover:
+- **Moving Average Calculation**: Test with known price sequences (e.g., [100, 110, 120, ...] should yield predictable MA)
+- **Percentage Difference**: Test with edge cases (zero MA, negative differences, exact 5% threshold)
+- **CSV Export**: Verify file creation, header format, and data row structure
+- **Symbol Provider**: Verify exactly 50 symbols returned with .NS suffix
+- **Error Handling**: Test retry logic with mocked network failures
+
+### Property-Based Testing Framework
+- **Framework**: Hypothesis (Python property-based testing library)
+- **Iterations**: Minimum 100 test cases per property
+- **Strategy**: Generate random but valid test data to verify universal properties
+
+### Property-Based Tests
+Each property-based test will:
+1. Generate random valid inputs (stock prices, symbol lists, etc.)
+2. Execute the function under test
+3. Verify the correctness property holds
+4. Be tagged with format: `# Feature: nifty-stock-analyzer, Property X: [property description]`
+
+Property tests will verify:
+- **Property 1**: Error isolation with randomly generated valid/invalid symbol mixes
+- **Property 2**: Current price inclusion across random successful fetches
+- **Property 3**: Historical data sufficiency across random fetch results
+- **Property 4**: Moving average mathematical correctness with random price sequences
+- **Property 5**: Percentage formula correctness with random price/MA pairs
+- **Property 6**: Highlight threshold accuracy with random percentage differences
+- **Property 7**: CSV column structure across random result sets
+- **Property 8**: Filename timestamp format across random execution times
+- **Property 9**: Failed stock tracking with random failure scenarios
+
+### Integration Testing
+- **End-to-End Test**: Run against a small subset of real Nifty 50 symbols (3-5 stocks)
+- **Mock Testing**: Use mocked yfinance responses for predictable testing
+- **CSV Validation**: Parse generated CSV files to verify data integrity
+
+### Test Data Strategy
+- **Mock Data**: Create realistic StockData and AnalysisResult fixtures
+- **Price Generators**: Generate valid price sequences (positive floats with realistic ranges)
+- **Symbol Generators**: Generate valid NSE symbol formats
+- **Edge Cases**: Empty lists, single-element lists, exactly 20 elements, boundary values
+
+## Dependencies
+
+### External Libraries
+```
+yfinance>=0.2.28 # Yahoo Finance data retrieval
+pandas>=2.0.0 # Data manipulation (used by yfinance)
+hypothesis>=6.90.0 # Property-based testing
+pytest>=7.4.0 # Unit testing framework
+```
+
+### Python Version
+- **Minimum**: Python 3.9
+- **Recommended**: Python 3.11+
+
+## Performance Considerations
+
+### Expected Performance
+- **Data Fetching**: ~2-3 seconds per stock (network dependent)
+- **Total Execution**: ~2-3 minutes for all 50 stocks
+- **Memory Usage**: < 100MB for typical execution
+
+### Optimization Strategies
+- **Sequential Processing**: Avoid rate limiting from Yahoo Finance
+- **Minimal Data Storage**: Keep only necessary historical data (30 days)
+- **Efficient CSV Writing**: Use pandas for optimized CSV export
+
+### Scalability
+The current design is optimized for the fixed set of 50 Nifty stocks. For larger datasets:
+- Consider parallel fetching with rate limiting
+- Implement caching for historical data
+- Use database storage instead of CSV for large result sets
+
+## Future Enhancements
+
+Potential future features (out of scope for initial implementation):
+1. **Configurable Thresholds**: Allow users to specify custom percentage thresholds
+2. **Multiple Moving Averages**: Support 50-day, 100-day, 200-day MAs
+3. **Technical Indicators**: Add RSI, MACD, Bollinger Bands
+4. **Historical Analysis**: Compare current signals against historical patterns
+5. **Alerts**: Email/SMS notifications when stocks meet criteria
+6. **Web Dashboard**: Interactive visualization of results
+7. **Database Storage**: Persist historical analysis results for trend tracking
diff --git a/.kiro/specs/nifty-stock-analyzer/requirements.md b/.kiro/specs/nifty-stock-analyzer/requirements.md
new file mode 100644
index 00000000000..7fc786e4bec
--- /dev/null
+++ b/.kiro/specs/nifty-stock-analyzer/requirements.md
@@ -0,0 +1,85 @@
+# Requirements Document
+
+## Introduction
+
+This document specifies the requirements for a Python CLI tool that analyzes Nifty 50 stocks in real-time. The tool fetches current stock data using the yfinance library, calculates 20-day moving averages, identifies stocks trading significantly above their moving average, and exports results to CSV format for further analysis.
