TradePulse Sample Data

This directory contains sample datasets for testing, development, and demonstration purposes.

Available Datasets

File	Description	Rows	Use Case
sample.csv	Simple price/volume time series	500	Basic indicator testing
sample_ohlc.csv	OHLCV candlestick data	300	Advanced indicator testing
sample_crypto_ohlcv.csv	Multi-asset crypto (BTC, ETH, SOL)	504	Multi-asset analysis
sample_stocks_daily.csv	Daily stock data (AAPL, SPY)	60	Portfolio analysis

Golden Datasets

The golden/ subdirectory contains versioned baseline datasets for regression testing:

• indicator_macd_baseline.csv - MACD indicator validation data

Data Formats

Simple Price/Volume (sample.csv)

ts,price,volume
1,100.15,1010
2,100.30,1020
...

OHLCV Format (sample_ohlc.csv, sample_crypto_ohlcv.csv)

timestamp,symbol,open,high,low,close,volume
2024-01-01 00:00:00+00:00,BTC,45173.76,45200.71,44948.90,45084.24,7985.28
...

Generating New Sample Data

Use the data generation script to create custom datasets:

# Generate single asset data
python scripts/generate_sample_ohlcv.py --output data/my_data.csv --symbols ASSET

# Generate multi-asset crypto data
python scripts/generate_sample_ohlcv.py --symbols BTC ETH SOL --days 30 --timeframe 1h

# Generate daily stock data
python scripts/generate_sample_ohlcv.py --symbols AAPL SPY --days 90 --timeframe 1d

# Generate 1-minute high-frequency data
python scripts/generate_sample_ohlcv.py --symbols BTC --days 1 --timeframe 1m

Command Options

Option	Description	Default
--output, -o	Output file path	data/sample_ohlcv_generated.csv
--symbols, -s	Asset symbols to generate	ASSET
--days, -d	Number of days of data	7
--timeframe, -t	Bar timeframe (1m, 5m, 15m, 30m, 1h, 4h, 1d)	1h
--seed	Random seed for reproducibility	42
--start-date	Start date in ISO format	2024-01-01

Data Validation

Validate data quality before using in analysis:

from core.data.validation import validate_ohlcv
import pandas as pd

df = pd.read_csv("data/sample_crypto_ohlcv.csv")
result = validate_ohlcv(df, price_col="close")

if result.valid:
    print("Data passed validation")
else:
    print(f"Issues: {result.issues}")

Run validation script on golden datasets:

python scripts/data_sanity.py data/golden

Usage Examples

Quick Start Analysis

import pandas as pd
from core.indicators.kuramoto_ricci_composite import TradePulseCompositeEngine

# Load sample data
df = pd.read_csv("data/sample_crypto_ohlcv.csv")
btc = df[df["symbol"] == "BTC"].copy()
btc.set_index("timestamp", inplace=True)

# Analyze market regime
engine = TradePulseCompositeEngine()
snapshot = engine.analyze_market(btc)

print(f"Phase: {snapshot.phase.value}")
print(f"Confidence: {snapshot.confidence:.3f}")

Backtesting

import numpy as np
import pandas as pd
from backtest.event_driven import EventDrivenBacktestEngine

# Load data
df = pd.read_csv("data/sample_ohlc.csv")
prices = df["close"].values

# Define simple momentum strategy
def momentum_signal(prices):
    returns = np.diff(prices, prepend=prices[0])
    signal = np.where(returns > 0, 1.0, -1.0)
    signal[:20] = 0  # Warmup
    return signal

# Run backtest
engine = EventDrivenBacktestEngine()
result = engine.run(prices, momentum_signal, initial_capital=100_000)

print(f"PnL: ${result.pnl:,.2f}")
print(f"Max Drawdown: {result.max_dd:.2%}")

Maintenance

• Sample datasets should remain stable for reproducibility
• Update checksums in docs/data/sample_market_data.md when modifying files
• Add new datasets to this README when created
• Run validation tests before committing changes

Related Documentation

• Data Validation Guide
• Testing Guide
• Examples