Congrats on expressing higher Agency Additional details: https://www.perplexity.ai/search/elaborate-and-identify-what-in-tHQVVZQoQ3a4P4ovdYIksw#1 It seems like the concepts of jerk and snap are not used much in quant finance except as metaphors for higer order derivatives defined in the options market. The concepts are intriguing yet since the promoter discounts time as irrelevant, there is a logical incongruency. This is due to the whole basis of measuring the base of velocity involves a factor of time.
The code that was shared is a mean-reversion strategy, it does not implement jerk and snap I extensively use diagrams (artifacts in mermaid, plantUML, YAML, etc) in my coding efforts. It causes better outputs and when used as references for inputs, allows for better context management. While Sonnet 4's response sounds authoritative and confidence high (as all LLMs), when confronted with errors (that only a domain expert would know), they pivot and return an equally faulty yet confident reply. Best to take LLM responses with a grain-of-salt.
what is the question about jerk and jounce? i use them both in all my trading to identify sudden moves that can not be identified by eye or feel. this puts math to the task of discovering when it's safe to join the the directional move.
Key Modifications: Jerk and snap now directly influence the trading signal Added sensitivity parameters to control their impact Enhanced debugging to show derivative information Maintained the core mean reversion logic The strategy now: Calculates price derivatives Uses jerk to amplify signals during significant momentum changes Uses snap to further modify signal sensitivity Provides more detailed logging of derivative information Potential Use Cases: Detecting sudden price momentum shifts Adding sensitivity to mean reversion signals Identifying potential trend acceleration Code: using System;[/B] [B]using System.Collections.Generic; using System.Linq; using PowerLanguage.Function; namespace PowerLanguage.Strategy { [IOGMode(IOGMode.Enabled)] public class ctest1 : SignalObject { #region Strategy Parameters [Input] public int LookbackWindow { get; set; } [Input] public double SignalThreshold { get; set; } [Input] public double JerkSensitivity { get; set; } [Input] public double SnapSensitivity { get; set; } [Input] public bool UseJerkEnhancement { get; set; } #endregion #region Private Variables private List<double> priceHistory; private List<double> signalHistory; private List<double> velocityHistory; private List<double> accelerationHistory; private List<double> jerkHistory; private List<double> snapHistory; private double currentCompositeSignal; private double previousCompositeSignal; private int currentPosition; #endregion #region Constructor public ctest1(object _ctx) : base(_ctx) { // Default parameters LookbackWindow = 10; SignalThreshold = 0.005; JerkSensitivity = 0.1; SnapSensitivity = 0.05; UseJerkEnhancement = true; } #endregion #region Initialization protected override void Create() { // Initialize collections priceHistory = new List<double>(); signalHistory = new List<double>(); velocityHistory = new List<double>(); accelerationHistory = new List<double>(); jerkHistory = new List<double>(); snapHistory = new List<double>(); } #endregion #region Main Strategy Logic protected override void CalcBar() { // Update price history UpdatePriceHistory(); // Need minimum bars for calculations if (priceHistory.Count < 5) return; // Calculate velocity derivatives CalculateVelocityDerivatives(); // Generate composite signal with jerk and snap enhancement currentCompositeSignal = GenerateEnhancedCompositeSignal(); // Generate trading decision int tradingSignal = GenerateTradingDecision(); // Output debug information OutputDetailedDebugInfo(tradingSignal); // Maintain history size MaintainHistorySize(); } #endregion #region Enhanced Signal Generation private void CalculateVelocityDerivatives() { if (priceHistory.Count < 5) return; var recentPrices = TakeLast(priceHistory, Math.Min(priceHistory.Count, LookbackWindow)); // Velocity (1st derivative) var velocity = CalculateGradient(recentPrices); if (velocity.Count > 0) velocityHistory.Add(velocity[velocity.Count - 1]); // Acceleration (2nd derivative) if (velocityHistory.Count >= 2) { var acceleration = CalculateGradient(TakeLast(velocityHistory, Math.Min(velocityHistory.Count, 50))); if (acceleration.Count > 0) accelerationHistory.Add(acceleration[acceleration.Count - 1]); } // Jerk (3rd derivative) if (accelerationHistory.Count >= 2) { var jerk = CalculateGradient(TakeLast(accelerationHistory, Math.Min(accelerationHistory.Count, 30))); if (jerk.Count > 0) jerkHistory.Add(jerk[jerk.Count - 1]); } // Snap/Jounce (4th derivative) if (jerkHistory.Count >= 2) { var snap = CalculateGradient(TakeLast(jerkHistory, Math.Min(jerkHistory.Count, 20))); if (snap.Count > 0) snapHistory.Add(snap[snap.Count - 1]); } } private double GenerateEnhancedCompositeSignal() { var recentPrices = TakeLast(priceHistory, Math.Min(priceHistory.Count, LookbackWindow)); if (recentPrices.Count < 5) return 0; // Basic mean reversion signal double currentPrice = recentPrices[recentPrices.Count - 1]; double meanPrice = Average(recentPrices); double baseSignal = (currentPrice - meanPrice) / meanPrice; // Jerk and Snap Enhancement if (UseJerkEnhancement && jerkHistory.Count > 0 && snapHistory.Count > 0) { double lastJerk = Math.Abs(jerkHistory[jerkHistory.Count - 1]); double lastSnap = Math.Abs(snapHistory[snapHistory.Count - 1]); // Jerk enhancement logic if (lastJerk > JerkSensitivity) { // Amplify signal when jerk is significant baseSignal *= (1 + lastJerk); } // Snap additional modification if (lastSnap > SnapSensitivity) { // Further modify signal based on snap baseSignal *= (1 + lastSnap); } } return baseSignal; } #endregion #region Trading Decision private int GenerateTradingDecision() { // Basic mean reversion trading logic if (currentPosition == 0) { // Long entry when signal is negative (price below mean) if (currentCompositeSignal < -SignalThreshold) { Output.WriteLine($"LONG ENTRY: Signal {currentCompositeSignal:F6}"); return 1; } // Short entry when signal is positive (price above mean) else if (currentCompositeSignal > SignalThreshold) { Output.WriteLine($"SHORT ENTRY: Signal {currentCompositeSignal:F6}"); return -1; } } else { // Exit logic with jerk-enhanced sensitivity double exitThreshold = SignalThreshold * 2; // Long position exit if (currentPosition > 0 && currentCompositeSignal > exitThreshold) { Output.WriteLine($"EXIT LONG: Signal {currentCompositeSignal:F6}"); return -1; } // Short position exit if (currentPosition < 0 && currentCompositeSignal < -exitThreshold) { Output.WriteLine($"EXIT SHORT: Signal {currentCompositeSignal:F6}"); return 1; } } return 0; // Hold current position } #endregion #region Utility Functions private List<double> CalculateGradient(List<double> data) { var gradient = new List<double>(); if (data.Count < 2) return gradient; // Forward difference for first point gradient.Add(data[1] - data[0]); // Central difference for middle points for (int i = 1; i < data.Count - 1; i++) { gradient.Add((data[i + 1] - data[i - 1]) / 2.0); } // Backward difference for last point gradient.Add(data[data.Count - 1] - data[data.Count - 2]); return gradient; } private double Average(List<double> values) { return values.Count == 0 ? 0 : values.Average(); } private List<double> TakeLast(List<double> source, int count) { return source.Count <= count ? new List<double>(source) : source.Skip(source.Count - count).ToList(); } private void MaintainHistorySize() { const int maxHistorySize = 100; if (velocityHistory.Count > maxHistorySize) velocityHistory.RemoveAt(0); if (accelerationHistory.Count > maxHistorySize) accelerationHistory.RemoveAt(0); if (jerkHistory.Count > maxHistorySize) jerkHistory.RemoveAt(0); if (snapHistory.Count > maxHistorySize) snapHistory.RemoveAt(0); } private void OutputDetailedDebugInfo(int tradingSignal) { // Detailed logging of derivatives and signal if (Bars.CurrentBar % 5 == 0) { string signalDescription = tradingSignal switch { 1 => "LONG ENTRY", -1 => "SHORT ENTRY", 0 => "HOLD", _ => "NO ACTION" }; // Log derivatives if available string derivativesInfo = jerkHistory.Count > 0 && snapHistory.Count > 0 ? $"Jerk: {jerkHistory[jerkHistory.Count - 1]:F6}, " + $"Snap: {snapHistory[snapHistory.Count - 1]:F6}" : "Derivatives not yet calculated"; Output.WriteLine( $"Bar {Bars.CurrentBar}: " + $"Signal: {currentCompositeSignal:F6}, " + $"Action: {signalDescription}, " + $"{derivativesInfo}" ); } } #endregion } } ================= previous version comments below Overview This is a sophisticated trading strategy implemented in MultiCharts.NET, specifically designed for mean reversion trading with advanced signal generation and risk management techniques. Core Strategy Philosophy The strategy aims to: Identify price deviations from a mean Generate trading signals based on these deviations Implement strict risk and position management Track and analyze trading performance Key Components 1. Input Parameters csharp [Input] public int LookbackWindow { get; set; } // How many bars to analyze [Input] public double SignalThreshold { get; set; } // Sensitivity of trading signals [Input] public int PositionSize { get; set; } // Number of contracts/shares to trade [Input] public double TransactionCost { get; set; } // Cost per trade [Input] public double RiskFactor { get; set; } // Risk multiplier [Input] public bool EnableQuantum { get; set; } // Advanced signal generation flag [Input] public bool EnableBacterial { get; set; } // Alternative signal processing [Input] public bool EnableFractional { get; set; } // Fractional position sizing 2. Strategy Initialization In the constructor, the strategy sets default, somewhat aggressive parameters: Short lookback window (10 bars) Very sensitive signal threshold (0.005) Minimal position sizing Low transaction costs Maximum holding periods (12 bars max, 3 bars minimum) 3. Signal Generation Methodology The strategy uses a multi-layered signal generation approach: Price Derivatives Calculation The CalculateVelocityDerivatives() method computes: Velocity (1st derivative of price) Acceleration (2nd derivative) Jerk (3rd derivative) Snap (4th derivative) This allows for complex, multi-dimensional price movement analysis. Composite Signal Generation GenerateCompositeSignal() creates a mean reversion signal: Calculates the mean price over the lookback window Compares current price to mean price Generates a normalized signal: Positive signal → Price above mean (potential short) Negative signal → Price below mean (potential long) Example calculation: csharp double primarySignal = (currentPrice - meanPrice) / meanPrice; primarySignal = primarySignal * 10; // Amplify sensitivity 4. Trading Decision Logic GenerateTradingDecision() determines trade actions based on: Current position status Risk-adjusted signal Minimum/maximum holding periods Trade scenarios: Forced exit after max holding period Exit on strong signal reversal Long entry when price is below mean Short entry when price is above mean 5. Trade Execution ExecuteTrade() handles: Sending market orders Tracking position duration Logging trade details Updating trade counters 6. Performance Tracking UpdatePerformanceMetrics() monitors: Peak equity Maximum drawdown Trade count 7. Advanced Utility Functions Includes custom implementations of: Standard deviation calculation Mean calculation List manipulation Gradient/derivative calculation Unique Features Derivative-Based Signal Generation Unlike traditional moving average crossover strategies, this approach: Calculates multiple price derivatives Uses non-linear signal generation Adapts to price movement characteristics Risk Management Dynamic risk factor Position duration limits Forced exits Adaptive signal thresholds Debugging & Transparency Extensive logging and debug output: Prints detailed trade reasoning Shows signal components Tracks performance metrics Potential Use Cases Mean reversion trading Volatile market environments Stocks with mean-reverting tendencies Algorithmic trading research Limitations Assumes transaction costs are minimal Relies heavily on lookback window selection May overfit to specific market conditions Would you like me to elaborate on any specific aspect of the strategy? I can dive deeper into signal generation, risk management, or any other component. M how does it use jerk and jounce Great question! Let's dive deep into how this strategy calculates and potentially uses jerk and jounce (snap). Derivative Calculation Method csharp private void CalculateVelocityDerivatives() { if (priceHistory.Count < 5) return; var recentPrices = TakeLast(priceHistory, Math.Min(priceHistory.Count, LookbackWindow)); // Velocity (1st derivative) var velocity = CalculateGradient(recentPrices); if (velocity.Count > 0) velocityHistory.Add(velocity[velocity.Count - 1]); // Acceleration (2nd derivative) if (velocityHistory.Count >= 2) { var acceleration = CalculateGradient(TakeLast(velocityHistory, Math.Min(velocityHistory.Count, 50))); if (acceleration.Count > 0) accelerationHistory.Add(acceleration[acceleration.Count - 1]); } // Jerk (3rd derivative) if (accelerationHistory.Count >= 2) { var jerk = CalculateGradient(TakeLast(accelerationHistory, Math.Min(accelerationHistory.Count, 30))); if (jerk.Count > 0) jerkHistory.Add(jerk[jerk.Count - 1]); } // Snap/Jounce (4th derivative) if (jerkHistory.Count >= 2) { var snap = CalculateGradient(TakeLast(jerkHistory, Math.Min(jerkHistory.Count, 20))); if (snap.Count > 0) snapHistory.Add(snap[snap.Count - 1]); } } Gradient Calculation Method csharp private List<double> CalculateGradient(List<double> data) { var gradient = new List<double>(); if (data.Count < 2) return gradient; // Forward difference for first point gradient.Add(data[1] - data[0]); // Central difference for middle points for (int i = 1; i < data.Count - 1; i++) { gradient.Add((data[i + 1] - data[i - 1]) / 2.0); } // Backward difference for last point gradient.Add(data[data.Count - 1] - data[data.Count - 2]); return gradient; } Conceptual Breakdown Derivatives Explained: Velocity (1st derivative): Rate of price change How quickly price is moving up or down Positive = Price increasing Negative = Price decreasing Acceleration (2nd derivative): Rate of change of velocity How quickly the price movement is changing Positive = Velocity increasing Negative = Velocity decreasing Jerk (3rd derivative): Rate of change of acceleration How quickly acceleration is changing Indicates sudden changes in price momentum Measures "jerkiness" of price movement Snap/Jounce (4th derivative): Rate of change of jerk Extreme measure of price volatility Indicates rapid changes in momentum Potential Trading Insights While the current implementation doesn't explicitly use jerk and snap in trading decisions, they could be used for: Volatility Detection csharp // Hypothetical volatility metric double volatilityScore = Math.Abs(snapHistory.LastOrDefault()) * Math.Abs(jerkHistory.LastOrDefault()); Momentum Confirmation csharp // Example of using derivatives for signal confirmation bool strongMomentum = velocityHistory.LastOrDefault() > 0 && // Upward velocity accelerationHistory.LastOrDefault() > 0 && // Increasing velocity jerkHistory.LastOrDefault() > 0; // Accelerating momentum Trend Change Detection csharp bool trendReversalSignal = Math.Abs(snapHistory.LastOrDefault()) > significantThreshold && Math.Sign(jerkHistory.LastOrDefault()) != Math.Sign(previousJerk); Limitations in Current Implementation Derivatives are calculated but not directly used in trading logic No explicit thresholds for derivative-based signals Computational overhead of multiple derivative calculations Potential Enhancements Add derivative-based signal filters Create more complex momentum indicators Use derivatives for dynamic stop-loss placement Implement machine learning models using these features Code Example of Enhanced Usage csharp private bool IsDerivativeSignalStrong() { // Check if derivatives indicate strong momentum return velocityHistory.LastOrDefault() > 0 && accelerationHistory.LastOrDefault() > 0 && Math.Abs(jerkHistory.LastOrDefault()) > 0.5 && Math.Abs(snapHistory.LastOrDefault()) > 0.1; } private void ModifyTradingDecision(ref int tradingSignal) { // Modify existing trading signal based on derivatives if (IsDerivativeSignalStrong()) { // Strengthen existing signal tradingSignal *= 2; } } Would you like me to elaborate on any specific aspect of derivative calculation or potential trading applications? M does the system use jerk and jounce was the question' No, it doesn't. Looking at the code, while the system calculates jerk and jounce (snap) in the CalculateVelocityDerivatives() method, these derivatives are: Calculated Stored in jerkHistory and snapHistory NOT used in any trading decision logic The strategy primarily uses a simple mean reversion signal based on price deviation from the mean, as seen in the