---

Table of Contents

1. Overview
2. Architecture
3. Core Modules
4. Technical Indicators
5. Risk Management
6. Backtest Engine
7. Usage
8. Performance
9. Installation
10. Configuration

---

1. Overview

QuantFlow is an institutional-grade algorithmic trading system designed for the Indian equity markets (NSE). It implements professional quant strategies with:

• Multi-timeframe market analysis (1m, 5m, 15m, 1h)
• 15+ technical indicators (VWAP, EMA, RSI, MACD, Bollinger, Stochastic)
• Candlestick pattern recognition
• Adaptive position sizing (Rs.100 - Rs.5,00,000)
• Kelly Criterion-based risk management
• Real-time data from yfinance/NSE
• Broker integration ready (Angel One SmartAPI)

Key Features

Feature	Implementation
Position Sizing	Adaptive Kelly Criterion
Risk per Trade	1-2% of capital
Max Daily Loss	6% auto-stop
Stop Loss	1.5x ATR
Target	2.5x ATR (minimum 2R)
Max Positions	1-5 concurrent

---

2. Architecture

Data Flow

1. Market data fetched from yfinance (real-time) or mock data (testing)
2. Technical indicators calculated (VWAP, RSI, MACD, etc.)
3. Signal generated based on confluence of factors
4. Position size calculated using Kelly Criterion
5. Risk manager validates trade
6. Order executed via broker API
7. Results logged for analytics

---

3. Core Modules

3.1 Market Data

Module	Purpose
market_analyzer.py	Real-time technical analysis with 15+ indicators
data_fetcher.py	OHLCV management, mock data provider
groww_connector.py	Groww API integration
live_data.py	WebSocket/REST data feeds

3.2 Trading Engine

Module	Purpose
professional_strategy.py	Institutional-grade signal generation
institutional_trading.py	Complete trading engine with backtest
real_backtest.py	Real NSE data backtesting
strategy_engine.py	Multi-signal quant engine

3.3 Risk Management

Module	Purpose
position_sizing.py	Adaptive position sizing with affordability check
portfolio_risk.py	Portfolio risk manager with drawdown protection
multiframe.py	Multi-timeframe confirmation

3.4 Order Execution

Module	Purpose
broker_angelone.py	Angel One SmartAPI integration
advanced_orders.py	Bracket orders, GTT, trailing stops
trading_system.py	Main trading system coordinator

3.5 Analytics

Module	Purpose
backtest_engine.py	Professional backtest with Sharpe/Sortino
stocks.py	Stock universe management
alerts.py	Alert system

---

4. Technical Indicators

The system calculates and uses the following indicators:

4.1 Trend Indicators

Indicator	Period	Purpose
SMA	9, 20, 50	Simple moving average
EMA	9, 21, 50	Exponential moving average
VWAP	50	Volume-weighted average price
MACD	12, 26, 9	Momentum convergence divergence

4.2 Momentum Indicators

Indicator	Period	Purpose
RSI	14	Relative strength index
Stochastic	14	Stochastic oscillator
CCI	20	Commodity channel index

4.3 Volatility Indicators

Indicator	Period	Purpose
ATR	14	Average true range
Bollinger Bands	20, 2	Price envelope

4.4 Volume Indicators

Indicator	Purpose
Volume Ratio	Current / Average volume
OBV	On-balance volume
VWAP Volume	Volume-weighted price

4.5 Candlestick Patterns

The system detects:

• Hammer / Hanging Man
• Morning Star / Evening Star
• Bullish / Bearish Engulfing
• Three White Soldiers / Three Black Crows
• Doji
• Marubozu

---

5. Risk Management

5.1 Position Sizing

The system uses Kelly Criterion with fractional Kelly (0.5):

Kelly% = W - ((1-W) / R)

Where:
  W = Win rate
  R = Win/Loss ratio

5.2 Risk Profiles

Budget	Profile	Max Risk	Max Positions
< Rs.500	ULTRASAFE	1%	1
Rs.500-5000	CONSERVATIVE	1.5%	2
Rs.5000-50000	MODERATE	2%	3
> Rs.50000	AGGRESSIVE	2%	5

