TEMA OBOS Strategy 【Pakun】📌 Overview
The TEMA + OBOS Strategy is an advanced trading strategy that combines the Triple Exponential Moving Average (TEMA) for trend-following and the Overbought/Oversold (OBOS) indicator for trade filtering.
This strategy leverages TEMA crossovers to identify trends and applies OBOS as a filter to improve entry precision.
💡 Main Objectives
Clearly determine trend direction (using TEMA)
Filter out overbought and oversold conditions (using OBOS)
Implement dynamic risk management (using ATR-based TP/SL)
This strategy is suitable for a wide range of markets, including Forex, stocks, and cryptocurrencies, and is best applied on mid-timeframes such as 15-minute to 1-hour charts.
📌 Key Features
🔹 Trend Analysis with TEMA (Triple EMA)
Generates entry signals based on ema3 and ema4 crossovers
Uses ema4 as the primary trend filter
🔹 Entry Optimization with OBOS (Overbought/Oversold Indicator)
up > down → Buy entry, up < down → Sell entry
Filters out excessive buying and selling conditions to improve accuracy
🔹 Take Profit & Stop Loss Based on ATR
ATR (Average True Range) multiplier is adjustable
Fully customizable TP/SL settings (default: TP = ATR × 2, SL = ATR × 1.5)
🔹 Customizable Parameters
TEMA Length (TEMA calculation period)
OBOS Length (OBOS calculation period)
Take Profit Multiplier (TP ratio)
Stop Loss Multiplier (SL ratio)
Show EMA? (Enable/disable TEMA lines)
Barcolor? (Enable/disable candlestick coloring)
📌 Trading Parameters
This strategy follows these trading rules:
✅ Long Entry (Buy)
ema3 crosses above ema4 (Golden Cross)
OBOS indicator confirms up > down (uptrend confirmation)
A long position is executed
✅ Short Entry (Sell)
ema3 crosses below ema4 (Death Cross)
OBOS indicator confirms up < down (downtrend confirmation)
A short position is executed
✅ Take Profit (TP)
Profit is taken when the price reaches Entry Price + ATR × TP multiplier (default: 2.0)
✅ Stop Loss (SL)
Loss is cut when the price reaches Entry Price - ATR × SL multiplier (default: 1.5)
📌 TP/SL values are fully customizable, allowing traders to fine-tune risk management.
📌 Risk Management Parameters
This strategy emphasizes flexible position sizing and risk control:
💰 Account Size: $7000
📉 Commissions & Slippage: Assumes 0-94 pips commission and 1 pip slippage
⚖️ Order Size: 10% of equity (adjustable as needed)
These settings help balance risk and reward while protecting capital.
📌 Visual Aids for Clarity
This strategy provides clear visual cues on the chart:
📊 TEMA Lines (3 EMA Levels)
Color change (Blue/Orange) indicates trend direction
ema3 (short-term) and ema4 (long-term) crossover signals
📉 OBOS Histogram
Green → Bullish pressure
Red → Bearish pressure
Blue → Potential trend shift
🔹 Entry & Exit Markers
🔼 Long Entry (Blue arrow)
🔽 Short Entry (Red arrow)
✅ Take Profit / Stop Loss Levels
📌 Originality & Enhancements
This strategy is based on the indicators " l_lonthoff " and " jdmonto0 ", with several improvements and enhancements.
While utilizing the core concepts of these indicators, we have optimized entry accuracy, improved visual clarity, and enhanced risk management features.
📌 Improvements & Upgrades
✅ Enhanced Trend Identification with TEMA (Triple EMA)
Uses ema3 (short-term) and ema4 (long-term) to detect trend shifts earlier
Reduces false signals and enables better trend-following trades
✅ Improved Filtering with OBOS (Overbought/Oversold Indicator)
Inspired by "jdmonto0", this feature filters overbought/oversold conditions for smarter trade entries
Reduces unnecessary trades and minimizes risk exposure
✅ Dynamic Risk Management with ATR (Average True Range)
Uses volatility-adjusted TP/SL levels instead of fixed values
Fully customizable ATR-based TP/SL multipliers (Default: TP = ATR × 2, SL = ATR × 1.5)
✅ Visual Enhancements for Clarity
TEMA lines change color dynamically to indicate trend direction
OBOS histogram provides an intuitive view of buying and selling pressure
Entry & exit markers make trades easy to track on the chart
📌 Summary
📌 TEMA + OBOS Strategy combines trend analysis and oscillators to create a powerful yet simple trading system:
Identifies trend direction using TEMA (Triple EMA)
Filters out noise and overbought/oversold conditions using OBOS
Applies ATR-based TP/SL settings for dynamic risk management
💡 Recommended for:
✅ Traders who prefer trend-following strategies
✅ Those who want a systematic, rules-based trading approach
✅ Users looking for a customizable strategy with strong risk management
📌 Try it out and backtest the results! 🚀
💡 How to Upload to TradingView
Paste this description in the Pine Script editor's "Strategy Description" field
Save and publish the script
Ensure that the "Description" field is filled with this content
Share it with the TradingView community!
