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Quantum Market Entanglement Problem [Re: TipmyPip] #488539
01/09/25 18:48
01/09/25 18:48
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The Puzzle of Currency Waves 🌊💸
Imagine you're standing at a beach and watching the waves. Each wave is like a currency pair (e.g., EUR/USD or GBP/JPY) in the financial market. Some waves are big, some are small, and they all move differently. But here’s the tricky part: these waves aren’t moving randomly—they’re connected! 🌐

Now, let’s pretend you’re a wave scientist. Your job is to figure out how these waves are connected and use that information to predict which wave will get bigger or smaller next. If you do it right, you can ride the perfect wave (make a smart trade) and avoid the ones that crash! 🏄‍♂️

The Finance Game 🎮
In the real world of finance, these "waves" are actually something called volatility. Volatility tells us how much the prices of currency pairs are changing. Sometimes prices jump up and down a lot (big waves), and other times they stay calm (small waves).

But here's the big mystery: what makes one wave affect another?

The Big Question 🧠
Let’s say we have 28 waves, one for each currency pair. If the EUR/USD wave gets bigger, does it make the GBP/JPY wave smaller? Or does it push all the waves to get bigger? Your job is to figure this out, just like a detective solving a mystery. 🕵️‍♀️

To solve this puzzle, you’ll use a magical tool called Relative Volatility Spread Similarity. It helps us measure how much one wave (currency pair) is different from another. For example:

If EUR/USD is very calm and GBP/JPY is very wild, they’re very different.
If both are wild, they’re similar.

Using the Magic Glasses 🔍✨
To make sense of all this, we use something like magic glasses called Enhanced PCA. It’s a way to focus on the most important parts of the puzzle and ignore the boring details.

Once we have the important pieces, we send them to a group of really smart robots called Graph Neural Networks (GNNs). These robots:

Look at how all the waves are connected.
Share their findings with each other.
Give us advice on whether to buy, sell, or hold currencies.

The Secret Sauce 🥣
But we don’t stop there! We check if the robots are working well by making them talk to each other. If two robots give very different advice, we ask, “Why are you so different?” and help them refine their answers. This is called Cross-Entropy Similarity, and it makes sure all the robots are working as a team.

The Goal 🎯
The goal of this whole game is to:

Predict the best waves (currency pairs) to ride.
Make money while keeping risks low (don’t fall off your surfboard!). 🏄‍♀️💰

Code
#define PAIRS 28
#define COMPONENTS 3     // Number of PCA components
#define GNN_LAYERS 2     // Number of GNN layers
#define ACTIONS 3        // Buy, Sell, Hold
#define LOOKBACK 100     // Lookback period for volatility calculation
#define VOL_KERNEL_WIDTH 0.5 // Width for Gaussian kernel
#define SIMILARITY_ALPHA 0.1 // Smoothing factor for similarity updates

// Declare currency pairs
string CurrencyPairs[PAIRS] = {
    "EURUSD", "GBPUSD", "USDJPY", "GBPJPY", "USDCAD", "EURAUD", "EURJPY",
    "AUDCAD", "AUDJPY", "AUDNZD", "AUDUSD", "CADJPY", "EURCAD", "EURCHF",
    "EURGBP", "EURNZD", "GBPCAD", "GBPCHF", "NZDCAD", "NZDJPY", "NZDUSD",
    "USDCHF", "CHFJPY", "AUDCHF", "GBPNZD", "NZDCHF", "CADCHF", "GBPAUD"
};

// Global Variables
vars kernelMatrix[PAIRS][PAIRS];            // Kernel matrix for PCA
vars pcaReducedFeatures[PAIRS][COMPONENTS]; // PCA-reduced features for each pair
vars adjacencyMatrices[PAIRS][PAIRS];       // Adjacency matrices for GNNs
vars gnnWeights[GNN_LAYERS][COMPONENTS][COMPONENTS]; // GNN weights
vars gnnOutputs[PAIRS][ACTIONS];            // GNN probabilities for Buy/Sell/Hold
vars similarityMatrix[PAIRS][PAIRS];        // Cross-Entropy similarity matrix
vars refinedOutputs[PAIRS][ACTIONS];        // Refined GNN probabilities
vars signals[PAIRS];                        // Final trading signals

// Utility Function: Calculate Rolling Volatility
function calcVolatility(string pair) {
    asset(pair);
    vars returns = series(log(priceClose() / priceClose(1)));
    return StdDev(returns, LOOKBACK);
}

// Step 1: Compute Kernel Matrix
function computeKernelMatrix() {
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < PAIRS; j++) {
            vars sigma_i = series(calcVolatility(CurrencyPairs[i]));
            vars sigma_j = series(calcVolatility(CurrencyPairs[j]));
            vars volatilitySpread = series(abs(sigma_i[0] - sigma_j[0]));
            kernelMatrix[i][j] = exp(-volatilitySpread[0]^2 / (2 * VOL_KERNEL_WIDTH^2));
        }
    }
}

// Step 2: Perform Enhanced PCA
function performEnhancedPCA() {
    eigenDecomposition(kernelMatrix, eigenvalues, eigenvectors);
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < COMPONENTS; j++) {
            pcaReducedFeatures[i][j] = dotProduct(kernelMatrix[i], eigenvectors[j]);
        }
    }
}

// Step 3: Initialize GNN Weights
function initializeGNNWeights() {
    for (int l = 0; l < GNN_LAYERS; l++) {
        for (int i = 0; i < COMPONENTS; i++) {
            for (int j = 0; j < COMPONENTS; j++) {
                gnnWeights[l][i][j] = random() * 0.1; // Small random initialization
            }
        }
    }
}

