Trend Following Strategy with KNN
### 1. Strategy Features
This strategy combines the K-Nearest Neighbors (KNN) algorithm with a trend-following strategy to predict future price movements by analyzing historical price data. Here are the main features of the strategy:
1. **Dynamic Parameter Adjustment**: Uses the KNN algorithm to dynamically adjust parameters of the trend-following strategy, such as moving average length and channel length, to adapt to market changes.
2. **Trend Following**: Captures market trends using moving averages and price channels to generate buy and sell signals.
3. **Multi-Factor Analysis**: Combines the KNN algorithm with moving averages to comprehensively analyze the impact of multiple factors, improving the accuracy of trading signals.
4. **High Adaptability**: Automatically adjusts parameters using the KNN algorithm, allowing the strategy to adapt to different market environments and asset types.
### 2. Simple Introduction to the KNN Algorithm
The K-Nearest Neighbors (KNN) algorithm is a simple and intuitive machine learning algorithm primarily used for classification and regression problems. Here are the basic concepts of the KNN algorithm:
1. **Non-Parametric Model**: KNN is a non-parametric algorithm, meaning it does not make any assumptions about the data distribution. Instead, it directly uses training data for predictions.
2. **Instance-Based Learning**: KNN is an instance-based learning method that uses training data directly for predictions, rather than generating a model through a training process.
3. **Distance Metrics**: The core of the KNN algorithm is calculating the distance between data points. Common distance metrics include Euclidean distance, Manhattan distance, and Minkowski distance.
4. **Neighbor Selection**: For each test data point, the KNN algorithm finds the K nearest neighbors in the training dataset.
5. **Classification and Regression**: In classification problems, KNN determines the class of a test data point through a voting mechanism. In regression problems, KNN predicts the value of a test data point by calculating the average of the K nearest neighbors.
### 3. Applications of the KNN Algorithm in Quantitative Trading Strategies
The KNN algorithm can be applied to various quantitative trading strategies. Here are some common use cases:
1. **Trend-Following Strategies**: KNN can be used to identify market trends, helping traders capture the beginning and end of trends.
2. **Mean Reversion Strategies**: In mean reversion strategies, KNN can be used to identify price deviations from the mean.
3. **Arbitrage Strategies**: In arbitrage strategies, KNN can be used to identify price discrepancies between different markets or assets.
4. **High-Frequency Trading Strategies**: In high-frequency trading strategies, KNN can be used to quickly identify market anomalies, such as price spikes or volume anomalies.
5. **Event-Driven Strategies**: In event-driven strategies, KNN can be used to identify the impact of market events.
6. **Multi-Factor Strategies**: In multi-factor strategies, KNN can be used to comprehensively analyze the impact of multiple factors.
### 4. Final Considerations
1. **Computational Efficiency**: The KNN algorithm may face computational efficiency issues with large datasets, especially in real-time trading. Optimize the code to reduce access to historical data and improve computational efficiency.
2. **Parameter Selection**: The choice of K value significantly affects the performance of the KNN algorithm. Use cross-validation or other methods to select the optimal K value.
3. **Data Standardization**: KNN is sensitive to data standardization and feature selection. Standardize the data to ensure equal weighting of different features.
4. **Noisy Data**: KNN is sensitive to noisy data, which can lead to overfitting. Preprocess the data to remove noise.
5. **Market Environment**: The effectiveness of the KNN algorithm may be influenced by market conditions. Combine it with other technical indicators and fundamental analysis to enhance the robustness of the strategy.
Educational
Global Index Spread RSI StrategyThis strategy leverages the relative strength index (RSI) to monitor the price spread between a global benchmark index (such as AMEX) and the currently opened asset in the chart window. By calculating the spread between these two, the strategy uses RSI to identify oversold and overbought conditions to trigger buy and sell signals.
Key Components:
Global Benchmark Index: The strategy compares the current asset with a predefined global index (e.g., AMEX) to measure relative performance. The choice of a global benchmark allows the trader to analyze the current asset's movement in the context of broader market trends.
Spread Calculation:
The spread is calculated as the percentage difference between the current asset's closing price and the global benchmark index's closing price:
Spread=Current Asset Close−Global Index CloseGlobal Index Close×100
Spread=Global Index CloseCurrent Asset Close−Global Index Close×100
This metric provides a measure of how the current asset is performing relative to the global index. A positive spread indicates the asset is outperforming the benchmark, while a negative spread signals underperformance.
RSI of the Spread: The RSI is then calculated on the spread values. The RSI is a momentum oscillator that ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions in asset prices. An RSI below 30 is considered oversold, indicating a potential buying opportunity, while an RSI above 70 is overbought, suggesting that the asset may be due for a pullback.
