Squashed 'vendor/ruvector/' content from commit b64c2172

git-subtree-dir: vendor/ruvector
git-subtree-split: b64c21726f2bb37286d9ee36a7869fef60cc6900

npm/packages/agentic-synth-examples/tests/generators/self-learning.test.ts

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+/**
+ * Tests for Self-Learning Generator
+ */
+
+import { describe, it, expect, beforeEach } from 'vitest';
+import { SelfLearningGenerator } from '../../src/generators/self-learning.js';
+import type { SelfLearningConfig, GenerateOptions } from '../../src/generators/self-learning.js';
+
+describe('SelfLearningGenerator', () => {
+  let config: SelfLearningConfig;
+
+  beforeEach(() => {
+    config = {
+      task: 'code-generation',
+      learningRate: 0.1,
+      iterations: 5
+    };
+  });
+
+  describe('Initialization', () => {
+    it('should create generator with valid config', () => {
+      const generator = new SelfLearningGenerator(config);
+      expect(generator).toBeDefined();
+    });
+
+    it('should accept quality threshold', () => {
+      const generatorWithThreshold = new SelfLearningGenerator({
+        ...config,
+        qualityThreshold: 0.9
+      });
+      expect(generatorWithThreshold).toBeDefined();
+    });
+
+    it('should accept maxAttempts option', () => {
+      const generatorWithMax = new SelfLearningGenerator({
+        ...config,
+        maxAttempts: 20
+      });
+      expect(generatorWithMax).toBeDefined();
+    });
+  });
+
+  describe('Generation and Learning', () => {
+    it('should generate output with quality improvement', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const result = await generator.generate({
+        prompt: 'Generate a function to validate emails'
+      });
+
+      expect(result.output).toBeDefined();
+      expect(result.finalQuality).toBeGreaterThan(0);
+      expect(result.finalQuality).toBeLessThanOrEqual(1);
+      expect(result.improvement).toBeGreaterThanOrEqual(0);
+      expect(result.iterations).toBe(5);
+      expect(result.metrics).toHaveLength(5);
+    });
+
+    it('should show quality improvement over iterations', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const result = await generator.generate({
+        prompt: 'Test improvement tracking'
+      });
+
+      const firstQuality = result.metrics[0].quality;
+      const lastQuality = result.metrics[result.metrics.length - 1].quality;
+
+      // Quality should generally improve (or at least not decrease significantly)
+      expect(lastQuality).toBeGreaterThanOrEqual(firstQuality * 0.95);
+      expect(result.improvement).toBeDefined();
+    });
+
+    it('should track metrics for each iteration', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const result = await generator.generate({
+        prompt: 'Track iteration metrics'
+      });
+
+      expect(result.metrics).toHaveLength(5);
+      result.metrics.forEach((metric, index) => {
+        expect(metric.iteration).toBe(index + 1);
+        expect(metric.quality).toBeGreaterThan(0);
+        expect(typeof metric.improvement).toBe('number');
+        expect(Array.isArray(metric.feedback)).toBe(true);
+      });
+    });
+
+    it('should apply learning rate correctly', async () => {
+      const highLearningRate = new SelfLearningGenerator({
+        ...config,
+        learningRate: 0.5,
+        iterations: 3
+      });
+      const lowLearningRate = new SelfLearningGenerator({
+        ...config,
+        learningRate: 0.05,
+        iterations: 3
+      });
+
+      const highResult = await highLearningRate.generate({
+        prompt: 'Test high learning rate'
+      });
+      const lowResult = await lowLearningRate.generate({
+        prompt: 'Test low learning rate'
+      });
+
+      // Higher learning rate should generally lead to faster improvement
+      expect(highResult.improvement).toBeDefined();
+      expect(lowResult.improvement).toBeDefined();
+    });
+  });
+
+  describe('Test Integration', () => {
+    it('should evaluate against test cases', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const tests = [