// Step 4: GNN Propagation
function propagateGNN() {
    vars tempFeatures[PAIRS][COMPONENTS];
    for (int l = 0; l < GNN_LAYERS; l++) {
        for (int i = 0; i < PAIRS; i++) {
            for (int k = 0; k < COMPONENTS; k++) {
                tempFeatures[i][k] = 0;
                for (int j = 0; j < PAIRS; j++) {
                    for (int m = 0; m < COMPONENTS; m++) {
                        tempFeatures[i][k] += adjacencyMatrices[i][j] * pcaReducedFeatures[j][m] * gnnWeights[l][m][k];
                    }
                }
                tempFeatures[i][k] = max(0, tempFeatures[i][k]); // ReLU activation
            }
        }
        // Update PCA features for the next layer
        for (int i = 0; i < PAIRS; i++) {
            for (int k = 0; k < COMPONENTS; k++) {
                pcaReducedFeatures[i][k] = tempFeatures[i][k];
            }
        }
    }
    // Generate probabilities (Buy/Sell/Hold) based on final GNN outputs
    for (int i = 0; i < PAIRS; i++) {
        for (int k = 0; k < ACTIONS; k++) {
            gnnOutputs[i][k] = random() * 0.1; // Placeholder for GNN probability outputs
        }
    }
}

// Step 5: Compute Cross-Entropy Similarity
function computeCrossEntropySimilarity() {
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < PAIRS; j++) {
            similarityMatrix[i][j] = 0;
            for (int k = 0; k < ACTIONS; k++) {
                similarityMatrix[i][j] -= gnnOutputs[i][k] * log(gnnOutputs[j][k] + 1e-8);
            }
        }
    }
}

// Step 6: Refine GNN Outputs Using Similarity
function refineGNNOutputs() {
    for (int i = 0; i < PAIRS; i++) {
        for (int k = 0; k < ACTIONS; k++) {
            refinedOutputs[i][k] = 0;
            double weightSum = 0;
            for (int j = 0; j < PAIRS; j++) {
                refinedOutputs[i][k] += similarityMatrix[i][j] * gnnOutputs[j][k];
                weightSum += similarityMatrix[i][j];
            }
            refinedOutputs[i][k] /= (weightSum + 1e-8); // Normalize
        }
    }
}

// Step 7: Generate Trading Signals
function generateSignals() {
    for (int i = 0; i < PAIRS; i++) {
        signals[i] = refinedOutputs[i][0] - refinedOutputs[i][1]; // Buy-Sell difference
    }
}

// Step 8: Execute Trades
function executeTrades() {
    for (int i = 0; i < PAIRS; i++) {
        if (signals[i] > 0) enterLong(CurrencyPairs[i]);
        else if (signals[i] < 0) enterShort(CurrencyPairs[i]);
    }
}

// Main Function
function run() {
    set(PLOTNOW);

    // Step 1: Compute Kernel Matrix
    computeKernelMatrix();

    // Step 2: Perform Enhanced PCA
    performEnhancedPCA();

    // Step 3: Initialize GNN weights
    initializeGNNWeights();

    // Step 4: Propagate GNN
    propagateGNN();

    // Step 5: Compute Cross-Entropy Similarity
    computeCrossEntropySimilarity();

    // Step 6: Refine GNN outputs
    refineGNNOutputs();

    // Step 7: Generate trading signals
    generateSignals();

    // Step 8: Execute trades
    executeTrades();
}

Attached Files
Last edited by TipmyPip; 01/09/25 18:48.
Stochastic Interdependent Volatility-Adaptive Signal [Re: TipmyPip] #488540
01/09/25 19:46
01/09/25 19:46
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Posts: 141
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Posts: 141
The Dynamic Exam Study Plan

Imagine you are part of a study group of 28 students (representing the 28 currency pairs), and each student has their own unique strengths and weaknesses in different subjects like Math, Science, and Literature (representing market volatility). Each student’s performance changes over time depending on how much they study, external distractions, and collaboration with others in the group (representing market dynamics and interdependencies). Your goal is to create a dynamic and adaptive study plan that helps the whole group excel in an upcoming exam, even though you have limited time and resources.

Key Elements of the Problem
Study Stress as Volatility:

Each student’s stress level represents their volatility. Some students get very stressed (high volatility), while others are calm and steady (low volatility).
Stress changes over time based on how difficult the subject is and how much preparation they’ve done recently (like rolling standard deviations of their past "study returns").
The Principal of PCA (Principal Component Analysis):

You notice that not all topics are equally hard for everyone. Some topics like Algebra (or "Eigenvalues" in our original problem) affect most students' stress levels more than others, such as Creative Writing.
To simplify the problem, you identify a few key topics that cause the most stress for the group. These topics act like "Principal Components," and focusing on them can explain most of the group’s challenges.
The GNN (Study Group Network):

Some students are better at helping others because they understand connections between topics well (like connecting Algebra to Physics). These connections form a "network" of study helpers.
The stronger the connections between two students (measured by how often they work together and help each other), the better the group performs.
Entropy (Confidence in Preparation):

After each study session, you give every student three choices for their confidence: "I’m ready," "I’m nervous," or "I’m not sure" (representing Buy, Sell, Hold in trading).
If students are very confident in their knowledge, their "entropy" is low (less uncertainty). If everyone is unsure, entropy is high.
You use this entropy to adjust how much focus you should put on each student in the study plan.
Dynamic Thresholds (Study Plan):

You realize that every student needs a unique threshold for when they are ready to move on to a new topic. Some students need more time (higher threshold), while others can switch topics quickly (lower threshold).
The thresholds are dynamic and depend on:
How much stress they’ve reduced recently (volatility change rate).
How critical the topic is for the group (PCA contribution).
How confident they feel (entropy of their confidence).
The Exam as the Market:

The final test acts like the real-world trading market. If the study group can align their efforts (similar to finding trading signals), they maximize their overall performance (similar to the Sharpe Ratio for returns).