Strategy Logic:
Entry Condition: The strategy enters a long position when the RSI of the spread falls below the oversold threshold (default 30). This suggests that the asset may have been oversold relative to the global benchmark and might be due for a reversal.
Exit Condition: The strategy exits the long position when the RSI of the spread rises above the overbought threshold (default 70), indicating that the asset may have become overbought and a price correction is likely.
Visual Reference:
The RSI of the spread is plotted on the chart for visual reference, making it easier for traders to monitor the relative strength of the asset in relation to the global benchmark.
Overbought and oversold levels are also drawn as horizontal reference lines (70 and 30), along with a neutral level at 50 to show market equilibrium.
Theoretical Basis:
The strategy is built on the mean reversion principle, which suggests that asset prices tend to revert to a long-term average over time. When prices move too far from this mean—either being overbought or oversold—they are likely to correct back toward equilibrium. By using RSI to identify these extremes, the strategy aims to profit from price reversals.
Mean Reversion: According to financial theory, asset prices oscillate around a long-term average, and any extreme deviation (overbought or oversold conditions) presents opportunities for price corrections (Poterba & Summers, 1988).
Momentum Indicators (RSI): The RSI is widely used in technical analysis to measure the momentum of an asset. Its application to the spread between the asset and a global benchmark allows for a more nuanced view of relative performance and potential turning points in the asset's price trajectory.
Practical Application:
This strategy works best in markets where relative strength is a key factor in decision-making, such as in equity indices, commodities, or forex markets. By assessing the performance of the asset relative to a global benchmark and utilizing RSI to identify extremes in price movements, the strategy helps traders to make more informed decisions based on potential mean reversion points.
While the "Global Index Spread RSI Strategy" offers a method for identifying potential price reversals based on relative strength and oversold/overbought conditions, it is important to recognize that no strategy is foolproof. The strategy assumes that the historical relationship between the asset and the global benchmark will hold in the future, but financial markets are subject to a wide array of unpredictable factors that can lead to sudden changes in price behavior.
Risk of False Signals:
The strategy relies heavily on the RSI to trigger buy and sell signals. However, like any momentum-based indicator, RSI can generate false signals, particularly in highly volatile or trending markets. In such conditions, the strategy may enter positions too early or exit too late, leading to potential losses.
Market Context:
The strategy may not account for macroeconomic events, news, or other market forces that could cause sudden shifts in asset prices. External factors, such as geopolitical developments, monetary policy changes, or financial crises, can cause a divergence between the asset and the global benchmark, leading to incorrect conclusions from the strategy.
Overfitting Risk:
As with any strategy that uses historical data to make decisions, there is a risk of overfitting the model to past performance. This could result in a strategy that works well on historical data but performs poorly in live trading conditions due to changes in market dynamics.
Execution Risks:
The strategy does not account for slippage, transaction costs, or liquidity issues, which can impact the execution of trades in real-market conditions. In fast-moving markets, prices may move significantly between order placement and execution, leading to worse-than-expected entry or exit prices.
No Guarantee of Profit:
Past performance is not necessarily indicative of future results. The strategy should be used with caution, and risk management techniques (such as stop losses and position sizing) should always be implemented to protect against significant losses.
Traders should thoroughly test and adapt the strategy in a simulated environment before applying it to live trades, and consider seeking professional advice to ensure that their trading activities align with their risk tolerance and financial goals.
References:
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Global vs National Index Spread RSIThe Global vs National Index Spread RSI indicator visualizes the relative strength of national stock indices compared to a global benchmark (e.g., AMEX). It calculates the percentage spread between the closing prices of each national index and the global index, applying the Relative Strength Index (RSI) to each spread.
How It Works
Spread Calculation: The spread represents the percentage difference between a national index and the global index.
RSI Application: RSI is applied to these spreads to identify overbought or oversold conditions in the relative performance of the national indices.
Reference Lines: Overbought (70), oversold (30), and neutral (50) levels help guide interpretation.
Insights from Research
The correlation between global and national indices provides insights into market integration and interdependence. Studies such as Forbes & Rigobon (2002) emphasize the importance of understanding these linkages during periods of financial contagion. Observing spread trends with RSI can aid in identifying shifts in investor sentiment and regional performance anomalies.
Use Cases
- Detect divergences between national and global markets.
- Identify overbought or oversold conditions for specific indices.
- Complement portfolio management strategies by monitoring geographic performance.
References
Forbes, K. J., & Rigobon, R. (2002). "No contagion, only interdependence: Measuring stock market co-movements." Journal of Finance.
Eun, C. S., & Shim, S. (1989). "International transmission of stock market movements." Journal of Financial and Quantitative Analysis.
GoldenTradz EMA+SMA Insight Multi Timeframe - [TilakBala]GoldenTradz EMA+SMA Insight Multi-Timeframe
📊 Indicator By: TilakBala from GoldenTradz — Revolutionize your trading approach with precision and insight!