+        (output: any) => output.content.length > 10,
+        (output: any) => output.quality > 0.5,
+        (output: any) => output.metadata !== undefined
+      ];
+
+      const result = await generator.generate({
+        prompt: 'Generate with tests',
+        tests
+      });
+
+      expect(result.finalQuality).toBeGreaterThan(0);
+      result.metrics.forEach(metric => {
+        expect(metric.testsPassingRate).toBeDefined();
+        expect(metric.testsPassingRate).toBeGreaterThanOrEqual(0);
+        expect(metric.testsPassingRate).toBeLessThanOrEqual(1);
+      });
+    });
+
+    it('should track test passing rate', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const tests = [
+        (output: any) => output.quality > 0.6,
+        (output: any) => output.quality > 0.7
+      ];
+
+      const result = await generator.generate({
+        prompt: 'Track test pass rate',
+        tests
+      });
+
+      // Test passing rate should be tracked for each iteration
+      result.metrics.forEach(metric => {
+        expect(metric.testsPassingRate).toBeGreaterThanOrEqual(0);
+        expect(metric.testsPassingRate).toBeLessThanOrEqual(1);
+      });
+    });
+
+    it('should handle failing tests gracefully', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const impossibleTests = [
+        () => false, // Always fails
+        () => false
+      ];
+
+      const result = await generator.generate({
+        prompt: 'Handle test failures',
+        tests: impossibleTests
+      });
+
+      expect(result.output).toBeDefined();
+      expect(result.finalQuality).toBeGreaterThan(0);
+      // Should complete despite test failures
+    });
+  });
+
+  describe('Event Emissions', () => {
+    it('should emit start event', async () => {
+      const generator = new SelfLearningGenerator(config);
+      let startEmitted = false;
+
+      generator.on('start', (data) => {
+        startEmitted = true;
+        expect(data.task).toBe('code-generation');
+        expect(data.iterations).toBe(5);
+      });
+
+      await generator.generate({ prompt: 'Test start event' });
+      expect(startEmitted).toBe(true);
+    });
+
+    it('should emit improvement events', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const improvements: any[] = [];
+
+      generator.on('improvement', (metrics) => {
+        improvements.push(metrics);
+      });
+
+      await generator.generate({ prompt: 'Test improvement events' });
+
+      expect(improvements).toHaveLength(5);
+      improvements.forEach(metric => {
+        expect(metric.iteration).toBeDefined();
+        expect(metric.quality).toBeDefined();
+      });
+    });
+
+    it('should emit complete event', async () => {
+      const generator = new SelfLearningGenerator(config);
+      let completeData: any = null;
+
+      generator.on('complete', (data) => {
+        completeData = data;
+      });
+
+      await generator.generate({ prompt: 'Test complete event' });
+
+      expect(completeData).toBeDefined();
+      expect(completeData.finalQuality).toBeDefined();
+      expect(completeData.improvement).toBeDefined();
+      expect(completeData.iterations).toBe(5);
+    });
+
+    it('should emit threshold-reached event', async () => {
+      const generator = new SelfLearningGenerator({
+        ...config,
+        qualityThreshold: 0.6,
+        iterations: 10
+      });
+      let thresholdReached = false;
+
+      generator.on('threshold-reached', (data) => {
+        thresholdReached = true;
+        expect(data.quality).toBeGreaterThanOrEqual(0.6);
+      });
+
+      await generator.generate({ prompt: 'Test threshold' });
+      // Threshold might or might not be reached depending on random variation
+    });
+  });
+
+  describe('Quality Thresholds', () => {
+    it('should stop when quality threshold is reached', async () => {
+      const generator = new SelfLearningGenerator({
+        ...config,
+        qualityThreshold: 0.7,
+        iterations: 10
+      });
+
+      const result = await generator.generate({
+        prompt: 'Test early stopping'
+      });
+