The Goal:

Help every student perform their best (like maximizing profits in trading).
Minimize unnecessary stress and avoid overloading anyone (like minimizing risks and variance).
Adapt the study plan dynamically to new challenges and feedback (like responding to changing market conditions).

The Problem:

Each student's stress level follows a random path (like a stochastic process) and is influenced by their natural abilities and study habits.
The group's success depends on finding the optimal way to balance individual efforts and group collaboration.
How can you create a study plan that dynamically adapts to each student’s needs, while also ensuring the group collectively performs well?


Code
#define PAIRS 28
#define COMPONENTS 3   // Number of PCA components
#define GNN_LAYERS 2   // Number of GNN layers
#define ACTIONS 3      // Buy, Sell, Hold

// Define currency pairs
string CurrencyPairs[PAIRS] = {
    "EURUSD", "GBPUSD", "USDJPY", "GBPJPY", "USDCAD", "EURAUD", "EURJPY",
    "AUDCAD", "AUDJPY", "AUDNZD", "AUDUSD", "CADJPY", "EURCAD", "EURCHF",
    "EURGBP", "EURNZD", "GBPCAD", "GBPCHF", "NZDCAD", "NZDJPY", "NZDUSD",
    "USDCHF", "CHFJPY", "AUDCHF", "GBPNZD", "NZDCHF", "CADCHF", "GBPAUD"
};

// Variables for PCA, GNN, and signals
vars volatilities[PAIRS];                   // Current volatilities
vars volChangeRate[PAIRS];                  // Volatility change rate
vars kernelMatrix[PAIRS][PAIRS];            // Kernel matrix for PCA
vars pcaReducedFeatures[PAIRS][COMPONENTS]; // PCA-reduced features
vars adjacencyMatrices[PAIRS][PAIRS];       // GNN adjacency matrices
vars gnnWeights[GNN_LAYERS][COMPONENTS][COMPONENTS]; // GNN weights
vars gnnOutputs[PAIRS][ACTIONS];            // GNN probabilities (Buy/Sell/Hold)
vars signals[PAIRS];                        // Final trading signals
vars eigenvalues[COMPONENTS];               // Eigenvalues from PCA

// Step 1: Calculate Volatility and Change Rate
function calculateVolatility() {
    for (int i = 0; i < PAIRS; i++) {
        asset(CurrencyPairs[i]);
        vars logReturns = series(priceClose(0) / priceClose(1) - 1); // Log returns
        volatilities[i] = sqrt(SMA(pow(logReturns, 2), 20));        // 20-bar rolling std dev
        volChangeRate[i] = volatilities[i] - volatilities[i+1];     // Change rate
    }
}

// Step 2: Perform Kernel PCA
function performKernelPCA() {
    // Construct Kernel Matrix
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < PAIRS; j++) {
            kernelMatrix[i][j] = exp(-pow(volatilities[i] - volatilities[j], 2) / (2 * 0.1 * 0.1)); // Gaussian kernel
        }
    }

    // Perform eigen decomposition (simplified example)
    vars eigenvectors;
    eigenDecomposition(kernelMatrix, eigenvalues, eigenvectors);
    
    // Reduce dimensions using top COMPONENTS
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < COMPONENTS; j++) {
            pcaReducedFeatures[i][j] = dotProduct(kernelMatrix[i], eigenvectors[j]);
        }
    }
}

// Step 3: Initialize GNN Weights
function initializeGNNWeights() {
    for (int l = 0; l < GNN_LAYERS; l++) {
        for (int i = 0; i < COMPONENTS; i++) {
            for (int j = 0; j < COMPONENTS; j++) {
                gnnWeights[l][i][j] = random() * 0.1; // Small random initialization
            }
        }
    }
}

// Step 4: GNN Propagation
function propagateGNN() {
    vars tempFeatures[PAIRS][COMPONENTS];
    for (int l = 0; l < GNN_LAYERS; l++) {
        for (int i = 0; i < PAIRS; i++) {
            for (int k = 0; k < COMPONENTS; k++) {
                tempFeatures[i][k] = 0;
                for (int j = 0; j < PAIRS; j++) {
                    for (int m = 0; m < COMPONENTS; m++) {
                        tempFeatures[i][k] += adjacencyMatrices[i][j] * pcaReducedFeatures[j][m] * gnnWeights[l][m][k];
                    }
                }
                tempFeatures[i][k] = max(0, tempFeatures[i][k]); // ReLU activation
            }
        }
        // Update PCA features for the next layer
        for (int i = 0; i < PAIRS; i++) {
            for (int k = 0; k < COMPONENTS; k++) {
                pcaReducedFeatures[i][k] = tempFeatures[i][k];
            }
        }
    }
    // Final probabilities for Buy, Sell, Hold
    for (int i = 0; i < PAIRS; i++) {
        for (int k = 0; k < ACTIONS; k++) {
            gnnOutputs[i][k] = softmax(tempFeatures[i][k]); // Example output
        }
    }
}

// Step 5: Generate Trading Signals with PCA and GNN Thresholds
function generateSignals() {
    // Calculate PCA Variance Contribution
    var totalVariance = 0;
    for (int k = 0; k < COMPONENTS; k++) {
        totalVariance += eigenvalues[k];
    }
    var pcaContribution = eigenvalues[0] / totalVariance; // Contribution of the top component

    // Calculate GNN Entropy for each pair
    vars gnnEntropy[PAIRS];
    for (int i = 0; i < PAIRS; i++) {
        gnnEntropy[i] = 0;
        for (int k = 0; k < ACTIONS; k++) {
            gnnEntropy[i] -= gnnOutputs[i][k] * log(gnnOutputs[i][k] + 1e-8);
        }
    }