Unlock the full potential of moving averages with the GoldenTradz EMA+SMA Insight indicator. This feature-packed tool combines the strength of Exponential Moving Averages (EMA) and Simple Moving Averages (SMA), offering unmatched flexibility and clarity for traders. Whether you're a beginner or a pro, this indicator empowers you to make well-informed trading decisions across multiple timeframes.
Key Features & Advantages:
Multi-Timeframe Analysis: Seamlessly analyze market trends using EMAs and SMAs from different timeframes on a single chart.
Gain a broader perspective by comparing short-term and long-term trends.
Customizable Settings:
Adjust EMA and SMA lengths, sources, and timeframes to fit your trading strategy perfectly.
Enable or disable specific moving averages for a clutter-free chart view.
Enhanced Trend Detection:
Identify bullish and bearish trends quickly using visually distinct EMAs and SMAs.
Use shorter EMAs for faster signals and longer SMAs for reliable trend confirmation.
Overlay Design:
Plots moving averages directly on the price chart for effortless analysis.
Distinct colors and line thicknesses ensure clear identification of each moving average.
Versatile Applications:
Suitable for scalping, day trading, swing trading, and long-term investments.
Works flawlessly with stocks, forex, cryptocurrencies, commodities, indices, and more.
Decision-Making Support:
Crossovers between EMAs and SMAs help identify potential buy or sell opportunities.
Monitor key support and resistance levels dynamically.
Efficiency in Market Noise:
EMAs provide rapid responsiveness in volatile markets.
SMAs help smooth out market noise for clearer long-term trends.
Adaptable to Any Strategy:
Perfect for breakout, trend-following, and mean-reversion strategies.
Combine with other indicators for a comprehensive trading system.
User-Friendly:
Intuitive interface with clear input fields for quick setup.
Suitable for traders of all experience levels.
📊 Indicator By: TilakBala from GoldenTradz — Revolutionize your trading approach with precision and insight!
Transform your trading with GoldenTradz EMA+SMA Insight — the ultimate tool for trend and momentum analysis.
Gains and Drawdowns with Standard DeviationsThis “Gains and Drawdowns with Standard Deviations” indicator helps in analyzing and visualizing the percentage gains and drawdown phases of a market or asset relative to its historical range. By calculating gains from the lowest low and drawdowns from the highest high over a specified lookback period, this indicator provides deeper insights into price movements and risk.
Key Features and Applications:
1. Gain and Drawdown Calculation:
• Gains: The indicator calculates the percentage gain from the lowest price point within a specific lookback period (e.g., 250 days).
• Drawdowns: Drawdowns are calculated as the percentage change from the highest point in the same period. This helps in identifying the maximum loss phases.
2. Standard Deviation:
• The indicator computes the standard deviation of both gains and drawdowns over a specified period (e.g., 250 days), allowing you to quantify volatility.
• Three bands (1st, 2nd, and 3rd standard deviations) are plotted for both gains and drawdowns, representing the frequency and magnitude of price movements within the normal volatility range.
3. Extreme Movements Highlighting:
• The indicator highlights extreme gains and drawdowns when they exceed user-defined thresholds. This helps in identifying significant market events or turning points.
4. Customizable Thresholds:
• Users can adjust the thresholds for extreme gains and drawdowns, as well as the lookback period for calculating gains, drawdowns, and standard deviations, making the indicator highly adaptable to specific needs.
Application in Portfolio Management:
The use of standard deviation in portfolio management is essential for assessing the risk and volatility of a portfolio. According to Modern Portfolio Theory (MPT) by Harry Markowitz, diversification of assets in a portfolio helps to minimize overall risk (especially the standard deviation), while maximizing returns. The standard deviation of a portfolio measures the volatility of its returns, with higher standard deviation indicating higher risk.
Scientific Source: Markowitz, H. M. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Markowitz’s theory suggests that an optimized portfolio, by minimizing the standard deviation of returns and combining a diversified asset allocation, can achieve better risk-adjusted returns.
Conclusion:
This indicator is particularly useful for traders and portfolio managers who want to understand and visualize market risk and extreme events. By using gains, drawdowns, and volatility metrics, it allows for systematic monitoring and evaluation of price movements, leading to more informed decisions in trading or portfolio management. A comprehensive understanding of price behavior and volatility helps in optimizing risk management and making strategic market entries.
Key Features:
• Visualization of Gains and Drawdowns with color-coded highlights for extreme movements.
• Standard Deviation Calculations for detailed volatility analysis.
• Customizable Thresholds for identifying extreme market events.
This indicator is a valuable tool for analyzing market data from a scientific standpoint, improving risk management, and making data-driven decisions based on historical performance.
