Combining Deep Learning (GAN) with Genetic Algorithms for Advanced Spoofing Analysis in Financial Markets

import as tf from '@tensorflow/tfjs-node';
import { Injectable } from '@nestjs/common';

@Injectable()
export class GanService {
    private generator: tf.LayersModel;
    private discriminator: tf.LayersModel;

    constructor() {
        this.initializeModels();
    }

    private initializeModels() {
        // Define the advanced Generator model
        this.generator = tf.sequential();
        this.generator.add(tf.layers.dense({ units: 256, inputShape: [100], activation: 'relu' }));
        this.generator.add(tf.layers.batchNormalization());
        this.generator.add(tf.layers.dense({ units: 512, activation: 'relu' }));
        this.generator.add(tf.layers.dropout({ rate: 0.3 }));
        this.generator.add(tf.layers.dense({ units: 2, activation: 'tanh' }));
        // Output: [price, volume]

        // Define the advanced Discriminator model
        this.discriminator = tf.sequential();
        this.discriminator.add(tf.layers.dense({ units: 512, inputShape: [2], activation: 'relu' }));
        this.discriminator.add(tf.layers.dropout({ rate: 0.3 }));
        this.discriminator.add(tf.layers.dense({ units: 256, activation: 'relu' }));
        this.discriminator.add(tf.layers.batchNormalization());
        this.discriminator.add(tf.layers.dense({ units: 1, activation: 'sigmoid' }));
        // Output: 0 or 1

        this.discriminator.compile({ optimizer: 'adam', loss: 'binaryCrossentropy' });
    }

    public async trainGan(data: number[][], epochs: number) {
        for (let epoch = 0; epoch < epochs; epoch++) {
            const noise = tf.randomNormal([data.length, 100]);
            const generatedData = this.generator.predict(noise) as tf.Tensor;
            const labelsReal = tf.ones([data.length, 1]);
            const labelsFake = tf.zeros([data.length, 1]);

            const lossReal = this.discriminator.trainOnBatch(tf.tensor2d(data), labelsReal);
            const lossFake = this.discriminator.trainOnBatch(generatedData, labelsFake);

            console.log(`Epoch ${epoch + 1}: Real Loss: ${lossReal}, Fake Loss: ${lossFake}`);
        }
    }

    public generateFakeOrder(): number[] {
        const noise = tf.randomNormal([1, 100]);
        const generatedOrder = this.generator.predict(noise) as tf.Tensor;
        return generatedOrder.dataSync();
    }
}

import { Injectable } from '@nestjs/common';

@Injectable()
export class GeneticService {
    private populationSize = 50;
    private generations = 100;
    private mutationRate = 0.1;

    private initializePopulation(): number[][] {
        return Array.from({ length: this.populationSize }, () => [
            Math.random(),
            // Price factor
            Math.random(),
            // Volume factor
        ]);
    }

    private fitness(individual: number[]): number {
        const [priceFactor, volumeFactor] = individual;
        const profit = this.simulateTrading(priceFactor, volumeFactor);
        const risk = this.calculateRisk(priceFactor, volumeFactor);
        return profit - risk;
    }

    private simulateTrading(priceFactor: number, volumeFactor: number): number {
        const historicalPrices = [100, 102, 101, 105, 110, 108, 107]; // Sample historical data
        let totalProfit = 0;
        let currentHoldings = 0;

        for (let i = 1; i < historicalPrices.length; i++) {
            const previousPrice = historicalPrices[i - 1];
            const currentPrice = historicalPrices[i];

            if (priceFactor > Math.random()) {
                currentHoldings += volumeFactor * 10; // Buy
                totalProfit -= previousPrice * volumeFactor * 10; // Cost of buying
            } else {
                totalProfit += currentPrice * currentHoldings; // Sell
                currentHoldings = 0; // Liquidate holdings
            }
        }
        return totalProfit;
    }

    private calculateRisk(priceFactor: number, volumeFactor: number): number {
        const returns = [0.01, -0.02, 0.015, -0.005, 0.03, -0.01]; // Sample returns
        const meanReturn = returns.reduce((a, b) => a + b, 0) / returns.length;
        const variance = returns.map(r => Math.pow(r - meanReturn, 2)).reduce((a, b) => a + b, 0) / returns.length;
        const standardDeviation = Math.sqrt(variance);
        return standardDeviation * 100; // Risk calculation
    }

    public optimizeGanParameters() {
        let population = this.initializePopulation();
        for (let generation = 0; generation < this.generations; generation++) {
            population = population
                .map(individual => ({ individual, fitness: this.fitness(individual) }))
                .sort((a, b) => b.fitness - a.fitness)
                .map(item => item.individual);

            const newPopulation = population.slice(0, this.populationSize / 2);
            while (newPopulation.length < this.populationSize) {
                const parent1 = newPopulation[Math.floor(Math.random() * newPopulation.length)];
                const parent2 = newPopulation[Math.floor(Math.random() * newPopulation.length)];
                const child = [
                    (parent1[0] + parent2[0]) / 2 + (Math.random() - 0.5) * this.mutationRate,
                    (parent1[1] + parent2[1]) / 2 + (Math.random() - 0.5) * this.mutationRate,
                ];
                newPopulation.push(child);
            }
            console.log(`Generation ${generation + 1}: Best Fitness: ${this.fitness(newPopulation[0])}`);
        }
        return population[0];
    }
}

import { Module } from '@nestjs/common';
import { RedisModule } from '@nestjs-modules/ioredis';

@Module({
    imports: [
        RedisModule.forRoot({
            config: {
                nodes: [
                    { host: 'redis-node-1', port: 6379 },
                    { host: 'redis-node-2', port: 6379 },
                    { host: 'redis-node-3', port: 6379 },
                ],
            },
        }),
    ],
    exports: [RedisModule],
})
export class CustomRedisModule {}

import as crypto from 'crypto';

export class SecurityUtils {
    static encryptData(data: string, secretKey: string): string {
        const cipher = crypto.createCipher('aes-256-cbc', secretKey);
        let encrypted = cipher.update(data, 'utf8', 'hex');
        encrypted += cipher.final('hex');
        return encrypted;
    }
}

import { Controller, Get } from '@nestjs/common';
import { GanService } from '../gan/gan.service';
import { GeneticService } from '../genetic/genetic.service';
import { RedisService } from '@nestjs-modules/ioredis';
import { SecurityUtils } from '../security/security.utils';

@Controller('orders')
export class OrdersController {
    constructor(
        private readonly ganService: GanService,
        private readonly geneticService: GeneticService,
        private readonly redisService: RedisService,
    ) {}

    @Get('generate')
    async generateOrder() {
        const bestParams = this.geneticService.optimizeGanParameters();
        console.log('Best Parameters:', bestParams);

        const fakeOrder = this.ganService.generateFakeOrder();
        console.log('Generated Fake Order:', fakeOrder);

        const secretKey = 'your-secret-key';
        const encryptedOrder = SecurityUtils.encryptData(JSON.stringify(fakeOrder), secretKey);

        const redisClient = this.redisService.getClient();
        await redisClient.lpush('orderBook', encryptedOrder);



        return { order: fakeOrder };
    }
}