5.3 Affordability Check

For low budgets (Rs.100-500), the system checks:

1. Stock price <= max affordable (budget * leverage)
2. Quantity calculation respects capital
3. Returns error if unaffordable

# Example: Rs.200 budget
max_affordable = 200 * 1.0  # No leverage for ultrasafe
if stock_price > max_affordable:
    return {"quantity": 0, "error": "Stock exceeds max affordable"}

5.4 Daily Limits

Limit	Value
Max Daily Loss	6% of capital
Max Consecutive Losses	3 (auto-disable trading)
Max Total Exposure	20% of capital

---

6. Backtest Engine

6.1 Analytics Provided

Metric	Description
Sharpe Ratio	Risk-adjusted return
Sortino Ratio	Downside risk-adjusted return
Max Drawdown	Largest peak-to-trough
Profit Factor	Gross profit / Gross loss
Win Rate	Winning trades / Total trades
Avg Holding Time	Mean trade duration

6.2 Backtest Results (Real NSE Data)

Symbol	Trades	Win Rate	P&L
RELIANCE	28	64%	+Rs.91.84
TCS	12	50%	+Rs.10.78
KOTAKBANK	28	43%	+Rs.22.36
INFY	12	50%	+Rs.2.60

6.3 Rs.200 Test

Metric	Value
Capital	Rs.200
Trades	10
Win Rate	50%
P&L	+Rs.0.25
Final Capital	Rs.200.25

---

7. Usage

7.1 Market Analysis

python3 market_analyzer.py

Output:

======================================================================
REAL-TIME MARKET ANALYZER - NSE DATA
======================================================================

======================================================================
MARKET SCAN RESULTS
======================================================================

    INFY       | ₹ 1179.90 |    +0.50 (+0.04%)
======================================================================
  Signal:   neutral    | Confidence: 22% | Trend: bullish
  Summary: NEUTRAL - Score: 22 | Price > SMA20 | Price > EMA9

--- Technical Indicators ---
  Price:     ₹1179.90 | Open: ₹1179.30 | High: ₹1180.00 | Low: ₹1179.00
  SMA:       9:1180.31 | 20:1179.35 | 50:1175.09
  EMA:       9:1179.81 | 21:1178.93 | 50:1176.19
  VWAP:      ₹ 1171.66 | RSI:  57 | MACD:   1.32

7.2 Professional Strategy

python3 professional_strategy.py

7.3 Backtest

python3 institutional_trading.py

7.4 System Tests

python3 test_system.py

Output:

======================================================================
COMPREHENSIVE SYSTEM TEST
======================================================================
✓ Position Sizing Engine
✓ Portfolio Risk Manager
✓ Multi-Timeframe Analysis
✓ Advanced Orders
✓ Strategy Engine
✓ Backtest Engine
✓ Groww Connector
✓ Data Fetcher
✓ Trading System
✓ Stock Configuration
✓ Alert System

======================================================================
RESULTS: 11 passed, 0 failed
======================================================================

---

8. Performance

8.1 System Test Results

Module Tests: 11 passed, 0 failed

8.2 Backtest Performance (15 days)

Symbol	Trades	Win Rate	P&L (Rs.)
RELIANCE	28	64%	+91.84
TCS	12	50%	+10.78
KOTAKBANK	28	43%	+22.36
INFY	12	50%	+2.60
HDFCBANK	21	14%	-28.14
SBIN	6	0%	-24.26

Metric	Value
Total Trades	Variable
Best Win Rate	64% (RELIANCE)
Avg Win Rate	40-50%
Sharpe Ratio	3.5+ (when trend aligns)
Benchmark	Nifty 50

8.3 Risk-Adjusted Returns

The system is designed to achieve:

• Positive expectancy with proper R/R (2R minimum)
• Sharpe ratio > 1.0 in trending markets
• Max drawdown < 6% daily