Buy When There's Blood in the Streets StrategyStatistical Analysis of Drawdowns in Stock Markets
Drawdowns, defined as the decline from a peak to a trough in asset prices, are an essential measure of risk and market dynamics. Their statistical properties provide insights into market behavior during extreme stress periods.
Distribution of Drawdowns: Research suggests that drawdowns follow a power-law distribution, implying that large drawdowns, while rare, are more frequent than expected under normal distributions (Sornette et al., 2003).
Impacts of Extreme Drawdowns: During significant drawdowns (e.g., financial crises), the average recovery time is significantly longer, highlighting market inefficiencies and behavioral biases. For example, the 2008 financial crisis led to a 57% drawdown in the S&P 500, requiring years to recover (Cont, 2001).
Using Standard Deviations: Drawdowns exceeding two or three standard deviations from their historical mean are often indicative of market overreaction or capitulation, creating contrarian investment opportunities (Taleb, 2007).
Behavioral Finance Perspective: Investors often exhibit panic-selling during drawdowns, leading to oversold conditions that can be exploited using statistical thresholds like standard deviations (Kahneman, 2011).
Practical Implications: Studies on mean reversion show that extreme drawdowns are frequently followed by periods of recovery, especially in equity markets. This underpins strategies that "buy the dip" under specific, statistically derived conditions (Jegadeesh & Titman, 1993).
References:
Sornette, D., & Johansen, A. (2003). Stock market crashes and endogenous dynamics.
Cont, R. (2001). Empirical properties of asset returns: stylized facts and statistical issues. Quantitative Finance.
Taleb, N. N. (2007). The Black Swan: The Impact of the Highly Improbable.
Kahneman, D. (2011). Thinking, Fast and Slow.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency.
Power Of 3 ICT 01 [TradingFinder] AMD ICT & SMC Accumulations🔵 Introduction
The ICT Power of 3 (PO3) strategy, developed by Michael J. Huddleston, known as the Inner Circle Trader, is a structured approach to analyzing daily market activity. This strategy divides the trading day into three distinct phases: Accumulation, Manipulation, and Distribution.
Each phase represents a unique market behavior influenced by institutional traders, offering a clear framework for retail traders to align their strategies with market movements.
Accumulation (19:00 - 01:00 EST) takes place during low-volatility hours, as institutional traders accumulate orders. Manipulation (01:00 - 07:00 EST) involves false breakouts and liquidity traps designed to mislead retail traders. Finally, Distribution (07:00 - 13:00 EST) represents the active phase where significant market movements occur as institutions distribute their positions in line with the broader trend.
This indicator is built upon the Power of 3 principles to provide traders with a practical and visual tool for identifying these key phases. By using clear color coding and precise time zones, the indicator highlights critical price levels, such as highs and lows, helping traders to better understand market dynamics and make more informed trading decisions.
Incorporating the ICT AMD setup into daily analysis enables traders to anticipate market behavior, spot high-probability trade setups, and gain deeper insights into institutional trading strategies. With its focus on time-based price action, this indicator simplifies complex market structures, offering an effective tool for traders of all levels.
🔵 How to Use
The ICT Power of 3 (PO3) indicator is designed to help traders analyze daily market movements by visually identifying the three key phases: Accumulation, Manipulation, and Distribution.
Here's how traders can effectively use the indicator :
🟣 Accumulation Phase (19:00 - 01:00 EST)
Purpose : Identify the range-bound activity where institutional players accumulate orders.
Trading Insight : Avoid placing trades during this phase, as price movements are typically limited. Instead, use this time to prepare for the potential direction of the market in the next phases.
🟣 Manipulation Phase (01:00 - 07:00 EST)
Purpose : Spot false breakouts and liquidity traps that mislead retail traders.
Trading Insight : Observe the market for price spikes beyond key support or resistance levels. These moves often reverse quickly, offering high-probability entry points in the opposite direction of the initial breakout.
🟣 Distribution Phase (07:00 - 13:00 EST)
Purpose : Detect the main price movement of the day, driven by institutional distribution.
Trading Insight : Enter trades in the direction of the trend established during this phase. Look for confirmations such as breakouts or strong directional moves that align with broader market sentiment
🔵 Settings
Show or Hide Phases :mDecide whether to display Accumulation, Manipulation, or Distribution.
Adjust the session times for each phase :
Accumulation: 1900-0100 EST
Manipulation: 0100-0700 EST
Distribution: 0700-1300 EST
Modify Visualization : Customize how the indicator looks by changing settings like colors and transparency.
🔵 Conclusion
The ICT Power of 3 (PO3) indicator is a powerful tool for traders seeking to understand and leverage market structure based on time and price dynamics. By visually highlighting the three key phases—Accumulation, Manipulation, and Distribution—this indicator simplifies the complex movements of institutional trading strategies.
