Auto Fitting GARCH OscillatorOverview
The Auto Fitting GARCH Oscillator is a sophisticated volatility indicator that dynamically fits GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models to the price data. It optimizes the parameters of the GARCH model to provide a reliable measure of volatility, which is then normalized to fit within a 0-100 range, making it easy to interpret as an oscillator. This indicator helps traders identify periods of high and low volatility, which can be crucial for making informed trading decisions.
Key Features
Dynamic GARCH(p, q) Fitting: Automatically optimizes the GARCH model parameters for the best fit.
Volatility Oscillator: Normalizes the volatility measure to a 0-100 range, indicating overbought and oversold conditions.
Customizable Timeframes: Adapts to various chart timeframes, from intraday to monthly data.
Projected Volatility: Provides options for projecting future volatility based on the optimized GARCH model.
User-friendly Visualization: Displays the oscillator with clear overbought and oversold levels.
Concepts Underlying the Calculations
The indicator leverages the GARCH model, which is widely used in financial time series analysis to model volatility clustering. The GARCH model considers past variances and returns to predict future volatility. This indicator dynamically adjusts the p and q parameters of the GARCH model within a specified range to find the optimal fit, minimizing the sum of squared errors (SSE).
How It Works
Data Preparation: Calculates the logarithmic returns and lagged variances from the price data.
SSE Optimization: Iterates through different p and q values to find the best GARCH parameters that minimize the SSE.
GARCH Calculation: Uses the optimized parameters to calculate the GARCH-based volatility.
Normalization: Normalizes the calculated volatility to a 0-100 range to form an oscillator.
Visualization: Plots the oscillator with overbought (70) and oversold (30) levels for easy interpretation.
How Traders Can Use It
Volatility Analysis: Identify periods of high and low volatility to adjust trading strategies accordingly.
Overbought/Oversold Conditions: Use the oscillator levels to identify potential reversal points in the market.
Risk Management: Incorporate volatility measures into risk management strategies to avoid trades during highly volatile periods.
Projection: Use the projected volatility feature to anticipate future market conditions.
Example Usage Instructions
Add the Indicator: Apply the "Auto Fitting GARCH Oscillator" to your chart from the Pine Script editor or TradingView library.
Customize Parameters: Adjust the maxP and maxQ values to set the range for GARCH model optimization.
Select Data Type: Choose between "Projected Variance in %" or "Projected Deviation in %" based on your analysis preference.
Set Projection Periods: Use the perForward input to specify how many periods forward you want to project the volatility.
Interpret the Oscillator: Observe the oscillator line and the overbought/oversold levels to make informed trading decisions.
Quantitativetrading
GARCH Volatility Estimation - The Quant ScienceThe GARCH (Generalized Autoregressive Conditional Heteroskedasticity) model is a statistical model used to forecast the volatility of a financial asset. This model takes into account the fluctuations in volatility over time, recognizing that volatility can vary in a heteroskedastic (i.e., non-constant variance) manner and can be influenced by past events.
The general formula of the GARCH model is:
σ²(t) = ω + α * ε²(t-1) + β * σ²(t-1)
where:
σ²(t) is the conditional variance at time t (i.e., squared volatility)
ω is the constant term (intercept) representing the baseline level of volatility
α is the coefficient representing the impact of the squared lagged error term on the conditional variance
ε²(t-1) is the squared lagged error term at the previous time period
β is the coefficient representing the impact of the lagged conditional variance on the current conditional variance
In the context of financial forecasting, the GARCH model is used to estimate the future volatility of the asset.
HOW TO USE
This quantitative indicator is capable of estimating the probable future movements of volatility. When the GARCH increases in value, it means that the volatility of the asset will likely increase as well, and vice versa. The indicator displays the relationship of the GARCH (bright red) with the trend of historical volatility (dark red).
USER INTERFACE
Alpha: select the starting value of Alpha (default value is 0.10).
Beta: select the starting value of Beta (default value is 0.80).
Lenght: select the period for calculating values within the model such as EMA (Exponential Moving Average) and Historical Volatility (default set to 20).
Forecasting: select the forecasting period, the number of bars you want to visualize data ahead (default set to 30).
Design: customize the indicator with your preferred color and choose from different types of charts, managing the design settings.
GRID SPOT TRADING ALGORITHM - GRID BOT TRADING STRATEGYGRID SPOT TRADING ALGORITHM : LONG ONLY STRATEGY OPEN SOURCE
This is a long only strategy for spot assets.
HOW IT WORKS
Grid trading is a trading strategy where an investor creates a so-called "price grid". The basic idea of the strategy is to repeatedly buy at the pre-specified price and then wait for the price to rise above that level and then sell the position (and vice versa with shorting or hedging).
FEATURES
Grids: This algorithm has a total of 10 grids.
Take profit: The trader can increase or decrease the distance between the grids from the User Interface panel, the distance between one grid and another represents the take profit.
Management: The algorithm buys 10% of the capital every time the price breaks down a grid and sells during a rise to the next higher grid. The initial capital is invested in 10 sizes which represent 10% of the capital per trade.
Stop Loss: The algorithm knows no stop loss as long as it is not activated from the User Interface panel. By activating the stop loss from the User Interface panel the algorithm will insert a close condition on all trades which will be calculated from the last lower grid.
Trades: Trades are opened only if the price is within the grid. If the market leaves the grid the algorithm will not buy new positions or sell new positions.
Optimal market conditions: The favorable market for this algorithm is the sideways market.
LIMITATIONS OF THE MODEL
The trader must take into account that this is a static model. It only works perfectly well if the market is in a sideways phase and incurs heavy losses if the market takes a downward trend. The model is unusable for an uptrend. The trader must therefore carefully analyze the market where he intends to use this strategy, making sure that the price is in a sideways phase.
USES
Indispensable research and backtesting tool for those using bots for their investments. The algorithm produces a backtesting of the strategy for past history. It is used by professional traders to understand if this strategy has been profitable on a market and what parameters to use for bots using this strategy (Kucoin, Binance etc.).
If you would like to develop your own algorithm with customized conditions based on a grid strategy, please contact us.
If you need help in using this tool, please contact us without hesitation.