+
+## Glossary
+
+- **Nifty 50**: The National Stock Exchange of India's benchmark stock market index representing the weighted average of 50 of the largest Indian companies listed on the exchange
+- **CLI Tool**: Command-Line Interface Tool - a program that operates through text-based commands in a terminal
+- **yfinance**: A Python library that provides access to Yahoo Finance market data
+- **20-day Moving Average**: The arithmetic mean of a stock's closing prices over the previous 20 trading days
+- **Trading Above Average**: When a stock's current price exceeds its moving average by a specified percentage threshold
+- **Stock Analyzer**: The system being developed
+
+## Requirements
+
+### Requirement 1
+
+**User Story:** As a stock trader, I want to fetch real-time data for all Nifty 50 stocks, so that I can analyze current market conditions.
+
+#### Acceptance Criteria
+
+1. WHEN the Stock Analyzer is executed, THE Stock Analyzer SHALL retrieve the list of all 50 Nifty 50 stock symbols
+2. WHEN retrieving stock data, THE Stock Analyzer SHALL fetch current price data using the yfinance library
+3. WHEN fetching data for multiple stocks, THE Stock Analyzer SHALL handle each stock symbol sequentially to ensure data completeness
+4. IF a stock symbol fails to retrieve data, THEN THE Stock Analyzer SHALL log the error and continue processing remaining stocks
+5. WHEN all data is retrieved, THE Stock Analyzer SHALL include the current trading price for each stock
+
+### Requirement 2
+
+**User Story:** As a technical analyst, I want the tool to calculate the 20-day moving average for each stock, so that I can identify price trends.
+
+#### Acceptance Criteria
+
+1. WHEN historical data is available, THE Stock Analyzer SHALL retrieve at least 20 days of closing price data for each stock
+2. WHEN calculating the moving average, THE Stock Analyzer SHALL compute the arithmetic mean of the most recent 20 closing prices
+3. IF fewer than 20 days of data are available for a stock, THEN THE Stock Analyzer SHALL mark the moving average as unavailable and continue processing
+4. WHEN the calculation completes, THE Stock Analyzer SHALL store both the current price and the 20-day moving average for each stock
+
+### Requirement 3
+
+**User Story:** As an investor, I want to identify stocks trading 5% above their 20-day moving average, so that I can spot potential momentum opportunities.
+
+#### Acceptance Criteria
+
+1. WHEN comparing prices to moving averages, THE Stock Analyzer SHALL calculate the percentage difference between current price and 20-day moving average
+2. WHEN a stock's current price exceeds its 20-day moving average by 5% or more, THE Stock Analyzer SHALL flag that stock as highlighted
+3. WHEN displaying results, THE Stock Analyzer SHALL clearly indicate which stocks meet the 5% threshold criteria
+4. WHEN calculating percentage differences, THE Stock Analyzer SHALL use the formula: ((current_price - moving_average) / moving_average) * 100
+
+### Requirement 4
+
+**User Story:** As a data analyst, I want the results saved to a CSV file, so that I can perform further analysis in spreadsheet applications.
+
+#### Acceptance Criteria
+
+1. WHEN analysis is complete, THE Stock Analyzer SHALL create a CSV file containing all analyzed stock data
+2. WHEN writing to CSV, THE Stock Analyzer SHALL include columns for stock symbol, current price, 20-day moving average, percentage difference, and highlight status
+3. WHEN the CSV file already exists, THE Stock Analyzer SHALL overwrite it with new data
+4. WHEN the CSV is created, THE Stock Analyzer SHALL use a descriptive filename that includes a timestamp
+5. WHEN writing completes, THE Stock Analyzer SHALL display the output file path to the user
+
+### Requirement 5
+
+**User Story:** As a CLI user, I want clear feedback during execution, so that I understand what the tool is doing and when it completes.
+
+#### Acceptance Criteria
+
+1. WHEN the Stock Analyzer starts, THE Stock Analyzer SHALL display a message indicating the analysis has begun
+2. WHILE fetching data, THE Stock Analyzer SHALL display progress indicators showing which stocks are being processed
+3. WHEN errors occur, THE Stock Analyzer SHALL display clear error messages with details about what failed
+4. WHEN analysis completes successfully, THE Stock Analyzer SHALL display a summary including the number of stocks analyzed and the number meeting the highlight criteria
+5. WHEN the tool finishes, THE Stock Analyzer SHALL display the location of the output CSV file
+
+### Requirement 6
+
+**User Story:** As a developer, I want the tool to handle network failures gracefully, so that temporary connectivity issues don't crash the application.