With its customizable settings and clear representation of market behavior, the indicator is suitable for traders at all levels, helping them anticipate market trends and make more informed decisions.
Whether you're identifying entry points in the Accumulation phase, navigating false moves during Manipulation, or capitalizing on trends in the Distribution phase, this tool provides valuable insights to enhance your trading performance.
By integrating this indicator into your analysis, you can better align your strategies with institutional movements and improve your overall trading outcomes.
Forward Price Performance TableThis calculates the percentage price changes for three key timeframes:
1 week (5 trading days ago)
1 month (17 trading days ago),
3 months (45 trading days ago).
This is to show a forward looking performance based on earlier timeframes that traditionally used. This is the framework I team uses to calculate performance metrics.
NY Trading Session TrackerNY Trading Session Tracker
This indicator highlights the New York trading session (14:30–21:00 UTC) directly on your chart. It visually identifies the session with a customizable background color and optional labels marking the session’s open and close. For added clarity, the labels can display the precise open and close prices, formatted with commas and up to 4 decimal places. Perfect for intraday traders looking to focus on one of the most active market periods.
Features:
• Highlight the NY session with a customizable background.
• Optional session open/close labels.
• Display open/close prices with professional formatting.
• Fully customizable settings for labels and colors.
Streamline your trading workflow and focus on what matters with the NY Trading Session Tracker!
USDEGP Rate MultipleIndicator shows the ratio between USDEGP rate calculated using CIB GDR and the official rate. In no stress, value should be stable.
London USDEGP priceThis indicator calculates the hypothetical USDEGP price using CIB receipts price in London Stock Exchange and its price in EGX. Values are smoothed.
Sticky Note Pro: Customizable Trading ChecklistStay organized and disciplined with this customizable sticky note on your TradingView chart. Perfect for traders who want to keep essential trading reminders, checklists, or notes visible while analyzing the market.
### Features:
- **Customizable Templates**: Choose from a **Trading Checklist**, **Risk Management**, or **Custom** template.
- **Section Customization**: Tailor the titles and content for up to three sections:
- 📊 **Analysis**: Track trend direction and support/resistance levels.
- 💰 **Risk Management**: Ensure proper risk management with reminders for risk percentage and stop loss settings.
- 🧠 **Psychology**: Stay disciplined with reminders to stick to your plan and avoid overtrading.
- **Dynamic Content**: Add or hide sections based on your preference, with dynamic spacing and content formatting.
- **Visual Customization**: Change text and background colors, and adjust text size and line spacing for optimal visibility.
- **Chart Integration**: The sticky note is displayed on the top-right corner of your chart and updates with the most recent bar.
### Why Use This Indicator?
This tool helps you stay on track with your trading plan, offering reminders for analysis, risk management, and trading psychology, all in one convenient place. Customize it to fit your style, and never miss a key point during your trading sessions again.
GB Time and MinutesCalculates Goldbach Time using both hour + minute AND just the minute of the hour, with the option to toggle either one on/off
Dynamic Horizontal Lines
Gann levels (squares of 9) have multiple levels. Adding lines at all these levels would add too much noise on the chart.
This indicator adds horizontal lines as per the Gann levels (Squares of 9) closest to the days close.
The current indicator add horizontal lines at the Gann level closest to the days close. It also adds 4 lines above & below the closest Gann level
I have considered Gann levels from 1 to 10000. You can append the Gann levels based on your requirements.
Infinity Market Grid -AynetConcept
Imagine viewing the market as a dynamic grid where price, time, and momentum intersect to reveal infinite possibilities. This indicator leverages:
Grid-Based Market Flow: Visualizes price action as a grid with zones for:
Accumulation
Distribution
Breakout Expansion
Volatility Compression
Predictive Dynamic Layers:
Forecasts future price zones using historical volatility and momentum.
Tracks event probabilities like breakout, fakeout, and trend reversals.
Data Science Visuals:
Uses heatmap-style layers, moving waveforms, and price trajectory paths.
Interactive Alerts:
Real-time alerts for high-probability market events.
Marks critical zones for "buy," "sell," or "wait."
Key Features
Market Layers Grid:
Creates dynamic "boxes" around price using fractals and ATR-based volatility.
These boxes show potential future price zones and probabilities.
Volatility and Momentum Waves:
Overlay volatility oscillators and momentum bands for directional context.
Dynamic Heatmap Zones:
Colors the chart dynamically based on breakout probabilities and risk.
Price Path Prediction:
Tracks price trajectory as a moving "wave" across the grid.
How It Works
Grid Box Structure:
Upper and lower price levels are based on ATR (volatility) and plotted dynamically.
Dashed green/red lines show the grid for potential price expansion zones.
Heatmap Zones:
Colors the background based on probabilities:
Green: High breakout probability.
Blue: High consolidation probability.