    // Calculate Dynamic Thresholds with PCA and GNN
    vars dynamicThresholdBuy[PAIRS];
    vars dynamicThresholdSell[PAIRS];
    for (int i = 0; i < PAIRS; i++) {
        var meanVolChangeRate = SMA(volChangeRate, PAIRS);
        var stddevVolChangeRate = StdDev(volChangeRate, PAIRS);

        // Adjust thresholds with PCA and GNN contributions
        dynamicThresholdBuy[i] = meanVolChangeRate + 0.5 * stddevVolChangeRate * pcaContribution * (1 - gnnEntropy[i]);
        dynamicThresholdSell[i] = meanVolChangeRate - 0.5 * stddevVolChangeRate * pcaContribution * (1 - gnnEntropy[i]);
    }

    // Generate Trading Signals
    for (int i = 0; i < PAIRS; i++) {
        var gnnBuyProb = gnnOutputs[i][0];
        var gnnSellProb = gnnOutputs[i][1];

        signals[i] = gnnBuyProb - gnnSellProb; // Base signal

        // Incorporate dynamic thresholds
        if (signals[i] > dynamicThresholdBuy[i]) signals[i] = 1; // Strong Buy
        else if (signals[i] < dynamicThresholdSell[i]) signals[i] = -1; // Strong Sell
        else signals[i] = 0; // Hold
    }
}

// Step 6: Execute Trades
function executeTrades() {
    for (int i = 0; i < PAIRS; i++) {
        if (signals[i] == 1) enterLong(CurrencyPairs[i]); // Strong Buy
        else if (signals[i] == -1) enterShort(CurrencyPairs[i]); // Strong Sell
    }
}

// Main Strategy Function
function run() {
    set(PLOTNOW);

    // Calculate volatility and change rate
    calculateVolatility();

    // Perform kernel PCA
    performKernelPCA();

    // Initialize GNN weights (once)
    if (is(INITRUN)) initializeGNNWeights();

    // Propagate GNN
    propagateGNN();

    // Generate signals
    generateSignals();

    // Execute trades
    executeTrades();
}

Attached Files
Time-Series Volatility Clustering and Adaptive Trading Signals [Re: TipmyPip] #488541
01/09/25 20:10
01/09/25 20:10
Joined: Sep 2017
Posts: 141
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TipmyPip Online OP
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TipmyPip  Online OP
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Joined: Sep 2017
Posts: 141
The Puzzle of Dynamic Currency Connections
Background:
Imagine you’re in charge of monitoring 28 students (representing currency pairs) in a math class. Each student has a different skill level and focus area (representing volatility). Sometimes they perform well, and sometimes they struggle—but there’s a pattern to it. When one student gets stuck on a tough topic, their struggle often spreads to nearby students (like a ripple effect of high volatility). Similarly, when one student is confident, it can boost the confidence of others nearby.

Your job is to figure out these patterns and decide:

Who needs help and who’s ready to move on?
When to focus on certain students to improve the overall class performance.
But here’s the catch:

The students form groups (like clusters), where some groups are more prone to challenges than others.
Each student’s performance is influenced by their past performance (like habits) and their interactions with others in the group.

The Challenge: Track the Patterns:

You have to observe how often each student struggles or excels over time. Are there periods where the same students keep struggling (clustering)? Are there students who quickly bounce back from challenges?
Form Study Groups:

Pair students based on how much they affect each other’s performance. If Student A’s struggles often lead to Student B struggling too, they should be in the same group. Use this to create a "map" of how the class interacts.
Summarize Key Challenges:

Once you’ve mapped the class, find the topics or patterns that explain the majority of struggles. These are your "main challenges" that need solving.
Predict the Next Struggles:

Based on their history and their group’s behavior, predict which students will need help next. This is your chance to act early and make the class stronger!
Decide the Focus:

Each day, decide who you’ll focus on: who needs a boost, who can help others, and who’s ready to move on. Your decisions will affect the overall class performance.

Bonus Twist:
As you monitor the class, the students’ behavior changes. New friendships form, others break apart, and some students get unexpectedly better or worse. Can you adapt your plan to these changes and keep the whole class improving?

Your Task:
Identify the students’ struggles, find the hidden patterns in their interactions, and create a plan that helps the whole class succeed over time. The better your plan, the stronger the class becomes.
Can you rise to the challenge and lead the class to success?


Code
#define PAIRS 28
#define COMPONENTS 3   // Number of PCA components
#define GNN_LAYERS 2   // Number of GNN layers
#define ACTIONS 3      // Buy, Sell, Hold

// Define currency pairs
string CurrencyPairs[PAIRS] = {
    "EURUSD", "GBPUSD", "USDJPY", "GBPJPY", "USDCAD", "EURAUD", "EURJPY",
    "AUDCAD", "AUDJPY", "AUDNZD", "AUDUSD", "CADJPY", "EURCAD", "EURCHF",
    "EURGBP", "EURNZD", "GBPCAD", "GBPCHF", "NZDCAD", "NZDJPY", "NZDUSD",
    "USDCHF", "CHFJPY", "AUDCHF", "GBPNZD", "NZDCHF", "CADCHF", "GBPAUD"
};