+
+#### Acceptance Criteria
+
+1. IF network connectivity is lost during data retrieval, THEN THE Stock Analyzer SHALL retry the request up to 3 times with exponential backoff
+2. IF all retry attempts fail for a stock, THEN THE Stock Analyzer SHALL log the failure and continue with remaining stocks
+3. WHEN network errors occur, THE Stock Analyzer SHALL display user-friendly error messages rather than technical stack traces
+4. WHEN the analysis completes with some failures, THE Stock Analyzer SHALL report which stocks could not be processed
diff --git a/.kiro/specs/nifty-stock-analyzer/tasks.md b/.kiro/specs/nifty-stock-analyzer/tasks.md
new file mode 100644
index 00000000000..7e1847dea74
--- /dev/null
+++ b/.kiro/specs/nifty-stock-analyzer/tasks.md
@@ -0,0 +1,176 @@
+# Implementation Plan
+
+- [x] 1. Set up project structure and dependencies
+
+
+
+
+
+ - Create directory structure for the nifty-stock-analyzer CLI tool
+ - Set up requirements.txt with yfinance, pandas, hypothesis, and pytest
+ - Create main entry point script and package structure
+ - _Requirements: All requirements depend on proper project setup_
+
+- [ ] 2. Implement Nifty 50 symbol provider
+ - [ ] 2.1 Create symbols.py module with Nifty 50 stock list
+ - Define the complete list of 50 Nifty stocks with .NS suffix for Yahoo Finance
+ - Implement get_nifty50_symbols() function to return the symbol list
+ - _Requirements: 1.1_
+
+ - [ ]* 2.2 Write unit tests for symbol provider
+ - Test that exactly 50 symbols are returned
+ - Test that all symbols end with .NS suffix
+ - Test that symbols are valid NSE format
+ - _Requirements: 1.1_
+
+- [ ] 3. Implement data models and core data structures
+ - [ ] 3.1 Create data models in models.py
+ - Define StockData dataclass with symbol, current_price, historical_prices, fetch_success, error_message
+ - Define AnalysisResult dataclass with symbol, current_price, moving_average_20d, percentage_difference, is_highlighted, error_message
+ - _Requirements: 1.5, 2.4, 3.1, 3.2_
+
+ - [ ]* 3.2 Write property test for data model validation
+ - **Property 2: Current price inclusion**
+ - **Validates: Requirements 1.5**
+
+- [ ] 4. Implement stock data fetcher with retry logic
+ - [ ] 4.1 Create fetcher.py module for Yahoo Finance integration
+ - Implement fetch_stock_data() function with yfinance integration
+ - Add retry logic with exponential backoff (3 attempts: 1s, 2s, 4s delays)
+ - Handle network timeouts and API errors gracefully
+ - _Requirements: 1.2, 1.4, 6.1, 6.2_
+
+ - [ ] 4.2 Implement batch fetching with progress tracking
+ - Create fetch_all_stocks() function to process multiple symbols
+ - Add progress indicators for user feedback during fetching
+ - Ensure failed stocks don't halt processing of remaining stocks
+ - _Requirements: 1.3, 1.4, 5.2_
+
+ - [ ]* 4.3 Write property test for error isolation
+ - **Property 1: Error isolation**
+ - **Validates: Requirements 1.4, 6.2**
+
+ - [ ]* 4.4 Write property test for historical data sufficiency
+ - **Property 3: Sufficient historical data**
+ - **Validates: Requirements 2.1**
+
+ - [ ]* 4.5 Write unit tests for fetcher module
+ - Test successful data fetching with mocked yfinance responses
+ - Test retry logic with simulated network failures
+ - Test error handling for invalid symbols
+ - _Requirements: 1.2, 1.4, 6.1_
+
+- [ ] 5. Implement analysis engine for moving averages and calculations
+ - [ ] 5.1 Create analyzer.py module for technical calculations
+ - Implement calculate_moving_average() function for 20-day MA calculation
+ - Implement calculate_percentage_difference() function using the specified formula
+ - Handle edge cases like insufficient data (< 20 days)
+ - _Requirements: 2.2, 2.3, 3.1, 3.4_
+
+ - [ ] 5.2 Implement stock analysis and highlighting logic
+ - Create analyze_stock() function to perform complete analysis on StockData
+ - Implement highlighting logic for stocks >= 5% above 20-day MA
+ - Ensure proper error propagation from data fetching to analysis
+ - _Requirements: 3.2, 2.4_
+
+ - [ ]* 5.3 Write property test for moving average correctness