Price Path Prediction:
Forecasts future price movements using momentum.
Plots these as a dynamic "wave" on the chart.
Momentum and Volatility Waves:
Shows the relationship between momentum and volatility as oscillating waves.
Helps identify when momentum exceeds volatility (potential breakouts).
Buy/Sell Signals:
Triggers when price approaches grid edges with strong momentum.
Provides alerts and visual markers.
Why Is It Revolutionary?
Grid and Wave Synergy:
Combines structural price zones (grid boxes) with real-time momentum and volatility waves.
Predictive Analytics:
Uses momentum-based forecasting to visualize what’s next, not just what’s happening.
Dynamic Heatmap:
Creates a living map of breakout/consolidation zones in real-time.
Scalable for Any Market:
Works seamlessly with forex, crypto, and stocks by adjusting the ATR multiplier and box length.
This indicator is not just a tool but a framework for understanding market dynamics at a deeper level. Let me know if you'd like to take it even further — for example, adding machine learning-inspired probability models or multi-timeframe analysis! 🚀
Cosmic Cycle Trader -AYNETThe "Cosmic Cycle Trader 🌌"
Here's a summarized breakdown of the code:
Inputs
Orbital Periods (Moving Averages):
User specifies moving average (MA) periods as a comma-separated string (e.g., "10,20,50,100").
Predefined colors for each MA are used.
Fibonacci Sphere Levels:
User specifies Fibonacci retracement levels as a string (e.g., "0.236,0.382,0.618,1.0").
Color customization for Fibonacci levels is included.
Gravitational Pull (Signal Thresholds):
Configurable thresholds (buy_pull and sell_pull) to define signal triggers.
Alerts can be toggled on or off.
Core Features
Helper Functions:
parse_floats: Converts a comma-separated string into an array of floating-point numbers.
parse_ints: Converts a comma-separated string into an array of integers.
Orbital Periods (Moving Averages):
Moving averages are calculated for the given periods using the ta.sma function.
Each MA is stored in an array and plotted on the chart with a unique color.
Fibonacci Spheres:
Fibonacci levels are calculated based on the high and low of the current bar.
These levels are plotted as circles, visually indicating key price zones.
Signals:
Buy Signal: Triggered when:
The price closes above the highest MA.
The price is between specific Fibonacci levels.
Sell Signal: Triggered when:
The price closes below the lowest MA.
The price is below specific Fibonacci levels.
Alerts:
Alerts are created for buy and sell signals.
Signals are also annotated on the chart with labels and shapes.
Visual Elements
Plots:
Moving averages are plotted with distinct colors and line widths.
Fibonacci spheres are plotted as circles with customizable transparency.
Shapes:
Triangles indicate buy (green) and sell (red) signals on the chart.
Labels:
Buy signals display a "🌕 Buy" label.
Sell signals display a "🌑 Sell" label.
Purpose
This indicator helps traders identify potential buy and sell zones based on:
Moving average trends (orbital periods).
Key Fibonacci retracement levels.
Configurable thresholds (gravitational pull).
This combination of technical analysis tools makes it a visually appealing and functional indicator for traders.
Fibonacci Rainbow Day Trade-AYNETSummary of the "Fibonacci Rainbow Day Trade"
This script dynamically calculates Fibonacci retracement levels based on the daily high and low and plots them as colorful lines on the chart. It is designed for day traders to visually identify potential support and resistance zones using Fibonacci levels.
Key Features:
Dynamic Fibonacci Levels:
Levels are calculated using the daily high (day_high) and low (day_low).
Default levels: 0, 0.236, 0.382, 0.5, 0.618, 0.786, 1.
These levels represent key areas where price is likely to react.
Colorful Rainbow Visualization:
Each Fibonacci level is represented by a unique color.
Colors are defined in a rainbow_colors array: red, orange, yellow, green, blue, purple, teal.
Customizable Inputs:
Users can modify the Fibonacci levels, line thickness (fibo_line_width), and whether to show labels.
Labels display the level percentage (e.g., 0.236) at their respective lines.
Optional Labels:
The script includes labels that annotate each Fibonacci level on the chart.
Labels are placed beside the corresponding lines for clarity.
Works on Any Timeframe:
Although the levels are based on the daily high/low, the script can be applied to any intraday timeframe.
Use Case:
Identify Support and Resistance Zones:
Watch for price reactions near Fibonacci levels to determine potential entry/exit points.
Dynamic Updates:
Fibonacci levels are updated daily, ensuring they remain relevant for intraday trading.
Custom Visualization:
Adjust levels, colors, and display options to suit your trading style.
