tradix 1.0.0

Blazing-fast vectorized backtesting engine for quantitative trading. 50+ indicators, 33 preset strategies, Strategy DNA, Korean market native.

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Blazing-Fast Vectorized Backtesting Engine

Quick Start · Why Tradix? · Features · Installation · API Reference · 한국어

Two lines. backtest · show — that's the entire workflow.

from tradix import backtest, goldenCross

result = backtest("AAPL", goldenCross())
result.show()

◈ Quick Start

pip install tradix

from tradix import backtest, goldenCross

result = backtest("005930", goldenCross())
result.show()
result.show(style="bloomberg")

backtest() auto-downloads data via FinanceDataReader, runs the strategy with commission and slippage, and returns a full result object.

=== Backtest Result ===
Strategy: GoldenCross
Period: 2020-01-02 ~ 2024-12-30
Initial: 10,000,000 KRW
Final:   14,230,000 KRW
Return:  +42.30%
Sharpe:  1.23
Max DD:  -12.45%
Trades:  18
Win Rate: 61.1%

◈ Why Tradix?

Dimension Tradix Backtrader Zipline bt Zero-config backtest ✅ ❌ ❌ ❌ 2-line API ✅ ❌ ❌ ❌ Vectorized engine ✅ ❌ ❌ ✅ 1K optimizations < 1s ✅ ❌ ❌ ❌ Built-in data feed ✅ ❌ ❌ ❌ 33 preset strategies ✅ ❌ ❌ ❌ Terminal dashboard ✅ ❌ ❌ ❌ Korean market native ✅ ❌ ❌ ❌ Walk-forward analysis ✅ ❌ ❌ ❌ Strategy DNA analysis ✅ ❌ ❌ ❌

◈ Features

Core Engine

Feature	Description
Vectorized Engine	NumPy-powered core, 100x faster than event-driven loops
60+ Indicators	SMA, EMA, RSI, MACD, Bollinger, ATR, Ichimoku, Supertrend, StochRSI, KDJ, and more
33 Preset Strategies	Trend, momentum, oscillator, volatility, multi-indicator, buy & hold, DCA
Strategy Builder	Declarative method chaining — no subclassing needed
Walk-Forward	Built-in overfitting prevention with time-series cross-validation
Optimization	Grid search and random search with any metric
Multi-Asset	Backtest across multiple symbols with rebalancing
Realistic Simulation	Commission, slippage, fill logic, position sizing

Advanced Analytics

Feature	Description
Strategy DNA	12-dimensional strategy fingerprinting
Black Swan Defense	Extreme event resilience scoring (0-100)
Health Score	Overfitting risk, parameter stability diagnostics
What-If Simulator	Commission, slippage, capital sensitivity analysis
Drawdown Simulator	Historical worst-case scenario generation
Seasonality Analyzer	Monthly, weekday, quarterly pattern discovery
Correlation Matrix	Multi-strategy correlation and clustering
Trading Journal	Auto trade diary with MFE/MAE analytics
Strategy Leaderboard	Multi-strategy ranking with badge system

Innovative Analytics

Feature	Description
Monte Carlo	10K-path bootstrap with ruin probability and confidence bands
Fractal Analysis	Hurst exponent for market character classification
Regime Detector	GMM-based probabilistic regime detection with transition matrix
Information Theory	Shannon entropy, mutual information for signal quality
Portfolio Stress	6 crisis scenarios (crash, volatility spike, rate shock, etc.)

Terminal UI — TradingView-Inspired

Feature	Description
3 Display Styles	Modern (TradingView), Bloomberg (dense 4-quadrant), Minimal (hedge fund)
12 Chart Types	Equity, drawdown, candlestick, returns, seasonality, heatmap, DNA, and more
Interactive Dashboard	5-view Textual app with keyboard navigation
CLI	tradix backtest, tradix chart, tradix compare, tradix optimize, tradix list

Korean Market

Feature	Description
Native Support	Built-in transaction tax (0.18%), brokerage fees, KRX stock mapping
Korean API	Full Korean function names: 백테스트("삼성전자", 골든크로스())

◈ Installation

Using uv (Recommended)

uv is the fastest Python package manager.