+ - **Property 4: Moving average correctness**
+ - **Validates: Requirements 2.2**
+
+ - [ ]* 5.4 Write property test for percentage difference formula
+ - **Property 5: Percentage difference formula**
+ - **Validates: Requirements 3.1, 3.4**
+
+ - [ ]* 5.5 Write property test for highlight threshold accuracy
+ - **Property 6: Highlight threshold accuracy**
+ - **Validates: Requirements 3.2**
+
+ - [ ]* 5.6 Write unit tests for analysis engine
+ - Test moving average calculation with known price sequences
+ - Test percentage difference calculation with edge cases
+ - Test highlighting logic at boundary conditions (exactly 5%)
+ - Test handling of insufficient historical data
+ - _Requirements: 2.2, 2.3, 3.1, 3.2_
+
+- [ ] 6. Implement CSV export functionality
+ - [ ] 6.1 Create exporter.py module for CSV output
+ - Implement export_to_csv() function to write AnalysisResult objects to CSV
+ - Create generate_filename() function for timestamped filenames
+ - Define CSV column structure: Symbol, Current Price, 20-Day MA, Percentage Difference, Highlighted
+ - _Requirements: 4.1, 4.2, 4.4_
+
+ - [ ] 6.2 Implement file handling and overwrite logic
+ - Handle existing file overwriting as specified
+ - Add proper error handling for file system operations
+ - Validate write permissions and disk space
+ - _Requirements: 4.3_
+
+ - [ ]* 6.3 Write property test for CSV column completeness
+ - **Property 7: CSV column completeness**
+ - **Validates: Requirements 4.2**
+
+ - [ ]* 6.4 Write property test for filename timestamp format
+ - **Property 8: Filename timestamp format**
+ - **Validates: Requirements 4.4**
+
+ - [ ]* 6.5 Write unit tests for CSV export
+ - Test CSV file creation and structure
+ - Test filename generation with timestamps
+ - Test file overwriting behavior
+ - Test error handling for file system issues
+ - _Requirements: 4.1, 4.2, 4.3, 4.4_
+
+- [ ] 7. Implement CLI interface and user feedback
+ - [ ] 7.1 Create cli.py module for command-line interface
+ - Implement main() function as entry point
+ - Add startup and completion messages for user feedback
+ - Integrate progress tracking during data fetching
+ - _Requirements: 5.1, 5.2, 5.5_
+
+ - [ ] 7.2 Implement error reporting and summary display
+ - Add clear error message display (avoid technical stack traces)
+ - Implement summary display with analysis statistics
+ - Display output CSV file location upon completion
+ - _Requirements: 5.3, 5.4, 5.5, 6.3_
+
+ - [ ]* 7.3 Write property test for failed stock tracking
+ - **Property 9: Failed stock tracking**
+ - **Validates: Requirements 6.4**
+
+ - [ ]* 7.4 Write unit tests for CLI interface
+ - Test main workflow orchestration
+ - Test error message formatting
+ - Test summary statistics calculation
+ - _Requirements: 5.1, 5.3, 5.4, 5.5_
+
+- [ ] 8. Create main entry point and package configuration
+ - [ ] 8.1 Set up main.py or __main__.py entry point
+ - Create executable entry point that calls cli.main()
+ - Add proper exit code handling (0 for success, 1 for failure)
+ - Ensure the tool can be run as `python -m nifty_analyzer`
+ - _Requirements: All requirements - this is the integration point_
+
+ - [ ] 8.2 Add command-line argument parsing (optional enhancement)
+ - Add optional --output flag for custom CSV filename
+ - Add --help flag with usage information
+ - Add --verbose flag for detailed progress output
+ - _Requirements: 4.4, 5.2_
+
+- [ ] 9. Integration testing and end-to-end validation
+ - [ ] 9.1 Create integration test with real API calls
+ - Test complete workflow with a small subset of Nifty 50 stocks (3-5 symbols)
+ - Validate that CSV output contains expected data structure
+ - Test error handling with intentionally invalid symbols
+ - _Requirements: All requirements - end-to-end validation_
+
+ - [ ]* 9.2 Write integration tests with mocked data
+ - Create comprehensive test with mocked yfinance responses
+ - Test complete pipeline from symbol list to CSV export
+ - Validate all error paths and edge cases
+ - _Requirements: All requirements_
+
+- [ ] 10. Final checkpoint and documentation
+ - Ensure all tests pass, ask the user if questions arise
