Implement multi-timeframe support for indicators
- Enhanced the `UserIndicator` class to include an optional `timeframe` attribute for custom indicator timeframes. - Updated the `get_indicator_data` method in `MarketDataIntegrator` to fetch and calculate indicators based on the specified timeframe, ensuring proper data alignment and handling. - Modified the `ChartBuilder` to pass the correct DataFrame for plotting indicators with different timeframes. - Added UI elements in the indicator modal for selecting timeframes, improving user experience. - Updated relevant JSON templates to include the new `timeframe` field for all indicators. - Refactored the `prepare_chart_data` function to ensure it returns a DataFrame with a `DatetimeIndex` for consistent calculations. This commit enhances the flexibility and usability of the indicator system, allowing users to analyze data across various timeframes.
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# Technical Indicators Module
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The Technical Indicators module provides comprehensive technical analysis capabilities for the TCP Trading Platform. It's designed to handle sparse OHLCV data efficiently and integrates seamlessly with the platform's aggregation strategy.
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The Technical Indicators module provides a suite of common technical analysis tools. It is designed to work efficiently with pandas DataFrames, which is the standard data structure for time-series analysis in the TCP Trading Platform.
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## Overview
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The module implements five core technical indicators commonly used in trading:
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The module has been refactored to be **DataFrame-centric**. All calculation methods now expect a pandas DataFrame with a `DatetimeIndex` and the required OHLCV columns (`open`, `high`, `low`, `close`, `volume`). This change simplifies the data pipeline, improves performance through vectorization, and ensures consistency across the platform.
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- **Simple Moving Average (SMA)** - Average price over a specified period
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- **Exponential Moving Average (EMA)** - Weighted average giving more importance to recent prices
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- **Relative Strength Index (RSI)** - Momentum oscillator measuring speed and change of price movements
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- **Moving Average Convergence Divergence (MACD)** - Trend-following momentum indicator
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- **Bollinger Bands** - Volatility indicator with upper and lower bands around a moving average
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The module implements five core technical indicators:
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- **Simple Moving Average (SMA)**
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- **Exponential Moving Average (EMA)**
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- **Relative Strength Index (RSI)**
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- **Moving Average Convergence Divergence (MACD)**
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- **Bollinger Bands**
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## Key Features
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### Sparse Data Handling
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- **No Interpolation**: Preserves gaps in timestamp data without artificial interpolation
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- **Efficient Processing**: Uses pandas for vectorized calculations
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- **Right-Aligned Timestamps**: Follows the platform's aggregation strategy convention
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- **Robust Error Handling**: Gracefully handles insufficient data and edge cases
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### Performance Optimized
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- **Vectorized Calculations**: Leverages pandas and numpy for fast computation
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- **Batch Processing**: Calculate multiple indicators simultaneously
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- **Memory Efficient**: Processes data in chunks without excessive memory usage
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### Flexible Configuration
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- **JSON Configuration**: Define indicator parameters via configuration files
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- **Multiple Price Columns**: Calculate indicators on open, high, low, or close prices
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- **Custom Parameters**: Adjust periods, standard deviations, and other parameters
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- **Validation**: Built-in configuration validation
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- **DataFrame-Centric Design**: Operates directly on pandas DataFrames for performance and simplicity.
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- **Vectorized Calculations**: Leverages pandas and numpy for high-speed computation.
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- **Flexible `calculate` Method**: A single entry point for calculating any supported indicator by name.
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- **Standardized Output**: All methods return a DataFrame containing the calculated indicator values, indexed by timestamp.
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## Usage Examples
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### Basic Usage
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### Preparing the DataFrame
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Before you can calculate indicators, you need a properly formatted pandas DataFrame. The `prepare_chart_data` utility is the recommended way to create one from a list of candle dictionaries.
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```python
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from components.charts.utils import prepare_chart_data
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from data.common.indicators import TechnicalIndicators
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from data.common.data_types import OHLCVCandle
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# Initialize indicators calculator
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indicators = TechnicalIndicators()
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# Assume 'candles' is a list of OHLCV dictionaries from the database
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# candles = fetch_market_data(...)
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# Calculate Simple Moving Average
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sma_results = indicators.sma(candles, period=20)
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# Prepare the DataFrame
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df = prepare_chart_data(candles)
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# Calculate Exponential Moving Average
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ema_results = indicators.ema(candles, period=12)
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# Calculate RSI
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rsi_results = indicators.rsi(candles, period=14)
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# Calculate MACD
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macd_results = indicators.macd(candles, fast_period=12, slow_period=26, signal_period=9)
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# Calculate Bollinger Bands
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bb_results = indicators.bollinger_bands(candles, period=20, std_dev=2.0)
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# df is now ready for indicator calculations
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# It has a DatetimeIndex and the necessary OHLCV columns.
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```
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### Multiple Indicators
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### Basic Indicator Calculation
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Once you have a prepared DataFrame, you can calculate indicators directly.