📌 This strategy provides a structured, risk-managed approach to trading. Try it out and see how it performs! 🔥
Quantitativetrading
Quantitative Breakout Bands (AIBitcoinTrend)Quantitative Breakout Bands (AIBitcoinTrend) is an advanced indicator designed to adapt to dynamic market conditions by utilizing a Kalman filter for real-time data analysis and trend detection. This innovative tool empowers traders to identify price breakouts, evaluate trends, and refine their trading strategies with precision.
👽 What Are Quantitative Breakout Bands, and Why Are They Unique?
Quantitative Breakout Bands combine advanced filtering techniques (Kalman Filters) with statistical measures such as mean absolute error (MAE) to create adaptive price bands. These bands adjust to market conditions dynamically, providing insights into volatility, trend strength, and breakout opportunities.
What sets this indicator apart is its ability to incorporate both position (price) and velocity (rate of price change) into its calculations, making it highly responsive yet smooth. This dual consideration ensures traders get reliable signals without excessive lag or noise.
👽 The Math Behind the Indicator
👾 Kalman Filter Estimation:
At the core of the indicator is the Kalman Filter, a recursive algorithm used to predict the next state of a system based on past observations. It incorporates two primary elements:
State Prediction: The indicator predicts future price (position) and velocity based on previous values.
Error Covariance Adjustment: The process and measurement noise parameters refine the prediction's accuracy by balancing smoothness and responsiveness.
👾 Breakout Bands Calculation:
The breakout bands are derived from the mean absolute error (MAE) of price deviations relative to the filtered trendline:
float upperBand = kalmanPrice + bandMultiplier * mae
float lowerBand = kalmanPrice - bandMultiplier * mae
The multiplier allows traders to adjust the sensitivity of the bands to market volatility.
👾 Slope-Based Trend Detection:
A weighted slope calculation measures the gradient of the filtered price over a configurable window. This slope determines whether the market is trending bullish, bearish, or neutral.
👾 Trailing Stop Mechanism:
The trailing stop employs the Average True Range (ATR) to calculate dynamic stop levels. This ensures positions are protected during volatile moves while minimizing premature exits.
👽 How It Adapts to Price Movements
Dynamic Noise Calibration: By adjusting process and measurement noise inputs, the indicator balances smoothness (to reduce noise) with responsiveness (to adapt to sharp price changes).
Trend Responsiveness: The Kalman Filter ensures that trend changes are quickly identified, while the slope calculation adds confirmation.
Volatility Sensitivity: The MAE-based bands expand and contract in response to changes in market volatility, making them ideal for breakout detection.
👽 How Traders Can Use the Indicator
👾 Breakout Detection:
Bullish Breakouts: When the price moves above the upper band, it signals a potential upward breakout.
Bearish Breakouts: When the price moves below the lower band, it signals a potential downward breakout.
The trailing stop feature offers a dynamic way to lock in profits or minimize losses during trending moves.
👾 Trend Confirmation:
The color-coded Kalman line and slope provide visual cues:
Bullish Trend: Positive slope, green line.
Bearish Trend: Negative slope, red line.
👽 Why It’s Useful for Traders
Dynamic and Adaptive: The indicator adjusts to changing market conditions, ensuring relevance across timeframes and asset classes.
Noise Reduction: The Kalman Filter smooths price data, eliminating false signals caused by short-term noise.
Comprehensive Insights: By combining breakout detection, trend analysis, and risk management, it offers a holistic trading tool.
👽 Indicator Settings
Process Noise (Position & Velocity): Adjusts filter responsiveness to price changes.
Measurement Noise: Defines expected price noise for smoother trend detection.
Slope Window: Configures the lookback for slope calculation.
Lookback Period for MAE: Defines the sensitivity of the bands to volatility.
Band Multiplier: Controls the band width.
ATR Multiplier: Adjusts the sensitivity of the trailing stop.
Line Width: Customizes the appearance of the trailing stop line.
Disclaimer: This indicator is designed for educational purposes and does not constitute financial advice. Please consult a qualified financial advisor before making investment decisions.
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.
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.