+ - Create README.md with installation and usage instructions
+ - Verify all requirements are met through manual testing
+ - _Requirements: All requirements - final validation_
\ No newline at end of file
diff --git a/1 File handle/File handle binary/.env b/1 File handle/File handle binary/.env
deleted file mode 100644
index ab3d7291cb4..00000000000
--- a/1 File handle/File handle binary/.env
+++ /dev/null
@@ -1 +0,0 @@
-STUDENTS_RECORD_FILE= "student_records.pkl"
\ No newline at end of file
diff --git a/1 File handle/File handle binary/Update a binary file2.py b/1 File handle/File handle binary/Update a binary file2.py
index 8eb900845c3..b9f6d5f2755 100644
--- a/1 File handle/File handle binary/Update a binary file2.py
+++ b/1 File handle/File handle binary/Update a binary file2.py
@@ -1,33 +1,33 @@
-# updating records in a binary file
-
-import pickle
-
-
-def update():
- with open("studrec.dat", "rb+") as File:
- value = pickle.load(File)
- found = False
- roll = int(input("Enter the roll number of the record"))
-
- for i in value:
- if roll == i[0]:
- print(f"current name {i[1]}")
- print(f"current marks {i[2]}")
- i[1] = input("Enter the new name")
- i[2] = int(input("Enter the new marks"))
- found = True
-
- if not found:
- print("Record not found")
-
- else:
- pickle.dump(value, File)
- File.seek(0)
- print(pickle.load(File))
-
-
-update()
-
-# ! Instead of AB use WB?
-# ! It may have memory limits while updating large files but it would be good
-# ! Few lakhs records would be fine and wouldn't create any much of a significant issues
+# updating records in a binary file
+
+import pickle
+
+
+def update():
+ with open("studrec.dat", "rb+") as File:
+ value = pickle.load(File)
+ found = False
+ roll = int(input("Enter the roll number of the record"))
+
+ for i in value:
+ if roll == i[0]:
+ print(f"current name {i[1]}")
+ print(f"current marks {i[2]}")
+ i[1] = input("Enter the new name")
+ i[2] = int(input("Enter the new marks"))
+ found = True
+
+ if not found:
+ print("Record not found")
+
+ else:
+ pickle.dump(value, File)
+ File.seek(0)
+ print(pickle.load(File))
+
+
+update()
+
+# ! Instead of AB use WB?
+# ! It may have memory limits while updating large files but it would be good
+# ! Few lakhs records would be fine and wouldn't create any much of a significant issues
diff --git a/1 File handle/File handle binary/delete.py b/1 File handle/File handle binary/delete.py
deleted file mode 100644
index c2175469522..00000000000
--- a/1 File handle/File handle binary/delete.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import logging
-import os
-import pickle
-
-from dotenv import load_dotenv
-
-base = os.path.dirname(__file__)
-load_dotenv(os.path.join(base, ".env"))
-
-logging.basicConfig(level=logging.INFO, format="%(levelname)s: %(message)s")
-student_record = os.getenv("STUDENTS_RECORD_FILE")
-
-
-def b_read():
- # Opening a file & loading it
- if not os.path.exists(student_record):
- logging.warning("File not found")
- return
-
- with open(student_record, "rb") as F:
- student = pickle.load(F)
- logging.info("File opened successfully")
- logging.info("Records in the file are:")
- for i in student:
- logging.info(i)
-
-
-def b_modify():
- # Deleting the Roll no. entered by user
- if not os.path.exists(student_record):
- logging.warning("File not found")
- return
- roll_no = int(input("Enter the Roll No. to be deleted: "))
- student = 0
- with open(student_record, "rb") as F:
- student = pickle.load(F)
-
- with open(student_record, "wb") as F:
- rec = [i for i in student if i[0] != roll_no]
- pickle.dump(rec, F)
-
-
-b_read()
-b_modify()
diff --git a/1 File handle/File handle binary/question 1 (elegible for remedial, top marks).py b/1 File handle/File handle binary/question 1 (elegible for remedial, top marks).py
deleted file mode 100644
index 8b6d120cbf7..00000000000
--- a/1 File handle/File handle binary/question 1 (elegible for remedial, top marks).py
+++ /dev/null
@@ -1,66 +0,0 @@
-"""Amit is a monitor of class XII-A and he stored the record of all
-the students of his class in a file named “student_records.pkl”.