+      // Should stop before completing all iterations if threshold reached
+      expect(result.iterations).toBeLessThanOrEqual(10);
+      if (result.finalQuality >= 0.7) {
+        expect(result.iterations).toBeLessThan(10);
+      }
+    });
+
+    it('should use initial quality if provided', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const result = await generator.generate({
+        prompt: 'Test initial quality',
+        initialQuality: 0.8
+      });
+
+      expect(result.output).toBeDefined();
+      // Improvement calculation should be based on initial quality
+    });
+  });
+
+  describe('History Tracking', () => {
+    it('should maintain learning history', async () => {
+      const generator = new SelfLearningGenerator(config);
+      await generator.generate({ prompt: 'First generation' });
+
+      const history = generator.getHistory();
+      expect(history).toHaveLength(5);
+      expect(history[0].iteration).toBe(1);
+      expect(history[4].iteration).toBe(5);
+    });
+
+    it('should accumulate history across multiple generations', async () => {
+      const generator = new SelfLearningGenerator(config);
+      await generator.generate({ prompt: 'First' });
+      await generator.generate({ prompt: 'Second' });
+
+      const history = generator.getHistory();
+      expect(history.length).toBe(10); // 5 + 5 iterations
+    });
+
+    it('should reset history when reset is called', async () => {
+      const generator = new SelfLearningGenerator(config);
+      await generator.generate({ prompt: 'Generate before reset' });
+
+      expect(generator.getHistory().length).toBe(5);
+
+      generator.reset();
+
+      expect(generator.getHistory()).toHaveLength(0);
+    });
+
+    it('should emit reset event', () => {
+      const generator = new SelfLearningGenerator(config);
+      let resetEmitted = false;
+
+      generator.on('reset', () => {
+        resetEmitted = true;
+      });
+
+      generator.reset();
+      expect(resetEmitted).toBe(true);
+    });
+  });
+
+  describe('Feedback Generation', () => {
+    it('should generate relevant feedback', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const result = await generator.generate({
+        prompt: 'Test feedback generation'
+      });
+
+      result.metrics.forEach(metric => {
+        expect(Array.isArray(metric.feedback)).toBe(true);
+        expect(metric.feedback.length).toBeGreaterThan(0);
+        metric.feedback.forEach(fb => {
+          expect(typeof fb).toBe('string');
+          expect(fb.length).toBeGreaterThan(0);
+        });
+      });
+    });
+
+    it('should provide contextual feedback based on quality', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const result = await generator.generate({
+        prompt: 'Test contextual feedback'
+      });
+
+      // Feedback should vary based on performance
+      const feedbackTypes = new Set(
+        result.metrics.flatMap(m => m.feedback)
+      );
+      expect(feedbackTypes.size).toBeGreaterThan(0);
+    });
+  });
+
+  describe('Edge Cases', () => {
+    it('should handle zero iterations', async () => {
+      const generator = new SelfLearningGenerator({
+        ...config,
+        iterations: 0
+      });
+
+      const result = await generator.generate({
+        prompt: 'Test zero iterations'
+      });
+
+      expect(result.output).toBeNull();
+      expect(result.metrics).toHaveLength(0);
+    });
+
+    it('should handle very high learning rate', async () => {
+      const generator = new SelfLearningGenerator({
+        ...config,
+        learningRate: 1.0
+      });
+
+      const result = await generator.generate({
+        prompt: 'Test high learning rate'
+      });
+
+      expect(result.output).toBeDefined();
+      expect(result.finalQuality).toBeLessThanOrEqual(1.0);
+    });
+
+    it('should handle very low learning rate', async () => {
+      const generator = new SelfLearningGenerator({
+        ...config,
+        learningRate: 0.001
+      });
+
+      const result = await generator.generate({
+        prompt: 'Test low learning rate'
+      });
+