uv init my-backtest && cd my-backtest
uv add tradix

uv add "tradix[full]"

Using pip

pip install tradix

pip install "tradix[full]"

From source

git clone https://github.com/eddmpython/tradix.git
cd tradix
uv sync --dev

Requirements: Python 3.9+, NumPy, Pandas

◈ Usage

Declarative Strategy Builder

from tradix import QuickStrategy, backtest, sma, rsi, crossover, crossunder

strategy = (
    QuickStrategy("MomentumRSI")
    .buyWhen(crossover(sma(10), sma(30)))
    .buyWhen(rsi(14) < 30)
    .sellWhen(crossunder(sma(10), sma(30)))
    .sellWhen(rsi(14) > 70)
    .stopLoss(5)
    .takeProfit(15)
)

result = backtest("AAPL", strategy)
result.show()

Parameter Optimization

from tradix import voptimize

best = voptimize(
    "005930",
    "goldenCross",
    fast=(5, 20, 5),
    slow=(20, 60, 10),
    metric="sharpeRatio"
)

print(f"Best: fast={best['best']['params']['fast']}, slow={best['best']['params']['slow']}")
print(f"Sharpe: {best['best']['metric']:.2f}")

Vectorized Mode (100x Faster)

from tradix import vbacktest

result = vbacktest("005930", "goldenCross", fast=10, slow=30)
print(f"Return: {result.totalReturn:+.2f}%")
print(f"Sharpe: {result.sharpeRatio:.2f}")
print(f"Max DD: {result.maxDrawdown:.2f}%")

Korean API

from tradix import 백테스트, 골든크로스

결과 = 백테스트("삼성전자", 골든크로스())
결과.보기()
결과.차트()

More Examples — Class-based strategy, walk-forward, multi-asset, risk

Class-Based Strategy

from tradix import Strategy, Bar, BacktestEngine
from tradix.datafeed import FinanceDataReaderFeed

class DualMomentum(Strategy):
    def initialize(self):
        self.fastPeriod = 10
        self.slowPeriod = 30

    def onBar(self, bar: Bar):
        fast = self.sma(self.fastPeriod)
        slow = self.sma(self.slowPeriod)

        if fast is None or slow is None:
            return

        if fast > slow and not self.hasPosition(bar.symbol):
            self.buy(bar.symbol)
        elif fast < slow and self.hasPosition(bar.symbol):
            self.closePosition(bar.symbol)

data = FinanceDataReaderFeed("005930", "2020-01-01", "2024-12-31")
engine = BacktestEngine(data, DualMomentum(), initialCash=10_000_000)
result = engine.run()
result.show()

Walk-Forward Analysis

from tradix import Strategy, Bar, WalkForwardAnalyzer, ParameterSpace
from tradix.datafeed import FinanceDataReaderFeed

def createStrategy(params):
    class MySma(Strategy):
        def initialize(self):
            self.fast = params["fast"]
            self.slow = params["slow"]
        def onBar(self, bar: Bar):
            f, s = self.sma(self.fast), self.sma(self.slow)
            if f and s:
                if f > s and not self.hasPosition(bar.symbol):
                    self.buy(bar.symbol)
                elif f < s and self.hasPosition(bar.symbol):
                    self.closePosition(bar.symbol)
    return MySma()

data = FinanceDataReaderFeed("005930", "2020-01-01", "2024-12-31")
space = ParameterSpace()
space.addInt("fast", 5, 20, step=5)
space.addInt("slow", 20, 60, step=10)

wfa = WalkForwardAnalyzer(
    data=data,
    strategyFactory=createStrategy,
    parameterSpace=space,
    inSampleMonths=12,
    outOfSampleMonths=3,
)

result = wfa.run()
print(f"Robustness: {result.robustnessRatio:.1%}")

Multi-Asset Portfolio

from tradix import MultiAssetEngine, MultiAssetStrategy

class EqualWeight(MultiAssetStrategy):
    def onBars(self, bars):
        self.rebalance({
            "005930": 0.4,
            "000660": 0.3,
            "035420": 0.3,
        })

engine = MultiAssetEngine(strategy=EqualWeight())
result = engine.run()
result.show()