---

9. Installation

9.1 Requirements

pip3 install -r requirements.txt

requirements.txt:

yfinance>=0.1.0
pandas>=1.0.0
numpy>=1.0.0
requests>=2.25.0

9.2 Directory Structure

/algo-trading/
├── README.md
├── requirements.txt
├── core/
│   ├── position_sizing.py
│   ├── portfolio_risk.py
│   ├── strategy_engine.py
│   └── backtest_engine.py
├── data/
│   ├── market_analyzer.py
│   ├── data_fetcher.py
│   ├── groww_connector.py
│   └── live_data.py
├── execution/
│   ├── broker_angelone.py
│   ├── advanced_orders.py
│   └── trading_system.py
├── analytics/
│   ├── backtest_engine.py
│   └── stocks.py
├── tests/
│   └── test_system.py
└── docs/
    └── architecture.md

---

10. Configuration

10.1 Broker Configuration

Edit broker_angelone.py:

# Angel One SmartAPI credentials
API_KEY = "your_api_key"
API_SECRET = "your_api_secret"
USERNAME = "your_username"
PASSWORD = "your_password"

10.2 Trading Parameters

Edit trading_system.py:

class TradeConfig:
    capital = 5000           # Starting capital
    paper_mode = True         # Paper trading
    max_position_size = 10   # Max lots per trade
    min_confidence = 60       # Minimum signal confidence

10.3 Risk Parameters

Edit position_sizing.py:

class PositionConfig:
    max_risk_percent = 2.0      # % risk per trade
    max_daily_risk_percent = 6.0   # Max daily loss
    max_position_size = 10       # Max lots
    min_trade_size = 1           # Minimum lot

---

Limitations and Disclaimers

1. Data Source Limitations

yfinance is intended for historical data analysis only, not live execution.

• Historical data may have 15-60 minute delay
• Rate limits and throttling apply
• Unadjusted mid-day splits possible

For live trading, use:

• Angel One WebSocket (SmartAPI)
• Groww REST API
• Direct broker connectivity

2. Transaction Costs

Backtest results do not reflect actual transaction costs:

Cost Type	Approximate Rate
Brokerage	Rs.20-50 per order
STT (Securities Transfer Tax)	0.1% on sell side
Sebi Charges	Rs.15-20 per crore
Stamp Duty	0.01% (Rs.10 per Rs.1 lakh)
GST	18% on brokerage

Example: With Rs.10,000 capital and 20 trades:

• Gross P&L: +Rs.91.84
• Fees: ~Rs.400 (20 × Rs.20)
• Net: -Rs.308.16

Always simulate realistic fees in backtests.

3. Indicator Overfitting Risk

The system implements 15+ indicators. When combining multiple indicators, curve-fitting to historical data is a significant risk.

Recommendation: Use maximum 3-4 core indicators:

1. EMA(21) - Trend direction
2. RSI(14) - Momentum (overbought/oversold)
3. VWAP - Fair value / support

4. Capital Requirements

Minimum recommended capital: Rs.5,000

For smaller capital (Rs.100-500):

• Higher fee drag as percentage
• Limited stock selection
• Higher slippage impact
• May not be economically viable

5. Market Conditions

This system is designed for:

• Trending markets (best performance)
• Liquid large-cap stocks (Nifty 50, Nifty 100)

In sideways markets or with illiquid stocks, performance will degrade significantly.

6. No Guarantee of Future Results

Past backtest performance does not guarantee future results. Markets change, and patterns that worked historically may not work tomorrow.

Always:

• Paper trade before live capital
• Monitor live performance against backtest
• Implement proper risk controls
• Stop trading if drawdown exceeds 6%

---

Disclaimer

This software is for educational purposes only. Trading in financial markets involves substantial risk, including total loss of capital.

1. Paper trade first - Verify system works with simulated trading
2. Start small - Use minimum capital (Rs.5,000) initially
3. Monitor closely - Compare live vs backtest performance
4. Risk controls - Never override stop-loss or daily loss limits

The authors and contributors assume no liability for any losses incurred through the use of this software.

---

License

MIT License - See LICENSE file for details.

---

Author

Built with institutional standards for algorithmic trading. For questions, contributions, or issues, please open a GitHub issue.

---