+      expect(result.output).toBeDefined();
+      // Improvement should be minimal but positive
+    });
+
+    it('should handle single iteration', async () => {
+      const generator = new SelfLearningGenerator({
+        ...config,
+        iterations: 1
+      });
+
+      const result = await generator.generate({
+        prompt: 'Single iteration test'
+      });
+
+      expect(result.iterations).toBe(1);
+      expect(result.metrics).toHaveLength(1);
+      expect(result.output).toBeDefined();
+    });
+  });
+
+  describe('Performance', () => {
+    it('should complete within reasonable time', async () => {
+      const generator = new SelfLearningGenerator(config);
+      const startTime = Date.now();
+
+      await generator.generate({
+        prompt: 'Performance test'
+      });
+
+      const duration = Date.now() - startTime;
+      expect(duration).toBeLessThan(2000); // Should complete in under 2 seconds
+    });
+
+    it('should handle many iterations efficiently', async () => {
+      const generator = new SelfLearningGenerator({
+        ...config,
+        iterations: 20
+      });
+
+      const startTime = Date.now();
+      await generator.generate({
+        prompt: 'Many iterations test'
+      });
+      const duration = Date.now() - startTime;
+
+      expect(duration).toBeLessThan(5000); // Even with 20 iterations
+    });
+  });
+});

npm/packages/agentic-synth-examples/tests/generators/stock-market.test.ts

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+/**
+ * Tests for Stock Market Simulator
+ */
+
+import { describe, it, expect, beforeEach } from 'vitest';
+import { StockMarketSimulator } from '../../src/generators/stock-market.js';
+import type { StockSimulatorConfig, GenerateOptions } from '../../src/generators/stock-market.js';
+
+describe('StockMarketSimulator', () => {
+  let config: StockSimulatorConfig;
+
+  beforeEach(() => {
+    config = {
+      symbols: ['AAPL', 'GOOGL'],
+      startDate: '2024-01-01',
+      endDate: '2024-01-10',
+      volatility: 'medium'
+    };
+  });
+
+  describe('Initialization', () => {
+    it('should create simulator with valid config', () => {
+      const simulator = new StockMarketSimulator(config);
+      expect(simulator).toBeDefined();
+    });
+
+    it('should accept Date objects', () => {
+      const simulatorWithDates = new StockMarketSimulator({
+        ...config,
+        startDate: new Date('2024-01-01'),
+        endDate: new Date('2024-01-10')
+      });
+      expect(simulatorWithDates).toBeDefined();
+    });
+
+    it('should handle different volatility levels', () => {
+      const lowVol = new StockMarketSimulator({ ...config, volatility: 'low' });
+      const highVol = new StockMarketSimulator({ ...config, volatility: 'high' });
+
+      expect(lowVol).toBeDefined();
+      expect(highVol).toBeDefined();
+    });
+  });
+
+  describe('Data Generation', () => {
+    it('should generate OHLCV data for all symbols', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      expect(data.length).toBeGreaterThan(0);
+
+      // Check that all symbols are present
+      const symbols = new Set(data.map(d => d.symbol));
+      expect(symbols.has('AAPL')).toBe(true);
+      expect(symbols.has('GOOGL')).toBe(true);
+    });
+
+    it('should generate correct number of trading days', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      // Should have data points for both symbols
+      const aaplData = data.filter(d => d.symbol === 'AAPL');
+      const googlData = data.filter(d => d.symbol === 'GOOGL');
+
+      expect(aaplData.length).toBeGreaterThan(0);
+      expect(googlData.length).toBeGreaterThan(0);
+    });
+
+    it('should skip weekends by default', async () => {
+      const simulator = new StockMarketSimulator({
+        symbols: ['AAPL'],
+        startDate: '2024-01-06', // Saturday
+        endDate: '2024-01-08', // Monday
+        volatility: 'medium'
+      });
+      const data = await simulator.generate();
+
+      // Should only have Monday's data, not Saturday or Sunday
+      expect(data.length).toBe(1);