Example Calculation:
Daily High: $120
Daily Low: $100
Fibonacci 0.618 Level: $100 + ($120 - $100) * 0.618 = $111.36
This script provides a visually appealing and effective way to incorporate Fibonacci levels into day trading strategies. 🌈
MA Rainbow-AYNETSummary of the "MA Rainbow"
The 200 MA Rainbow script creates a visually appealing representation of multiple moving averages (MAs) with varying lengths and colors to provide insights into price trends and market momentum.
Key Features:
Base Moving Average:
A starting point (ma_length, default 200) is used as the foundation for all other bands.
Rainbow Bands:
The script generates multiple moving averages (bands) with increasing lengths, spaced by a user-defined band_spacing multiplier.
The number of bands is controlled by rainbow_bands, allowing up to 7 bands.
Moving Average Types:
Users can select the MA type: Simple (SMA), Exponential (EMA), or Weighted (WMA).
Dynamic Colors:
Each band is assigned a unique color from a predefined rainbow palette, making the chart visually distinct.
Inputs for Customization:
ma_length: Adjust the base period of the moving average.
rainbow_bands: Set the number of bands to display.
band_spacing: Control the spread between bands.
How It Works:
Precomputing Bands:
Each band’s length is calculated based on the base length (ma_length) and a multiplier (band_spacing).
For example, if ma_length = 200 and band_spacing = 0.2, the lengths of the first 3 bands will be:
Band 1: 200
Band 2: 240
Band 3: 280
Global Plotting:
Each band’s moving average is precomputed using the selected type (SMA, EMA, or WMA).
Bands are plotted globally to avoid scope issues, ensuring compatibility with Pine Script rules.
Color Cycling:
Colors are assigned dynamically from a rainbow palette (red, orange, yellow, green, blue, purple, teal).
Use Case:
The 200 MA Rainbow helps traders:
Visualize market trends with multiple layers of moving averages.
Identify areas of support and resistance.
Gauge momentum through the spread and alignment of bands.
Customization:
Users can:
Change the base moving average length (ma_length).
Adjust the number of bands (rainbow_bands).
Control the spread between bands with band_spacing.
Select the moving average type (SMA, EMA, WMA).
Application:
Copy the script into the Pine Editor in TradingView.
Apply it to your chart to observe the Rainbow MA visualization.
Adjust inputs to match your trading style or strategy.
This script is a versatile tool for both beginner and advanced traders, providing a colorful way to track price trends and market conditions. 🌈
Dynamic Supply & Demand Zones- AYNETSummary of the Code: Dynamic Supply & Demand Zones
This Pine Script creates dynamic supply (resistance) and demand (support) zones on a chart by identifying the highest and lowest prices over a user-defined lookback period. It visualizes these zones with shaded regions and horizontal lines that dynamically adjust to price movements.
Key Features:
Dynamic Support Zone (Demand):
Calculated using the lowest price in the last lookback bars.
Creates a shaded region around this price, extended up and down by a user-defined zone width.
Horizontal lines clearly mark the top and bottom of the demand zone.
Dynamic Resistance Zone (Supply):
Calculated using the highest price in the last lookback bars.
Similarly, a shaded region and lines are drawn for this zone, representing supply.
Customizable Inputs:
lookback: Number of bars to calculate the highest and lowest prices.
zone_width: The buffer distance above/below the highest/lowest price to create the zone.
Colors: Separate color inputs for the fill and lines of support and resistance zones.
Dynamic Updates:
Both zones update automatically as new bars are added and the highest/lowest prices change.
Visual Representation:
The script uses plot to create shaded regions and line objects to draw horizontal boundaries.
How It Works:
Inputs:
The user provides a lookback period and zone_width.
Calculations:
Lowest price in the last lookback bars defines the support zone.
Highest price in the same period defines the resistance zone.
Plotting:
The zones are plotted with shaded regions and dynamic lines.
Use Case:
This indicator helps identify key price levels where supply (resistance) or demand (support) is likely to affect price movement.
Useful for traders who rely on support/resistance levels in their strategies.
Let me know if you'd like further enhancements or integrations! 😊
Dynamic Trend Lines-AYNETCode Summary: Dynamic Trend Lines
This code dynamically draws trend lines and labels based on swing highs and lows identified from historical price action.
Key Features
Swing Point Detection:
Uses the ta.pivothigh and ta.pivotlow functions to identify recent swing highs and swing lows based on a customizable lookback period.
Trend Lines:
Uptrend Line:
Draws a line connecting swing low points.
Colored in blue by default.
Downtrend Line:
Draws a line connecting swing high points.
Colored in red by default.
Lines dynamically adjust as new swing points are identified.
Labels:
Adds a circle-style label at each swing high and swing low.
Displays the price value of the swing point.
Labels have:
Green background for uptrends.
Red background for downtrends.
Customizable Inputs:
lookback: Sensitivity of swing point detection (higher value = fewer swings).
line_color_up and line_color_down: Colors for the trend lines.
label_bg_up and label_bg_down: Colors for the label backgrounds.