-Structure of record is [roll number, name, percentage]. His computer
-teacher has assigned the following duty to Amit
-
-Write a function remcount( ) to count the number of students who need
- remedial class (student who scored less than 40 percent)
-and find the top students of the class.
-
-We have to find weak students and bright students.
-"""
-
-## Find bright students and weak students
-
-from dotenv import load_dotenv
-import os
-
-base = os.path.dirname(__file__)
-load_dotenv(os.path.join(base, ".env"))
-student_record = os.getenv("STUDENTS_RECORD_FILE")
-
-import pickle
-import logging
-
-# Define logger with info
-# import polar
-
-
-## ! Unoptimised rehne de abhi ke liye
-
-
-def remcount():
- with open(student_record, "rb") as F:
- val = pickle.load(F)
- count = 0
- weak_students = []
-
- for student in val:
- if student[2] <= 40:
- print(f"{student} eligible for remedial")
- weak_students.append(student)
- count += 1
- print(f"the total number of weak students are {count}")
- print(f"The weak students are {weak_students}")
-
- # ! highest marks is the key here first marks
-
-
-def firstmark():
- with open(student_record, "rb") as F:
- val = pickle.load(F)
- count = 0
- main = [i[2] for i in val]
-
- top = max(main)
- print(top, "is the first mark")
-
- for i in val:
- if top == i[2]:
- print(f"{i}\ncongrats")
- count += 1
- print("The total number of students who secured top marks are", count)
-
-
-remcount()
-firstmark()
diff --git a/1 File handle/File handle binary/read.py b/1 File handle/File handle binary/read.py
deleted file mode 100644
index 9c69281b4bf..00000000000
--- a/1 File handle/File handle binary/read.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import pickle
-
-
-def binary_read():
- with open("studrec.dat", "rb") as b:
- stud = pickle.load(b)
- print(stud)
-
- # prints the whole record in nested list format
- print("contents of binary file")
-
- for ch in stud:
- print(ch) # prints one of the chosen rec in list
-
- rno = ch[0]
- rname = ch[1] # due to unpacking the val not printed in list format
- rmark = ch[2]
-
- print(rno, rname, rmark, end="\t")
-
-
-binary_read()
diff --git a/1 File handle/File handle binary/search record in binary file.py b/1 File handle/File handle binary/search record in binary file.py
deleted file mode 100644
index a6529e15240..00000000000
--- a/1 File handle/File handle binary/search record in binary file.py
+++ /dev/null
@@ -1,22 +0,0 @@
-# binary file to search a given record
-
-import pickle
-from dotenv import load_dotenv
-
-
-def search():
- with open("student_records.pkl", "rb") as F:
- # your file path will be different
- search = True
- rno = int(input("Enter the roll number of the student"))
-
- for i in pickle.load(F):
- if i[0] == rno:
- print(f"Record found successfully\n{i}")
- search = False
-
- if search:
- print("Sorry! record not found")
-
-
-binary_search()
diff --git a/1 File handle/File handle text/counter.py b/1 File handle/File handle text/counter.py
deleted file mode 100644
index 476371a3951..00000000000
--- a/1 File handle/File handle text/counter.py
+++ /dev/null
@@ -1,34 +0,0 @@
-"""
-Class resposible for counting words for different files:
-- Reduce redundant code
-- Easier code management/debugging
-- Code readability
-"""
-
-
-class Counter:
- def __init__(self, text: str) -> None:
- self.text = text
-
- # Define the initial count of the lower and upper case.
- self.count_lower = 0
- self.count_upper = 0
- self.count()
-
- def count(self) -> None:
- for char in self.text:
- if char.lower():
- self.count_lower += 1
- elif char.upper():
- self.count_upper += 1
-
- return (self.count_lower, self.count_upper)
-
- def get_total_lower(self) -> int:
- return self.count_lower
-
- def get_total_upper(self) -> int:
- return self.count_upper
-
- def get_total(self) -> int:
- return self.count_lower + self.count_upper
diff --git a/1 File handle/File handle text/file handle 12 length of line in text file.py b/1 File handle/File handle text/file handle 12 length of line in text file.py
deleted file mode 100644
index 608f1bf94e3..00000000000
--- a/1 File handle/File handle text/file handle 12 length of line in text file.py
+++ /dev/null
@@ -1,38 +0,0 @@
-import os
-import time
-
-file_name = input("Enter the file name to create:- ")
-
-print(file_name)
-
-
-def write_to_file(file_name):
- if os.path.exists(file_name):
- print(f"Error: {file_name} already exists.")