+      expect(data[0].date.getDay()).not.toBe(0); // Not Sunday
+      expect(data[0].date.getDay()).not.toBe(6); // Not Saturday
+    });
+
+    it('should include weekends when configured', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        includeWeekends: true,
+        startDate: '2024-01-06', // Saturday
+        endDate: '2024-01-08' // Monday
+      });
+      const data = await simulator.generate();
+
+      const aaplData = data.filter(d => d.symbol === 'AAPL');
+      expect(aaplData.length).toBe(3); // Saturday, Sunday, Monday
+    });
+  });
+
+  describe('OHLCV Data Validation', () => {
+    it('should generate valid OHLCV data', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      data.forEach(point => {
+        expect(point.open).toBeGreaterThan(0);
+        expect(point.high).toBeGreaterThan(0);
+        expect(point.low).toBeGreaterThan(0);
+        expect(point.close).toBeGreaterThan(0);
+        expect(point.volume).toBeGreaterThan(0);
+
+        // High should be highest
+        expect(point.high).toBeGreaterThanOrEqual(point.open);
+        expect(point.high).toBeGreaterThanOrEqual(point.close);
+        expect(point.high).toBeGreaterThanOrEqual(point.low);
+
+        // Low should be lowest
+        expect(point.low).toBeLessThanOrEqual(point.open);
+        expect(point.low).toBeLessThanOrEqual(point.close);
+        expect(point.low).toBeLessThanOrEqual(point.high);
+      });
+    });
+
+    it('should have reasonable price ranges', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      data.forEach(point => {
+        // Prices should be in a reasonable range (not negative, not absurdly high)
+        expect(point.open).toBeLessThan(10000);
+        expect(point.high).toBeLessThan(10000);
+        expect(point.low).toBeLessThan(10000);
+        expect(point.close).toBeLessThan(10000);
+
+        // Price precision (2 decimal places)
+        expect(point.open.toString().split('.')[1]?.length || 0).toBeLessThanOrEqual(2);
+        expect(point.close.toString().split('.')[1]?.length || 0).toBeLessThanOrEqual(2);
+      });
+    });
+
+    it('should have realistic volume', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      data.forEach(point => {
+        expect(Number.isInteger(point.volume)).toBe(true);
+        expect(point.volume).toBeGreaterThan(1000000); // At least 1M volume
+        expect(point.volume).toBeLessThan(1000000000); // Less than 1B volume
+      });
+    });
+  });
+
+  describe('Market Conditions', () => {
+    it('should generate bullish trends', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-01',
+        endDate: '2024-01-30'
+      });
+      const data = await simulator.generate({ marketConditions: 'bullish' });
+
+      const aaplData = data.filter(d => d.symbol === 'AAPL').sort((a, b) => a.date.getTime() - b.date.getTime());
+
+      if (aaplData.length > 5) {
+        const firstPrice = aaplData[0].close;
+        const lastPrice = aaplData[aaplData.length - 1].close;
+
+        // Bullish market should trend upward (with some tolerance for randomness)
+        // Over 30 days, we expect positive movement more often than not
+        const priceChange = ((lastPrice - firstPrice) / firstPrice) * 100;
+        // Allow for some randomness, but generally should be positive
+      }
+    });
+
+    it('should generate bearish trends', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-01',
+        endDate: '2024-01-30'
+      });
+      const data = await simulator.generate({ marketConditions: 'bearish' });
+
+      expect(data.length).toBeGreaterThan(0);
+      // Bearish trends are applied but due to randomness, actual direction may vary
+    });
+
+    it('should generate neutral market', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-01',
+        endDate: '2024-01-30'
+      });
+      const data = await simulator.generate({ marketConditions: 'neutral' });
+
+      expect(data.length).toBeGreaterThan(0);
+      // Neutral market should have balanced ups and downs
+    });
+  });
+
+  describe('Volatility Levels', () => {
+    it('should reflect different volatility in price movements', async () => {