Risk Simulation

import pandas as pd
from tradix import backtest, goldenCross
from tradix.risk import RiskSimulator, VaRMethod

result = backtest("005930", goldenCross())
returns = pd.DataFrame({"returns": pd.Series(result.equityCurve).pct_change().dropna()})

simulator = RiskSimulator()
simulator.fit(returns)

var95, cvar95 = simulator.calcVaR(confidence=0.95, method=VaRMethod.HISTORICAL)
print(f"95% VaR: {var95:.2%}")
print(f"CVaR: {cvar95:.2%}")

mc = simulator.monteCarloSimulation(horizon=252, nSim=10000)

◈ Strategies

Trend Following (9)

Strategy	Description
goldenCross()	SMA crossover (fast/slow)
emaCross()	EMA crossover
tripleEma()	Triple EMA crossover
trendFollowing()	ADX-filtered trend following with trailing stop
superTrend()	Supertrend indicator reversal
ichimokuCloud()	Ichimoku cloud breakout
parabolicSar()	Parabolic SAR reversal
donchianBreakout()	Donchian channel breakout
breakout()	Channel breakout (Turtle Trading)

Momentum & Oscillator (8)

Strategy	Description
rsiOversold()	RSI reversal (oversold/overbought)
macdCross()	MACD histogram crossover
stochasticCross()	Stochastic K/D crossover
williamsReversal()	Williams %R reversal
cciBreakout()	CCI overbought/oversold breakout
rsiDivergence()	RSI divergence detection
momentumCross()	Momentum zero-line crossover
rocBreakout()	Rate of Change breakout

Volatility (5)

Strategy	Description
bollingerBreakout()	Bollinger band breakout
bollingerSqueeze()	Bollinger squeeze expansion
keltnerChannel()	Keltner channel breakout
volatilityBreakout()	ATR-based volatility breakout
meanReversion()	Bollinger mean reversion

Multi-Indicator (5)

Strategy	Description
tripleScreen()	Elder's triple screen system
dualMomentum()	Absolute + relative momentum
macdRsiCombo()	MACD + RSI combined signal
trendMomentum()	Trend + momentum filter
bollingerRsi()	Bollinger + RSI combined

Special (6)

Strategy	Description
gapTrading()	Gap up/down trading
pyramiding()	Pyramiding position building
swingTrading()	Swing high/low trading
scalpingMomentum()	Short-term momentum scalping
buyAndHold()	Passive buy and hold
dollarCostAverage()	Dollar cost averaging

◈ Indicators

Category	Indicators
Moving Averages	sma ema wma hma tema dema vwma alma
Momentum	rsi macd stochastic roc momentum cci williamsR cmo stochasticRsi kdj awesomeOscillator ultimateOscillator
Volatility	atr bollinger keltner donchian bollingerPercentB bollingerWidth
Volume	obv vwap mfi adl chaikin emv forceIndex nvi pvi vroc pvt klingerOscillator
Trend	adx supertrend psar ichimoku trix dpo linearRegression
Price	pivotPoints fibonacciRetracement zigzag elderRay twap
Other	ulcer percentChange highest lowest

◈ Performance

Benchmarked on 10 years of daily data (2,458 bars):

Operation	Time
SMA calculation	0.006ms
RSI calculation	0.009ms
MACD calculation	0.040ms
Full backtest (single)	0.132ms
1,000 param optimization	0.02s

◈ Terminal UI

Display Styles

result.show()                     # Modern — TradingView metric cards
result.show(style="bloomberg")    # Bloomberg — dense 4-quadrant layout
result.show(style="minimal")      # Minimal — clean hedge fund report

CLI

tradix backtest AAPL -s goldenCross --dashboard
tradix backtest AAPL -s bollingerSqueeze --style bloomberg
tradix chart AAPL -n 60
tradix compare AAPL -s goldenCross,rsiOversold
tradix optimize AAPL -s goldenCross
tradix list

Charts (12 Types)

from tradix.tui.charts import (
    plotEquityCurve, plotDrawdown, plotCandlestick, plotReturns,
    plotSeasonality, plotMonthlyHeatmap, plotRollingMetrics,
    plotTradeScatter, plotTradeMarkers, plotCorrelationBars,
    plotStrategyDna, plotDashboard,
)

plotEquityCurve(result, smaPeriods=[20, 60])
plotDrawdown(result)
plotCandlestick(df, smaPeriods=[5, 20])
plotDashboard(result, lang="ko")