- return
-
- with open(file_name, "a") as F:
- while True:
- text = input("enter any text to add in the file:- ")
- F.write(f"{text}\n")
- choice = input("Do you want to enter more, y/n").lower()
- if choice == "n":
- break
-
-
-def longlines():
- with open(file_name, encoding="utf-8") as F:
- lines = F.readlines()
- lines_less_than_50 = list(filter(lambda line: len(line) < 50, lines))
-
- if not lines_less_than_50:
- print("There is no line which is less than 50")
- else:
- for i in lines_less_than_50:
- print(i, end="\t")
-
-
-if __name__ == "__main__":
- write_to_file(file_name)
- time.sleep(1)
- longlines()
diff --git a/1 File handle/File handle text/happy.txt b/1 File handle/File handle text/happy.txt
deleted file mode 100644
index c5ca39434bb..00000000000
--- a/1 File handle/File handle text/happy.txt
+++ /dev/null
@@ -1,11 +0,0 @@
-hello how are you
-what is your name
-do not worry everything is alright
-everything will be alright
-please don't lose hope
-Wonders are on the way
-Take a walk in the park
-At the end of the day you are more important than anything else.
-Many moments of happiness are waiting
-You are amazing!
-If you truly believe.
\ No newline at end of file
diff --git a/1 File handle/File handle text/input,output and error streams.py b/1 File handle/File handle text/input,output and error streams.py
deleted file mode 100644
index 65c7b4462bc..00000000000
--- a/1 File handle/File handle text/input,output and error streams.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# practicing with streams
-import sys
-
-sys.stdout.write("Enter the name of the file")
-file = sys.stdin.readline()
-
-with open(
- file.strip(),
-) as F:
- while True:
- ch = F.readlines()
- for i in ch: # ch is the whole file,for i in ch gives lines, for j in i gives letters,for j in i.split gives words
- print(i, end="")
- else:
- sys.stderr.write("End of file reached")
- break
diff --git a/1 File handle/File handle text/question 2.py b/1 File handle/File handle text/question 2.py
deleted file mode 100644
index b369b564373..00000000000
--- a/1 File handle/File handle text/question 2.py
+++ /dev/null
@@ -1,32 +0,0 @@
-"""Write a method/function DISPLAYWORDS() in python to read lines
- from a text file STORY.TXT,
- using read function
-and display those words, which are less than 4 characters."""
-
-print("Hey!! You can print the word which are less then 4 characters")
-
-
-def display_words(file_path):
- try:
- with open(file_path) as F:
- words = F.read().split()
- words_less_than_40 = list(filter(lambda word: len(word) < 4, words))
-
- for word in words_less_than_40:
- print(word)
-
- return (
- "The total number of the word's count which has less than 4 characters",
- (len(words_less_than_40)),
- )
-
- except FileNotFoundError:
- print("File not found")
-
-
-print("Just need to pass the path of your file..")
-
-file_path = input("Please, Enter file path: ")
-
-if __name__ == "__main__":
- print(display_words(file_path))
diff --git a/1 File handle/File handle text/question 5.py b/1 File handle/File handle text/question 5.py
deleted file mode 100644
index de03fbb81fd..00000000000
--- a/1 File handle/File handle text/question 5.py
+++ /dev/null
@@ -1,40 +0,0 @@
-"""Write a function in python to count the number of lowercase
-alphabets present in a text file “happy.txt"""
-
-import time
-import os
-from counter import Counter
-
-print(
- "You will see the count of lowercase, uppercase and total count of alphabets in provided file.."