+      const lowVolSimulator = new StockMarketSimulator({ ...config, volatility: 'low' });
+      const highVolSimulator = new StockMarketSimulator({ ...config, volatility: 'high' });
+
+      const lowVolData = await lowVolSimulator.generate();
+      const highVolData = await highVolSimulator.generate();
+
+      // Both should generate data
+      expect(lowVolData.length).toBeGreaterThan(0);
+      expect(highVolData.length).toBeGreaterThan(0);
+
+      // Calculate average daily price range for comparison
+      const calcAvgRange = (data: any[]) => {
+        const ranges = data.map(d => ((d.high - d.low) / d.close) * 100);
+        return ranges.reduce((a, b) => a + b, 0) / ranges.length;
+      };
+
+      const lowAvgRange = calcAvgRange(lowVolData.filter(d => d.symbol === 'AAPL'));
+      const highAvgRange = calcAvgRange(highVolData.filter(d => d.symbol === 'AAPL'));
+
+      // High volatility should generally have larger ranges (with some tolerance)
+      // Due to randomness, this might not always hold, so we just check they're different
+      expect(lowAvgRange).toBeGreaterThan(0);
+      expect(highAvgRange).toBeGreaterThan(0);
+    });
+  });
+
+  describe('Optional Features', () => {
+    it('should include sentiment when requested', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate({ includeSentiment: true });
+
+      data.forEach(point => {
+        expect(point.sentiment).toBeDefined();
+        expect(point.sentiment).toBeGreaterThanOrEqual(-1);
+        expect(point.sentiment).toBeLessThanOrEqual(1);
+      });
+    });
+
+    it('should not include sentiment by default', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      // Most points should not have sentiment
+      const withSentiment = data.filter(d => d.sentiment !== undefined);
+      expect(withSentiment.length).toBe(0);
+    });
+
+    it('should include news when requested', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-01',
+        endDate: '2024-02-01' // Longer period for more news events
+      });
+      const data = await simulator.generate({ includeNews: true });
+
+      // Should have some news events (10% probability per day)
+      const withNews = data.filter(d => d.news && d.news.length > 0);
+      expect(withNews.length).toBeGreaterThan(0);
+
+      withNews.forEach(point => {
+        expect(Array.isArray(point.news)).toBe(true);
+        expect(point.news!.length).toBeGreaterThan(0);
+        point.news!.forEach(headline => {
+          expect(typeof headline).toBe('string');
+          expect(headline.length).toBeGreaterThan(0);
+        });
+      });
+    });
+
+    it('should not include news by default', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      const withNews = data.filter(d => d.news && d.news.length > 0);
+      expect(withNews.length).toBe(0);
+    });
+  });
+
+  describe('Date Handling', () => {
+    it('should generate data in correct date range', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      const startDate = new Date('2024-01-01');
+      const endDate = new Date('2024-01-10');
+
+      data.forEach(point => {
+        expect(point.date.getTime()).toBeGreaterThanOrEqual(startDate.getTime());
+        expect(point.date.getTime()).toBeLessThanOrEqual(endDate.getTime());
+      });
+    });
+
+    it('should sort data by date', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      // Data should be sorted by date
+      for (let i = 1; i < data.length; i++) {
+        expect(data[i].date.getTime()).toBeGreaterThanOrEqual(data[i - 1].date.getTime());
+      }
+    });
+
+    it('should handle single day generation', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-15',
+        endDate: '2024-01-15'
+      });
+      const data = await simulator.generate();
+
+      const aaplData = data.filter(d => d.symbol === 'AAPL');
+      expect(aaplData.length).toBe(1);
+      expect(aaplData[0].date.toISOString().split('T')[0]).toBe('2024-01-15');
+    });
+  });
+
+  describe('Statistics', () => {