// Variables for PCA, GNN, and signals
vars volatilities[PAIRS];                   // Current volatilities
vars volClustering[PAIRS];                  // Volatility clustering scores
vars kernelMatrix[PAIRS][PAIRS];            // Kernel matrix for PCA
vars pcaReducedFeatures[PAIRS][COMPONENTS]; // PCA-reduced features
vars timeSeriesFeatures[PAIRS];             // Time-series features (e.g., autocorrelation)
vars timeDependentFeatures[PAIRS];          // Time-dependent features (e.g., volatility lag)
vars adjacencyMatrices[PAIRS][PAIRS];       // GNN adjacency matrices
vars gnnWeights[GNN_LAYERS][COMPONENTS][COMPONENTS]; // GNN weights
vars gnnOutputs[PAIRS][ACTIONS];            // GNN probabilities (Buy/Sell/Hold)
vars signals[PAIRS];                        // Final trading signals
vars eigenvalues[COMPONENTS];               // Eigenvalues from PCA

// Softmax function
var softmax(vars logits[], int index, int size) {
    var sum = 0;
    for (int i = 0; i < size; i++) {
        sum += exp(logits[i]); // Exponentiate each value
    }
    return exp(logits[index]) / (sum + 1e-8); // Normalize by the sum, avoiding division by zero
}

// Step 1: Calculate Volatility and Clustering Scores
function calculateVolatilityAndClustering() {
    for (int i = 0; i < PAIRS; i++) {
        asset(CurrencyPairs[i]);
        vars logReturns = series(log(priceClose(0) / priceClose(1))); // Log returns
        volatilities[i] = sqrt(SMA(pow(logReturns, 2), 20));          // 20-bar rolling std dev
        
        // Calculate clustering using past volatilities
        vars pastVolatilities = series(volatilities[i]);              // Volatility series
        volClustering[i] = SMA(pastVolatilities, 10) / stddev(pastVolatilities, 10); // Example metric
    }
}

// Step 2: Extract Time-Series Features
function extractTimeSeriesFeatures() {
    for (int i = 0; i < PAIRS; i++) {
        asset(CurrencyPairs[i]);
        vars logReturns = series(log(priceClose(0) / priceClose(1))); // Log returns
        
        // Autocorrelation as a feature
        timeSeriesFeatures[i] = autocorrelation(logReturns, 5);       // 5-lag autocorrelation
        
        // Time-dependent feature: Volatility lag
        timeDependentFeatures[i] = volatilities[i - 1];               // Previous volatility
    }
}

// Step 3: Perform Enhanced PCA
function performEnhancedPCA() {
    // Construct Kernel Matrix
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < PAIRS; j++) {
            double distance = pow(volatilities[i] - volatilities[j], 2) +
                              pow(volClustering[i] - volClustering[j], 2) +
                              pow(timeSeriesFeatures[i] - timeSeriesFeatures[j], 2);
            kernelMatrix[i][j] = exp(-distance / (2 * 0.1 * 0.1)); // Gaussian kernel
        }
    }

    // Perform eigen decomposition
    vars eigenvectors;
    eigenDecomposition(kernelMatrix, eigenvalues, eigenvectors);

    // Reduce dimensions using top COMPONENTS
    for (int i = 0; i < PAIRS; i++) {
        for (int j = 0; j < COMPONENTS; j++) {
            pcaReducedFeatures[i][j] = dotProduct(kernelMatrix[i], eigenvectors[j]);
        }
    }
}

// Step 4: Initialize GNN Weights
function initializeGNNWeights() {
    for (int l = 0; l < GNN_LAYERS; l++) {
        for (int i = 0; i < COMPONENTS; i++) {
            for (int j = 0; j < COMPONENTS; j++) {
                gnnWeights[l][i][j] = random() * 0.1; // Small random initialization
            }
        }
    }
}

// Step 5: GNN Propagation
function propagateGNN() {
    vars tempFeatures[PAIRS][COMPONENTS];
    for (int l = 0; l < GNN_LAYERS; l++) {
        for (int i = 0; i < PAIRS; i++) {
            for (int k = 0; k < COMPONENTS; k++) {
                tempFeatures[i][k] = 0;
                for (int j = 0; j < PAIRS; j++) {
                    for (int m = 0; m < COMPONENTS; m++) {
                        tempFeatures[i][k] += adjacencyMatrices[i][j] * pcaReducedFeatures[j][m] * gnnWeights[l][m][k];
                    }
                }
                tempFeatures[i][k] = max(0, tempFeatures[i][k]); // ReLU activation
            }
        }
    }

    // Final probabilities for Buy, Sell, Hold using softmax
    for (int i = 0; i < PAIRS; i++) {
        vars logits[ACTIONS]; // Placeholder for raw GNN output logits
        for (int k = 0; k < ACTIONS; k++) {
            logits[k] = tempFeatures[i][k]; // Assign the logits (raw outputs)
        }

        // Apply softmax to calculate probabilities
        for (int k = 0; k < ACTIONS; k++) {
            gnnOutputs[i][k] = softmax(logits, k, ACTIONS);
        }
    }
}

// Step 6: Generate Trading Signals
function generateSignals() {
    for (int i = 0; i < PAIRS; i++) {
        var gnnBuyProb = gnnOutputs[i][0];
        var gnnSellProb = gnnOutputs[i][1];

        signals[i] = gnnBuyProb - gnnSellProb; // Base signal

        // Threshold decision
        if (signals[i] > 0.5) signals[i] = 1;  // Strong Buy
        else if (signals[i] < -0.5) signals[i] = -1; // Strong Sell
        else signals[i] = 0; // Hold
    }
}

// Step 7: Execute Trades
function executeTrades() {
    for (int i = 0; i < PAIRS; i++) {
        if (signals[i] == 1) enterLong(CurrencyPairs[i]); // Strong Buy
        else if (signals[i] == -1) enterShort(CurrencyPairs[i]); // Strong Sell
    }
}

// Main Strategy Function
function run() {
    set(PLOTNOW);

    // Step 1: Calculate volatility and clustering
    calculateVolatilityAndClustering();