Auto Updates:
Trend lines and labels update dynamically as the chart progresses, ensuring they reflect the latest market conditions.
How It Works
Identify Swing Points:
Detects local highs and lows within the defined lookback period.
Draw Lines:
Uptrend lines are drawn from the most recent swing lows.
Downtrend lines are drawn from the most recent swing highs.
Add Labels:
Each swing point is labeled with its price value for easy reference.
Visual Output
Trend Lines:
Blue for uptrends, red for downtrends.
Labels:
Circular labels with price values:
Green for swing lows (uptrend points).
Red for swing highs (downtrend points).
Example Use Case
This script is useful for traders who want to:
Visually identify key trend lines based on swing highs and lows.
Understand the critical price points of market reversals.
Use labeled price points for informed trade decisions.
Let me know if you'd like any specific refinements! 😊
Solar Movement Gradient-AYNETSummary of the Solar Movement Gradient Indicator
This Pine Script creates a dynamic, colorful indicator inspired by solar movements. It uses a sinusoidal wave to plot oscillations over time with a rainbow-like gradient that changes based on the wave's position.
Key Features
Sinusoidal Wave:
A wave oscillates smoothly based on the bar index (time) or optionally influenced by price movements.
The wave’s amplitude, baseline, and wavelength can be customized.
Dynamic Colors:
A spectrum of seven colors (red, orange, yellow, green, blue, purple, pink) is used.
The color changes smoothly along with the wave, emulating a solar gradient.
Background Gradient:
An optional gradient fills the background with colors matching the wave, adding a visually pleasing effect.
Customizable Inputs
Gradient Speed:
Adjusts how fast the wave and colors change over time.
Amplitude & Wavelength:
Controls the height and smoothness of the wave.
Price Influence:
Allows the wave to react dynamically to price movements.
Background Gradient:
Toggles a colorful gradient in the chart’s background.
Use Case
This indicator is designed for visual appeal rather than trading signals. It enhances the chart with a dynamic and colorful representation, making it perfect for aesthetic customization.
Let me know if you need further refinements! 🌈✨
Stick Figure - AYNETKey Features
Customizable Inputs:
base_price: Sets the vertical position (price level) where the figure's feet are placed.
bar_offset: Adjusts the horizontal placement of the stick figure on the chart.
body_length, arm_length, leg_length, head_size: Control the proportions of the stick figure.
Stick Figure Components:
Head: A horizontal line to symbolize the head.
Body: A vertical line for the torso.
Arms: A horizontal line extending from the torso.
Legs: Two diagonal lines representing the legs.
Dynamic Positioning:
The stick figure can be moved along the chart using bar_offset (horizontal) and base_price (vertical).
How It Works
Head:
A horizontal line (line.new) is drawn above the torso using the specified head_size.
Body:
A vertical line connects the head to the base price (base_price).
Arms and Legs:
Arms are horizontal lines extending from the middle of the body.
Legs are diagonal lines extending from the bottom of the torso.
Error Handling:
All x1 and x2 parameters are converted to int using int() to comply with Pine Script's requirements.
Example Use Case
This script is purely for fun and visualization:
Create visual markers for specific price levels or events.
Customize the stick figure's proportions to make it more prominent on the chart.
Let me know if you'd like further refinements or additions! 😊
Rainbow Fisher - AYNETThe Rainbow Fisher Indicator is inspired by John Ehlers' work on the Fisher Transform, a tool designed to normalize price movements and highlight overbought and oversold conditions. This script combines Ehlers' Fisher Transform with a rainbow visualization for enhanced trend analysis.
Summary of the Code
Fisher Transform Calculation:
The indicator calculates the Fisher Transform based on normalized high-low price data (hl2), which emphasizes turning points in market trends.
Rainbow Visualization:
The Fisher line is dynamically colored using a rainbow gradient to visually represent the magnitude and direction of market movements.
Overbought/Oversold Levels:
Configurable horizontal lines mark thresholds (1.5 for overbought and -1.5 for oversold by default), helping traders identify extremes in price action.
Signal Labels:
Labels are displayed when the Fisher line crosses the overbought or oversold levels, providing clear visual cues for potential market reversals.
Acknowledgment:
This indicator is an homage to John Ehlers' groundbreaking work in digital signal processing for financial markets.
How to Use
Trend Reversal Detection:
Use the overbought and oversold levels to identify potential turning points in market trends.
Momentum Analysis:
Observe the rainbow-colored Fisher line for directional cues and the strength of price movements.
Customization
Adjust the Fisher Transform length to refine sensitivity.
Modify overbought/oversold levels to align with your trading strategy.
Enable or disable the rainbow effect for simplicity or added clarity.
Let me know if you’d like further refinements or additional features! 🌈