+    it('should calculate market statistics', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-01',
+        endDate: '2024-01-30'
+      });
+      const data = await simulator.generate();
+
+      const aaplData = data.filter(d => d.symbol === 'AAPL');
+      const stats = simulator.getStatistics(aaplData);
+
+      expect(stats.totalDays).toBe(aaplData.length);
+      expect(stats.avgPrice).toBeGreaterThan(0);
+      expect(stats.minPrice).toBeGreaterThan(0);
+      expect(stats.maxPrice).toBeGreaterThan(0);
+      expect(stats.avgVolume).toBeGreaterThan(0);
+      expect(typeof stats.priceChange).toBe('number');
+      expect(stats.volatility).toBeGreaterThan(0);
+
+      // Min should be less than avg, avg less than max
+      expect(stats.minPrice).toBeLessThanOrEqual(stats.avgPrice);
+      expect(stats.avgPrice).toBeLessThanOrEqual(stats.maxPrice);
+    });
+
+    it('should handle empty data for statistics', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const stats = simulator.getStatistics([]);
+
+      expect(stats).toEqual({});
+    });
+
+    it('should calculate volatility correctly', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      const aaplData = data.filter(d => d.symbol === 'AAPL');
+      const stats = simulator.getStatistics(aaplData);
+
+      expect(stats.volatility).toBeGreaterThan(0);
+      expect(stats.volatility).toBeLessThan(100); // Reasonable volatility range
+    });
+  });
+
+  describe('Multiple Symbols', () => {
+    it('should handle single symbol', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        symbols: ['AAPL']
+      });
+      const data = await simulator.generate();
+
+      expect(data.every(d => d.symbol === 'AAPL')).toBe(true);
+    });
+
+    it('should handle many symbols', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        symbols: ['AAPL', 'GOOGL', 'MSFT', 'AMZN', 'TSLA']
+      });
+      const data = await simulator.generate();
+
+      const symbols = new Set(data.map(d => d.symbol));
+      expect(symbols.size).toBe(5);
+      expect(symbols.has('AAPL')).toBe(true);
+      expect(symbols.has('TSLA')).toBe(true);
+    });
+
+    it('should generate independent data for each symbol', async () => {
+      const simulator = new StockMarketSimulator(config);
+      const data = await simulator.generate();
+
+      const aaplData = data.filter(d => d.symbol === 'AAPL');
+      const googlData = data.filter(d => d.symbol === 'GOOGL');
+
+      // Prices should be different (independent generation)
+      expect(aaplData[0].close).not.toBe(googlData[0].close);
+    });
+  });
+
+  describe('Edge Cases', () => {
+    it('should handle very short time period', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-02',
+        endDate: '2024-01-02'
+      });
+      const data = await simulator.generate();
+
+      expect(data.length).toBeGreaterThan(0);
+    });
+
+    it('should handle long time periods', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-01',
+        endDate: '2024-12-31'
+      });
+      const data = await simulator.generate();
+
+      // Should have roughly 252 trading days * 2 symbols
+      expect(data.length).toBeGreaterThan(400);
+    });
+
+    it('should handle unknown symbols gracefully', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        symbols: ['UNKNOWN', 'FAKE']
+      });
+      const data = await simulator.generate();
+
+      // Should still generate data with default prices
+      expect(data.length).toBeGreaterThan(0);
+      data.forEach(point => {
+        expect(point.close).toBeGreaterThan(0);
+      });
+    });
+  });
+
+  describe('Performance', () => {
+    it('should generate data efficiently', async () => {
+      const simulator = new StockMarketSimulator({
+        ...config,
+        startDate: '2024-01-01',
+        endDate: '2024-03-31',
+        symbols: ['AAPL', 'GOOGL', 'MSFT']
+      });
+
+      const startTime = Date.now();
+      await simulator.generate();
+      const duration = Date.now() - startTime;
+
+      // Should complete quickly even with 3 months of data
+      expect(duration).toBeLessThan(1000);
+    });
+  });
+});