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```python
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# Define configuration for multiple indicators
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config = {
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'sma_20': {'type': 'sma', 'period': 20},
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'sma_50': {'type': 'sma', 'period': 50},
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'ema_12': {'type': 'ema', 'period': 12},
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'rsi_14': {'type': 'rsi', 'period': 14},
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'macd': {'type': 'macd'},
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'bb_20': {'type': 'bollinger_bands', 'period': 20}
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}
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# Initialize the calculator
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indicators = TechnicalIndicators()
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# Calculate all indicators at once
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results = indicators.calculate_multiple_indicators(candles, config)
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# Calculate a Simple Moving Average
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sma_df = indicators.sma(df, period=20)
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# Access individual indicator results
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sma_20_values = results['sma_20']
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rsi_values = results['rsi_14']
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macd_values = results['macd']
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# Calculate an Exponential Moving Average
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ema_df = indicators.ema(df, period=12)
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# sma_df and ema_df are pandas DataFrames containing the results.
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```
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### Using the `calculate` Method
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The most flexible way to compute an indicator is with the `calculate` method, which accepts the indicator type as a string.
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```python
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# Calculate RSI using the generic method
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rsi_pkg = indicators.calculate('rsi', df, period=14)
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if rsi_pkg:
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rsi_df = rsi_pkg['data']
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# Calculate MACD with custom parameters
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macd_pkg = indicators.calculate('macd', df, fast_period=10, slow_period=30, signal_period=8)
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if macd_pkg:
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macd_df = macd_pkg['data']
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```
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### Using Different Price Columns
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```python
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# Calculate SMA on high prices instead of close
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sma_high = indicators.sma(candles, period=20, price_column='high')
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# Calculate EMA on low prices
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ema_low = indicators.ema(candles, period=12, price_column='low')
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# Calculate RSI on open prices
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rsi_open = indicators.rsi(candles, period=14, price_column='open')
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```
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### Default Configuration
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You can specify which price column (`open`, `high`, `low`, or `close`) to use for the calculation.
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```python
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from data.common.indicators import create_default_indicators_config
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# Calculate SMA on the 'high' price
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sma_high_df = indicators.sma(df, period=20, price_column='high')
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# Get default configuration
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default_config = create_default_indicators_config()
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# Calculate using defaults
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results = indicators.calculate_multiple_indicators(candles, default_config)
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# Calculate RSI on the 'open' price
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rsi_open_pkg = indicators.calculate('rsi', df, period=14, price_column='open')
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```
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## Indicator Details
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The following details the parameters and the columns returned in the result DataFrame for each indicator.
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### Simple Moving Average (SMA)
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Calculates the arithmetic mean of prices over a specified period.
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**Parameters:**
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- `period`: Number of periods (default: 20)
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- `price_column`: Price column to use (default: 'close')
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**Returns:**
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- `sma`: Simple moving average value
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- **Parameters**: `period` (int), `price_column` (str, default: 'close')
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- **Returned Columns**: `sma`
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### Exponential Moving Average (EMA)
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Calculates exponentially weighted moving average, giving more weight to recent prices.
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**Parameters:**
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- `period`: Number of periods (default: 20)
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- `price_column`: Price column to use (default: 'close')
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**Returns:**
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- `ema`: Exponential moving average value
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- **Parameters**: `period` (int), `price_column` (str, default: 'close')
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- **Returned Columns**: `ema`
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### Relative Strength Index (RSI)
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Momentum oscillator that measures the speed and change of price movements.
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**Parameters:**
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- `period`: Number of periods (default: 14)
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- `price_column`: Price column to use (default: 'close')
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**Returns:**
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- `rsi`: RSI value (0-100 range)
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- **Parameters**: `period` (int), `price_column` (str, default: 'close')
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- **Returned Columns**: `rsi`
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### MACD (Moving Average Convergence Divergence)
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Trend-following momentum indicator showing the relationship between two moving averages.
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**Parameters:**
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- `fast_period`: Fast EMA period (default: 12)
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- `slow_period`: Slow EMA period (default: 26)
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- `signal_period`: Signal line EMA period (default: 9)
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- `price_column`: Price column to use (default: 'close')
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**Returns:**
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- `macd`: MACD line (fast EMA - slow EMA)
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- `signal`: Signal line (EMA of MACD)
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- `histogram`: MACD histogram (MACD - Signal)
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- **Parameters**: `fast_period` (int), `slow_period` (int), `signal_period` (int), `price_column` (str, default: 'close')
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- **Returned Columns**: `macd`, `signal`, `histogram`
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### Bollinger Bands
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Volatility indicator consisting of a moving average and two standard deviation bands.
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- **Parameters**: `period` (int), `std_dev` (float), `price_column` (str, default: 'close')
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- **Returned Columns**: `upper_band`, `middle_band`, `lower_band`
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**Parameters:**
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- `period`: Number of periods for moving average (default: 20)
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- `std_dev`: Number of standard deviations (default: 2.0)
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- `price_column`: Price column to use (default: 'close')
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## Integration with the TCP Platform
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**Returns:**
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- `upper_band`: Upper Bollinger Band
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- `middle_band`: Middle band (SMA)
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- `lower_band`: Lower Bollinger Band
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- `bandwidth`: Band width relative to middle band
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- `percent_b`: %B indicator (position within bands)
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The refactored `TechnicalIndicators` module is now tightly integrated with the `ChartBuilder`, which handles all data preparation and calculation automatically when indicators are added to a chart. For custom analysis or strategy development, you can use the class directly as shown in the examples above. The key is to always start with a properly prepared DataFrame using `prepare_chart_data`.
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## Data Structures
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