Interactive Dashboard

pip install tradix[tui]

from tradix.tui.dashboard import launchDashboard
launchDashboard(result)

◈ Advanced Analytics

from tradix import (
    StrategyDnaAnalyzer, BlackSwanAnalyzer, StrategyHealthAnalyzer,
    WhatIfSimulator, DrawdownSimulator, SeasonalityAnalyzer,
    CorrelationAnalyzer, TradingJournal, StrategyLeaderboard,
)

result = backtest("005930", goldenCross())

dna = StrategyDnaAnalyzer().analyze(result)
printStrategyDna(dna)

health = StrategyHealthAnalyzer().analyze(result)
printHealthScore(health)

blackSwan = BlackSwanAnalyzer().analyze(result)
printBlackSwanScore(blackSwan)

from tradix import (
    MonteCarloStressAnalyzer, FractalAnalyzer, RegimeDetector,
    InformationTheoryAnalyzer, PortfolioStressAnalyzer,
)

mc = MonteCarloStressAnalyzer().analyze(result, paths=10000)
print(f"Ruin probability: {mc.ruinProbability:.2%}")

fractal = FractalAnalyzer().analyze(result)
print(f"Hurst: {fractal.hurstExponent:.3f} → {fractal.marketCharacter}")

regime = RegimeDetector().analyze(result)
print(f"Current regime: {regime.currentRegime}")

◈ API Reference

Core Functions

Function	Description
backtest(symbol, strategy)	Run a backtest with a preset or custom strategy
vbacktest(symbol, strategy, **params)	Run a vectorized backtest
voptimize(symbol, strategy, **ranges)	Grid search parameter optimization
BacktestEngine(data, strategy)	Event-driven backtest engine
VectorizedEngine(initialCash)	Vectorized backtest engine
QuickStrategy(name)	Declarative strategy builder

Strategy Builder Methods

Method	Description
.buyWhen(condition)	Add buy condition
.sellWhen(condition)	Add sell condition
.stopLoss(pct)	Set stop loss percentage
.takeProfit(pct)	Set take profit percentage
.trailingStop(pct)	Set trailing stop percentage

Condition Builders

Function	Returns
sma(period)	SMA indicator
ema(period)	EMA indicator
rsi(period)	RSI indicator
macd(fast, slow, signal)	MACD indicator
bollinger(period, std)	Bollinger Bands
atr(period)	ATR indicator
price()	Current price
crossover(fast, slow)	Crossover condition
crossunder(fast, slow)	Crossunder condition

Indicators support comparison operators: sma(10) > sma(30), rsi(14) < 30

◈ Architecture

tradix/
├── engine.py              # Core backtest engine
├── multiAssetEngine.py    # Multi-asset portfolio engine
├── strategy/              # Strategy base + 60+ indicators + ensemble
├── easy/                  # 2-line API, presets, Korean API
├── vectorized/            # Vectorized engine + indicators (Pure NumPy)
├── datafeed/              # Data feeds (FinanceDataReader + Parquet cache)
├── broker/                # Commission, slippage, fill simulation
├── risk/                  # Position sizing, VaR, Monte Carlo
├── optimize/              # Grid / random search optimizer
├── walkforward/           # Walk-forward analysis
├── analytics/             # Strategy DNA, Black Swan, Health Score, etc.
├── portfolio/             # Portfolio tracking + optimization
├── quant/                 # Factor analysis, statistical arbitrage
├── signals/               # Signal prediction + adaptive signals
├── advisor/               # Market regime + strategy recommendation
├── entities/              # Bar, Order, Position, Trade
├── events/                # Event system
├── tui/                   # Terminal UI (Rich + Plotext + Textual)
├── cli.py                 # Typer CLI
└── tests/                 # 87 tests

◈ Contributing

git clone https://github.com/eddmpython/tradix.git
cd tradix
uv sync --dev
uv run pytest

◈ Support

If Tradix helps your trading research, consider supporting the project:

◈ License

MIT License. Use it freely in personal and commercial projects.

Trade smarter. Backtest faster.