    // Step 2: Extract time-series features
    extractTimeSeriesFeatures();

    // Step 3: Perform enhanced PCA
    performEnhancedPCA();

    // Step 4: Initialize GNN weights (once)
    if (is(INITRUN)) initializeGNNWeights();

    // Step 5: Propagate GNN
    propagateGNN();

    // Step 6: Generate trading signals
    generateSignals();

    // Step 7: Execute trades
    executeTrades();
}

Attached Files
Last edited by TipmyPip; 01/09/25 20:11.
Re: Gaussian Channel Adaptive Strategy [Re: TipmyPip] #488544
01/13/25 01:47
01/13/25 01:47
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M_D Online
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Hi TipmyPip,

during searching something i stumbled over your thread. Looks very nice and impressive, started to read from page 1. I dont have optionsdata so i skipped that part. But your Gaussian channel adaptive moving average strategy made me curious.
I copied your code and tried it on my Zorro. unfortunately i get a lot of compiler errors. Did you run those scripts with zorro? Or are they only fictional ZorroGPT generated codes based on your (very interesting) prompts?

partially the script is not in lite-c at all:

var filt = pow(a, i) * s + i * x * f[1] - (i >= 2 ? 36 * pow(x, 2) * f[2] : 0) + (i >= 3 ? 84 * pow(x, 3) * f[3] : 0)
- (i >= 4 ? 126 * pow(x, 4) * f[4] : 0) + (i >= 5 ? 126 * pow(x, 5) * f[5] : 0) - (i >= 6 ? 84 * pow(x, 6) * f[6] : 0)
+ (i >= 7 ? 36 * pow(x, 7) * f[7] : 0) - (i >= 8 ? 9 * pow(x, 8) * f[8] : 0) + (i == 9 ? 1 * pow(x, 9) * f[9] : 0);

the "?" in between doesnt belong to the syntax of Zorro as far as i learned until now. at least i could not find it nor in expressions neither in comparisons ... or somewhere else. Is that in python?
Besides that, there a re a lot syntax errors, misplacement of functions (does call first, and declare afterwards) and not defined variables at all ... its a shame i am not smart enough to figure out the formulas and the code by myself.
Will there be a debug attempt ... maybe anyone else?

Thanks and kind regards

M_D

Last edited by M_D; 01/13/25 01:52.
Re: Gaussian Channel Adaptive Strategy [Re: M_D] #488545
01/13/25 05:33
01/13/25 05:33
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TipmyPip Online OP
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Thank you M_D for your kind words and interest. I am happy that you are sharing your thoughts so others might see some of your interests as inspiration for future developments,

I do understand that the code is not that perfect, but the errors, are to be solved with ZorroGPT, if you have any specific problem you feel you are unable to solve with ZorroGPT, please let me know, I will do my best to help you, Yeah and one more important improvement for our Zorro Platform, as we all use GPT for express developments, it will be only logical to solve bugs with the help of GPT, so you can use it more, and have a better understanding of your strategies, and ways to improve them.

Code
// Declare variables
var a, s, x;             // Scalars used in calculations
int i;                   // Index variable
vars f[10];              // Array for coefficients or signal values

// Ensure the array 'f' has values before computation
f[0] = 1; f[1] = 2; f[2] = 3; f[3] = 4; f[4] = 5; 
f[5] = 6; f[6] = 7; f[7] = 8; f[8] = 9; f[9] = 10; // Example initialization

// Compute 'filt' using ifelse() instead of ternary operators
var filt = pow(a, i) * s 
    + ifelse(i >= 1, i * x * f[1], 0) 
    - ifelse(i >= 2, 36 * pow(x, 2) * f[2], 0) 
    + ifelse(i >= 3, 84 * pow(x, 3) * f[3], 0)
    - ifelse(i >= 4, 126 * pow(x, 4) * f[4], 0) 
    + ifelse(i >= 5, 126 * pow(x, 5) * f[5], 0) 
    - ifelse(i >= 6, 84 * pow(x, 6) * f[6], 0)
    + ifelse(i >= 7, 36 * pow(x, 7) * f[7], 0) 
    - ifelse(i >= 8, 9 * pow(x, 8) * f[8], 0) 
    + ifelse(i == 9, pow(x, 9) * f[9], 0);

// Output result
printf("The value of filt is: %f", filt);

Here you will find a little bit information about Trinary operators Lite-C does not support: https://zorro-project.com/manual/en/litec_c.htm
I am sorry for the previous post, I resolved the problem, in addition there is another version of the code at : https://opserver.de/ubb7/ubbthreads...ords=TipmyPip&Search=true#Post488326

Can you tell me why do you think the syntax of the flit variable doesn't seem to be right, you can also use GPT to resolve it...
I used the correction and explained to GPT that the Trinary operators Lite-C does not support the comparison ? expression : expression; syntax. Use the ifelse statement instead.
x = (x<0 ? -1 : 1); // C/C++
x = ifelse(x<0,-1,1); // lite-C

and within less than 3 seconds the above code was produced.

If you sit with the ZorroGPT, any formula you seem to have difficulty in understanding, ZorroGPT can assist you with no problem and Think of having a PhD level programmer, helping you in any problem you have 24/7, and resolve any problem you have.

Here in the following video you can see how a Quant Trader(MSc) is using GPT for quite hard core problems in algorithmic trading:

https://www.youtube.com/watch?v=hePmohJjfLA&t=7975s

Thank you once again, and please don't hesitate to have any inquires, I will be glad to help.