-)
-
-
-file_path = input("Please, Enter file path: ")
-
-if os.path.exists(file_path):
- print("The file exists and this is the path:\n", file_path)
-
-
-def lowercase(file_path):
- try:
- with open(file_path) as F:
- word_counter = Counter(F.read())
-
- print(
- f"The total number of lower case letters are {word_counter.get_total_lower()}"
- )
- time.sleep(0.5)
- print(
- f"The total number of upper case letters are {word_counter.get_total_upper()}"
- )
- time.sleep(0.5)
- print(f"The total number of letters are {word_counter.get_total()}")
- time.sleep(0.5)
-
- except FileNotFoundError:
- print("File is not exist.. Please check AGAIN")
-
-
-if __name__ == "__main__":
- lowercase(file_path)
diff --git a/1 File handle/File handle text/question 6.py b/1 File handle/File handle text/question 6.py
deleted file mode 100644
index a942d9db5c6..00000000000
--- a/1 File handle/File handle text/question 6.py
+++ /dev/null
@@ -1,21 +0,0 @@
-"""Write a function in python to count the number of lowercase
-alphabets present in a text file “happy.txt”"""
-
-from counter import Counter
-
-
-def lowercase():
- with open("happy.txt") as F:
- word_counter = Counter(F.read())
-
- print(
- f"The total number of lower case letters are {word_counter.get_total_lower()}"
- )
- print(
- f"The total number of upper case letters are {word_counter.get_total_upper()}"
- )
- print(f"The total number of letters are {word_counter.get_total()}")
-
-
-if __name__ == "__main__":
- lowercase()
diff --git a/1 File handle/File handle text/question3.py b/1 File handle/File handle text/question3.py
deleted file mode 100644
index 924a178638b..00000000000
--- a/1 File handle/File handle text/question3.py
+++ /dev/null
@@ -1,47 +0,0 @@
-"""Write a user-defined function named count() that will read
-the contents of text file named “happy.txt” and count
-the number of lines which starts with either “I‟ or “M‟."""
-
-import os
-import time
-
-file_name = input("Enter the file name to create:- ")
-
-# step1:
-print(file_name)
-
-
-def write_to_file(file_name):
- if os.path.exists(file_name):
- print(f"Error: {file_name} already exists.")
-
- else:
- with open(file_name, "a") as F:
- while True:
- text = input("enter any text")
- F.write(f"{text}\n")
-
- if input("do you want to enter more, y/n").lower() == "n":
- break
-
-
-# step2:
-def check_first_letter():
- with open(file_name) as F:
- lines = F.read().split()
-
- # store all starting letters from each line in one string after converting to lower case
- first_letters = "".join([line[0].lower() for line in lines])
-
- count_i = first_letters.count("i")
- count_m = first_letters.count("m")
-
- print(
- f"The total number of sentences starting with I or M are {count_i + count_m}"
- )
-
-
-if __name__ == "__main__":
- write_to_file(file_name)
- time.sleep(1)
- check_first_letter()
diff --git a/1 File handle/File handle text/special symbol after word.py b/1 File handle/File handle text/special symbol after word.py
deleted file mode 100644
index 1e23af6bddb..00000000000
--- a/1 File handle/File handle text/special symbol after word.py
+++ /dev/null
@@ -1,11 +0,0 @@
-with open("happy.txt", "r") as F:
- # method 1
- for i in F.read().split():
- print(i, "*", end="")
- print("\n")
-
- # method 2
- F.seek(0)
- for line in F.readlines():
- for word in line.split():
- print(word, "*", end="")
diff --git a/1 File handle/File handle text/story.txt b/1 File handle/File handle text/story.txt
deleted file mode 100644
index eb78dae4be8..00000000000
--- a/1 File handle/File handle text/story.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-once upon a time there was a king.
-he was powerful and happy.
-he
-all the flowers in his garden were beautiful.
-he lived happily ever after.
\ No newline at end of file
diff --git a/async_downloader/async_downloader.py b/async_downloader/async_downloader.py
index 4f715048905..cf1c31ec587 100644
--- a/async_downloader/async_downloader.py
+++ b/async_downloader/async_downloader.py
@@ -114,6 +114,10 @@ def test():
"https://www.ya.ru",
"https://www.duckduckgo.com",
"https://www.fail-path.unknown",
+ "https://www.google.com"
+ "https://www.pornhub.com",
+ "https://www.pornhub.org"
+ "https://www.xhamster.com",
]
download(ways)
diff --git a/async_downloader/www.duckduckgo.com b/async_downloader/www.duckduckgo.com
new file mode 100644
index 00000000000..e3bb829627e
--- /dev/null
+++ b/async_downloader/www.duckduckgo.com
@@ -0,0 +1 @@
+DuckDuckGo - Protection. Privacy. Peace of mind.
\ No newline at end of file
diff --git a/async_downloader/www.ya.ru b/async_downloader/www.ya.ru
new file mode 100644
index 00000000000..19ca248cb28
--- /dev/null
+++ b/async_downloader/www.ya.ru
@@ -0,0 +1,7 @@
+
+
+
+
+
+
+Вы не робот?