GenerateCompositeSignal() method: csharp double primarySignal = (currentPrice - meanPrice) / meanPrice; primarySignal = primarySignal * 10; // Amplify for sensitivity The jerk and jounce calculations appear to be placeholder or research code that isn't integrated into the actual trading logic. They're calculated but never referenced in trading decisions, signal generation, or risk management. Would you like me to explain why these might have been included, or how they could potentially be used in a trading strategy? M yes There are several potential reasons for including jerk and jounce calculations without using them: Research and Development Often, quantitative traders will implement multiple derivative calculations during strategy development These calculations serve as experimental features for future signal generation They provide additional data points that might reveal hidden market characteristics Machine Learning Preparation Derivatives like jerk and jounce can be valuable features for: Training predictive models Creating more complex signal generators Developing advanced machine learning trading algorithms Potential Future Integration The code suggests the developer was exploring: EnableQuantum EnableBacterial EnableFractional These flags hint at potential future implementations where these derivatives might be used. Academic/Theoretical Exploration In physics and advanced quantitative finance, higher-order derivatives can reveal: Sudden momentum changes Acceleration of price movement Complex market dynamics not visible in first-order calculations Example of how these could be integrated: csharp private double GenerateAdvancedSignal() { // Hypothetical advanced signal using derivatives double baseSignal = (currentPrice - meanPrice) / meanPrice; // Modify base signal with derivative information if (jerkHistory.Count > 0 && snapHistory.Count > 0) { double jerkFactor = Math.Abs(jerkHistory.Last()); double snapFactor = Math.Abs(snapHistory.Last()); // Amplify or dampen signal based on derivatives baseSignal *= (1 + jerkFactor * snapFactor); } return baseSignal; }
indeed. butnot for a while. ai is limited by the energy humans can produce. eventually we will hit a wall of energy demand and production demand. unless ai moves on from using gpus to other types of computing we will hit that wall really fast. where we might have laws limiting energy use by humans in order to run advanced ai processors to calculate something stupid for rich people and institutions. to make more money i know what they are thinking already because im thinking same thing(villains always thins same because all villains have same goal) i know they are gathering datapoints already to create a future predicting algorithm based on as many data points as they can gather. because at the end of the day the universe runs on code and the chain of events leads to certain outcomes. if you can gather enough data about the past you can predict the future. humans just were not able to graph and track enough data points to create a map of the past
Code: #!/usr/bin/perl use strict; use warnings; # Sample price data my @prices = (100, 101, 102, 103, 104, 105, 106, 107, 108, 109); # Function to calculate gradient (first derivative) sub calculate_gradient { my ($data_ref) = @_; my @data = @$data_ref; my @gradient; for (my $i = 1; $i < @data; $i++) { push @gradient, $data[$i] - $data[$i - 1]; } return \@gradient; } # Calculate velocity (first derivative) my $velocity = calculate_gradient(\@prices); # Calculate acceleration (second derivative) my $acceleration = calculate_gradient($velocity); # Calculate jerk (third derivative) my $jerk = calculate_gradient($acceleration); # Calculate snap (fourth derivative) my $snap = calculate_gradient($jerk); # Print results print "Velocity: @{$velocity}\n"; print "Acceleration: @{$acceleration}\n"; print "Jerk: @{$jerk}\n"; print "Snap: @{$snap}\n";
Snap and jerk sounds like some type of noise coding. Velocity and acceleration makes sense, but I can't understand snap and jerk being very relevent in understanding direction or prediction.
for very fast daytrading i use jounce the 4th derivative - it's very fast for detecting very fast movement. you have to be extremely fast with order execution when using this.