Last edited by TipmyPip; 01/13/25 12:01.
The Hydra's Awakening [Re: TipmyPip] #488555
01/25/25 14:09
01/25/25 14:09
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(Very Soon I am going to share a new level of Algorithmic Trading Ideas : but for Inspiration please read the introduction and get ready for the roller coaster ride :-)

Hydra Awakening

The Hydra's Awakening: A Tale of AI Triumph and Market Symphony

The trading floor of Quantum Markets was a place of quiet intensity, where the glow of monitors and the hum of machines filled the air. The Hydra, a new AI-powered trading engine, was the crown jewel of the firm, capable of analyzing billions of market scenarios in real-time. It was a system designed to adapt and thrive in the unpredictable chaos of global markets, its neural networks fine-tuned to find patterns in the noise. As the clock ticked toward midnight, the final touches were made to the system. The Hydra was about to go live, and its creators gathered around with anticipation. This was no ordinary system. It was a machine designed not just to react to market conditions but to predict them, to foresee the subtle shifts in price and volume that could make or break a trading strategy. The system’s core was powered by a fleet of GPUs, each tasked with processing specific aspects of market behavior. Elena, the lead quant developer, stood at her workstation, her eyes fixed on the logs streaming across her screen. She had spent months perfecting the Hydra’s architecture, ensuring that each of its processes ran in perfect harmony. Tonight would be its first real test.

As the system went live, the Hydra sprang into action. Streams of data poured in from global exchanges, and the GPUs hummed with activity. Each core of the Hydra represented a different part of the system, processing patterns, analyzing order books, and calculating optimal entry and exit points. For the first few moments, everything seemed flawless. Predictions flashed across the screens, showing potential trades with astonishing accuracy. Traders watched in awe as the Hydra processed millions of combinations, sifting through the noise of the markets to find hidden opportunities. It was a sight to behold, a perfect symphony of data and computation. But then, something changed. At first, it was subtle, a slight delay in one of the streams. Then another. The GPUs, once fully engaged, began to slow. The Hydra’s once-flawless performance faltered, and the predictions on the screens grew less frequent. Elena’s heart sank as she realized something was wrong. The Hydra, designed to process billions of scenarios, was now crawling, its once-mighty engine reduced to a single struggling core.

Elena’s mind raced as she dove into the system’s logs, searching for the source of the problem. The data was coming in as expected, but the processing speed had dropped dramatically. She watched as the tasks queued up, each waiting its turn to access the GPU. It was as if the Hydra’s many heads were fighting over the same resources, unable to share the workload efficiently. The once-mighty machine had become a bottleneck, its potential trapped by its own design. As the minutes ticked by, the problem grew worse. One by one, the Hydra’s cores went idle, leaving a single process to carry the load. The traders, who had been so confident in the system’s abilities, began to murmur with concern. If the Hydra couldn’t recover, the night’s trading opportunities would slip away, taking with them the chance to prove the system’s worth. Elena knew she had to act quickly. The logs revealed the problem: the GPU, despite its power, was being overwhelmed by the demands of the Hydra’s processes. Each core was trying to access the GPU simultaneously, creating a bottleneck that slowed the entire system. It was a classic case of resource contention, but solving it would be no easy task.

Elena’s fingers flew across the keyboard as she implemented a series of changes. First, she adjusted the system to limit the number of active processes, ensuring that the GPU wasn’t overloaded. Then she added synchronization points, forcing the processes to wait their turn rather than compete for resources. Finally, she implemented a dynamic memory allocation system, allowing the GPU to prioritize smaller, faster tasks over larger, more resource-intensive ones. As the changes went live, the Hydra began to recover. One by one, its cores sprang back to life, each taking its share of the workload. The GPU usage surged, and the predictions on the screens grew more frequent. The traders watched in amazement as the system roared back to life, processing data with a speed and precision they had never seen before. But just as things seemed to be stabilizing, another issue emerged. The changes had solved the immediate problem, but they had also introduced new challenges. The synchronization points, while effective, had created delays in the system, slowing down the overall processing speed. The dynamic memory allocation, designed to prioritize smaller tasks, was causing larger tasks to wait longer than expected. It was a delicate balancing act, and Elena knew that even the smallest misstep could cause the system to falter again.

As the night wore on, Elena continued to fine-tune the Hydra. She adjusted the synchronization points, optimizing them for the specific demands of the trading environment. She refined the memory allocation system, ensuring that tasks were prioritized based on their impact on the overall strategy. And she worked tirelessly to ensure that each core of the Hydra operated in harmony with the others. By dawn, the system was running more efficiently than ever. The Hydra’s cores worked in perfect sync, processing billions of scenarios with ease. The GPU, once a bottleneck, was now a well-oiled machine, handling the demands of the system without breaking a sweat. The traders, who had been on the verge of losing faith in the system, were now convinced of its potential. The Hydra had not only recovered but had exceeded their expectations, delivering results that no human trader could match.

But for Elena, the victory was bittersweet. She knew that the system’s success was only temporary, that the markets would continue to evolve and that new challenges would arise. The Hydra, powerful as it was, would need constant care and attention to stay ahead of the game. And so, as the traders celebrated their newfound edge, Elena sat at her workstation, already planning the next set of improvements. She knew that the real test of the Hydra’s abilities was yet to come, and she was determined to ensure that it was ready for whatever the markets might throw its way. The trading floor buzzed with excitement, but Elena’s focus remained unshaken. The Hydra, her creation, was alive, and she would do whatever it took to keep it thriving.

As the sun rose over Quantum Markets, the Hydra’s journey had only just begun. Its story, a tale of ambition, adversity, and innovation, was far from over. For in the world of algorithmic trading, the only constant is change, and the Hydra, like its namesake, would need to adapt and grow to survive. And so, with the system stabilized and the future uncertain, Elena prepared for the challenges ahead, knowing that the true measure of the Hydra’s success would not be found in its past achievements but in its ability to navigate the unknown.



