🥷 NinjaQuant – Injective Strategy Backtesting API

Injective Python FastAPI Quant Framework

Demo : https://devtrad.onrender.com/docs

Repo : https://github.com/Suganthan96/Devtrad

🚀 Built on Injective Mainnet

NinjaQuant is a production-ready quantitative backtesting and analytics API built on Injective Mainnet perpetual futures markets.

It provides a structured intelligence layer on top of Injective’s derivatives infrastructure, enabling:

• Strategy backtesting

• Parameter comparison

• Market regime detection

• Risk analytics

• Professional performance evaluation

🔗 Injective Mainnet Integration

✅ REAL Injective Market IDs (Mainnet Verified)

This API uses hardcoded, blockchain-verified Market IDs directly from Injective Mainnet.

📊 Supported Markets (Mainnet)

🔐 Verification Details

• ✅ Mainnet Market IDs

• ✅ Verified on Injective Explorer

• ✅ Connected to Pyth Oracle feeds

• ✅ Blockchain-validated market mapping

Server logs confirm usage:

✅ Using Injective Market ID
Market ID: 0x9b9980167ecc3645ff1a5517886652d94a0825e54a77d2057cbbe3ebee015963
Oracle: Pyth

🎯 Problem

Injective provides rich on-chain derivatives data.

However, developers lack:

• A structured backtesting engine

• Risk-adjusted performance metrics

• Market condition analytics

• Strategy comparison tools

• A quant abstraction layer

Most APIs expose raw data — not evaluated trading intelligence.

💡 Solution

NinjaQuant provides a modular FastAPI-based quant intelligence engine that:

• Uses verified Injective Mainnet Market IDs

• Fetches historical OHLCV data

• Executes strategy simulations

• Computes professional metrics

• Classifies market conditions

• Performs risk analysis

• Compares multiple strategies in one request

It transforms Injective into a quant research-ready ecosystem.

📡 API Routes

🧪 Core Backtesting Endpoints

🔹 POST /backtest/ema-crossover

Backtest EMA crossover strategy on Injective Mainnet markets.

POST http://devtrad.onrender.com/backtest/ema-crossover

Content-Type: application/json

{

"market": "BTC/USDT PERP",

"timeframe": "1h",

"parameters": {

"short_period": 9,

"long_period": 21

},

"initial_capital": 10000

}

🔹 POST /backtest/rsi-mean-reversion

Backtest RSI mean reversion strategy.

POST https://devtrad.onrender.com/backtest/rsi-mean-reversion
Content-Type: application/json
{

"market": "ETH/USDT PERP",

"timeframe": "1h",

"strategy": "rsi_mean_reversion",

"parameters": {

"period": 14,

"oversold": 30,

"overbought": 70

},

"initial_capital": 10000

}

🚀 Advanced Quant APIs

🔬 POST /compare

Compare multiple strategy configurations in one request.

Example:

• EMA(9,21) vs EMA(12,26)

• RSI(14,30,70)

Automatically identifies best performing strategy.

POST http://devtrad.onrender.com/compare

Content-Type: application/json
{

"market": "BTC/USDT PERP",

"timeframe": "1h",

"strategies": [

{

"strategy": "ema_crossover",

"parameters": {

"short_period": 9,

"long_period": 21

}

},

{

"strategy": "ema_crossover",

"parameters": {

"short_period": 12,

"long_period": 26

}

},

{

"strategy": "rsi_mean_reversion",

"parameters": {

"period": 14,

"oversold": 30,

"overbought": 70

}

}

],

"initial_capital": 10000

}

🌡️ GET /market-regime

Classifies current market condition.

Returns:

• Trending / Ranging / Volatile

• Trend strength

• Volatility level

• Strategy recommendation
  

  GET http://devtrad.onrender.com/market-regime?market=BTC/USDT PERP&timeframe=1h

  Content-Type: application/json

📊 POST /risk-analysis

Professional risk metrics:

• Return volatility

• Value at Risk (VaR)

• Max consecutive losses

• Risk classification (Low / Medium / High)
  
  POST http://devtrad.onrender.com/risk-analysis
  Content-Type: application/json
  {

  "market": "ETH/USDT PERP",

  "timeframe": "1h",

  "strategy": "rsi_mean_reversion",

  "parameters": {

  "period": 14,

  "oversold": 30,

  "overbought": 70

  },

  "initial_capital": 10000

  }

🧠 Strategy Engine

1️⃣ EMA Crossover Strategy

• Uses Short EMA & Long EMA

• Golden Cross → Buy

• Death Cross → Sell

• Best for trending markets

2️⃣ RSI Mean Reversion Strategy

• Uses RSI momentum oscillator

• Oversold → Buy

• Overbought → Sell

• Best for range-bound markets

📊 Standardized Performance Metrics

Each backtest returns:

• Win Rate

• Total Return

• Maximum Drawdown

• Sharpe Ratio

• Total Trades

Formulas:

• Win Rate = Profitable Trades / Total Trades

• Total Return = (Final − Initial) / Initial

• Max Drawdown = Largest peak-to-trough decline

• Sharpe Ratio = Mean Return / Std Dev Return

🏗 Architecture Overview

Injective Mainnet Data
        ↓
Data Layer (injective_client.py)
        ↓
Strategy Engine
        ↓
Metrics Engine
        ↓
FastAPI Routes
        ↓
Structured JSON Output