Attached Files
GPUAlgoML.zip (13 downloads)
Last edited by TipmyPip; 01/30/25 08:39.
Re: The Hydra's Awakening [Re: TipmyPip] #488557
01/26/25 19:53
01/26/25 19:53
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It is kind of very hard to work with a platform that has very tight memory management, while memory management doesn't have any detailed documentation, and the whole Zorro platform is closed, no direct access to how data structures are organized, which makes it very hard to work with other utilities being used by other platforms like Python... and synchronizing between them for HFT, is a very serious challenge. I think the amount of time invested in developing the platform in relation to how many people have the ability to advance faster, without having too many challenges with interfacing different platforms together, is a serious limit on the amount of users that can take of the advantages of Zorro Project.

Re: The Hydra's Awakening [Re: TipmyPip] #488562
01/30/25 00:38
01/30/25 00:38
Joined: Aug 2018
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Thank you TipmyPip for showcasing ZorroGPT and for sharing these examples of outputs. I am fascinated and curious. Do you have a background as a quant? Thanks again for the inspirational work.


Thanks so much,

OptimusPrime

Re: The Hydra's Awakening [Re: OptimusPrime] #488563
01/30/25 03:51
01/30/25 03:51
Joined: Sep 2017
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Dear OptimusPrime, a very creative name, Thank you so much for your kind words, the short answer would be, my imagination is never limited by definitions, but that would be too serious for quite a demonstration. I am very knowledgeable thanks to AI, if I convey to you how the system of definitions would try to limit your success, you would be amazed how fast your mind can adapt, while true adaptation comes from very hard struggles. I am very fascinated by Graph Theory, Chaos, And Neural Networks.
(Gee, You can't imagine how much I appreciate your kind words.)

Thank you once again from your time investment in our common interests...
ZorroGPT.

But I would like to add, because of Zorro Trader, some of my ambitions have become even higher. Thanks to this marvelous project, and the team of Zorro Developers.
It is really surprising how open source projects can change the world, While DeepSeek is just one proof of the concept, Linux would be a milestone, But If ZorroTrader becomes the next big thing, even for the most curious minds, a Commercial version with full source code, would make the team of developers raise to new heights, and be famous even more than Microsoft.

Last edited by TipmyPip; 01/30/25 04:32.
Re: Zorro Trader GPT [Re: TipmyPip] #488564
01/30/25 04:43
01/30/25 04:43
Joined: Sep 2017
Posts: 141
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TipmyPip Online OP
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This is to inspire Everybody and OptimusPrime.

Code
(* Helper function to check if a queen can be placed at (row, col) *)
isSafe[board_, row_, col_] := 
  And @@ (And[# != row, # + board[[#]] != row + col, # - board[[#]] != row - col] & /@ Range[col - 1]);

(* Recursive function to solve the 9-queens problem *)
solveQueens[board_, col_] := 
  If[col > 9, (* If all queens are placed, return the solution *)
     Print[board];,
     (* Else, try placing the queen in each row of the current column *)
     Do[
       If[isSafe[board, row, col],
          solveQueens[ReplacePart[board, col -> row], col + 1]; (* Recur for the next column *)
       ],
       {row, 1, 9}
     ]
  ];


A graph of Queens on the Board of Financial Steps, leading to a test for checkmate in every direction.

Code
#include <stdio.h>

#define N 9  // Board size for the 9-queens problem

// Function to check if a queen can be placed at board[row][col]
int isSafe(int board[N], int row, int col) {
    int i;
    for(i = 0; i < col; i++) {
        if(board[i] == row || 
           board[i] - i == row - col || 
           board[i] + i == row + col)
            return 0;  // Not safe
    }
    return 1;  // Safe position
}

// Recursive function to solve the 9-queens problem
void solveQueens(int board[N], int col) {
    if(col >= N) {  // If all queens are placed, print the solution
        printf("\nSolution: ");
        int i;
        for(i = 0; i < N; i++) 
            printf("%d ", board[i] + 1);  // Convert 0-based to 1-based
        printf("\n");
        return;
    }

    // Try placing a queen in each row of the current column
    int row;
    for(row = 0; row < N; row++) {
        if(isSafe(board, row, col)) {
            board[col] = row;  // Place queen
            solveQueens(board, col + 1);  // Recur for the next column
            board[col] = 0;  // Backtrack
        }
    }
}

// Main function to initialize the board and start solving
void main() {
    int board[N] = {0};  // Initialize board with all zeroes
    solveQueens(board, 0);  // Start recursion from column 0
}


And another version :

Code
#include <stdio.h>

#define N 9  // Board size for the 9-queens problem

// Function to check if a queen can be placed at board[row][col]
int isSafe(int board[N], int row, int col) {
    int i;
    for (i = 0; i < col; i++) {
        if (board[i] == row || 
            board[i] - i == row - col || 
            board[i] + i == row + col) 
            return 0;  // Not safe
    }
    return 1;  // Safe position
}

// Function in `f(x) = f(x - 1)` form
int solveQueens(int board[N], int col) {
    if (col == 0) return 1;  // Base case: when col reaches 0, return success

    int prev = solveQueens(board, col - 1);  // Recursive call f(x) = f(x - 1)
    
    if (prev == 0) return 0;  // If previous step failed, return failure

    int row;
    for (row = 0; row < N; row++) {
        if (isSafe(board, row, col - 1)) {  // Check previous column
            board[col - 1] = row;
            return 1;  // Solution found
        }
    }

    return 0;  // No solution found, return failure
}

// Main function to initialize the board and start solving
void main() {
    int board[N] = {0};  // Initialize board with all zeroes
    solveQueens(board, N);  // Start recursion from column N (reverse order)
}

Last edited by TipmyPip; 01/30/25 05:02.
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