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Educational Resources
Master Algorithmic Trading

Algo Trading Guide

Learn the fundamentals of algorithmic trading, from strategy development to execution and risk management.

System Design

Algo Trading Architecture

Understanding how algorithmic trading systems process market data and execute trades

Trading System Flow

Market DataReal-time TicksHistorical Data5+ YearsNews & SentimentAI AnalysisAI Processing EngineSignal GeneratorPattern DetectionRisk AssessmentStrategy Engine50+ Pre-built AlgorithmsRisk ManagerReal-time MonitoringOrder Execution (0.02s) → Broker APIs20+ Indian Brokers Connected

Key Components Explained

Data Input Layer

Real-time market feeds, historical data, and sentiment analysis from multiple sources

AI Processing Engine

Machine learning models for pattern recognition and signal generation

Strategy Engine

Executes trading logic with 50+ pre-built strategies and custom rule support

Risk Management

Position sizing, stop-loss controls, and real-time portfolio monitoring

System Design

Algo Trading System Architecture

How automated trading systems process and execute trades

DATA INPUT LAYER

Market Data

Real-time quotes

< 1ms

Historical Data

Tick-by-tick

10+ Years

News Feed

Event triggers

50+

Sentiment

Social signals

85%
Processing Pipeline

PROCESSING CORE

Strategy Engine

Executes trading logic

  • 50+ Indicators
  • Custom Rules
  • Multi-timeframe

Risk Manager

Real-time risk monitoring

  • Position Sizing
  • Stop Loss
  • Max Drawdown

Order Router

Smart order execution

  • Best Price
  • Slippage Control
  • Multi-broker
Execution Output

EXECUTION & OUTPUT

Trade Execution

Order to fill

< 50ms

Risk Reports

P&L tracking

Real-time

Audit Logs

Compliance ready

100%

Analytics

Performance

50+ Metrics
Core Concepts

Algo Trading Fundamentals

Understanding the building blocks of algorithmic trading

What is Algo Trading?
Algorithmic trading uses computer programs to execute trades based on predefined rules and conditions. It removes emotional decision-making, enables faster execution, and can process multiple markets simultaneously.
Strategy Logic
A trading strategy consists of entry conditions (when to buy/sell), exit conditions (profit targets, stop losses), position sizing rules, and risk management parameters. Logic can be based on technical indicators, price patterns, or statistical models.
Backtesting
Backtesting involves testing a strategy against historical data to evaluate its performance. Key metrics include win rate, profit factor, maximum drawdown, Sharpe ratio, and consistency across different market conditions.
Paper Trading
Paper trading (simulation) allows you to test strategies in real-time market conditions without risking real money. Essential step before live deployment to validate strategy behavior in current market conditions.
Execution Speed
Speed matters in algo trading. Factors include API latency, broker server location, network speed, and code optimization. Trade Metrix Technologies uses co-located servers for minimal execution delays.
Broker API Integration
APIs allow programmatic access to broker platforms for placing orders, fetching data, and managing positions. Each broker has unique API specifications. Trade Metrix Technologies handles all broker integrations seamlessly.

Types of Trading Strategies

Common algorithmic trading approaches and when to use them

Risk: Medium
Trend Following
Identifies and follows market trends using moving averages, MACD, or ADX. Buys in uptrends, sells in downtrends. Works best in trending markets but may suffer in sideways conditions.

Examples:

Moving Average CrossoverBreakout TradingMomentum Strategies
Risk: Medium-High
Mean Reversion
Assumes prices revert to their historical average. Buys oversold conditions, sells overbought. Uses indicators like RSI, Bollinger Bands. Works in range-bound markets.

Examples:

RSI Oversold/OverboughtBollinger Band BounceStatistical Arbitrage
Risk: High
Breakout Strategies
Enters trades when price breaks key support/resistance levels. Captures large moves at the start of new trends. Requires good volatility filters to avoid false breakouts.

Examples:

Range BreakoutOpening Range BreakoutVolatility Breakout
Risk: Variable
Options Strategies
Uses options for hedging, income generation, or directional bets. Includes spreads, straddles, and iron condors. Requires understanding of Greeks (Delta, Theta, Vega, Gamma).

Examples:

Iron CondorBull Call SpreadStraddle/Strangle

Risk Management in Algo Trading

Essential risk management principles for algorithmic traders

Position Sizing
Never risk more than 1-2% of your capital on a single trade. Calculate position size based on stop loss distance and risk tolerance. Proper sizing prevents catastrophic losses.
Drawdown Management
Maximum drawdown is the largest peak-to-trough decline. Set daily loss limits (e.g., 3-5%) and weekly limits. Stop trading if limits are breached to prevent emotional decisions.
Risk-Reward Ratio
Aim for minimum 1:2 risk-reward ratio. If risking ₹1000 per trade, target should be at least ₹2000. Higher ratios allow profitability even with lower win rates.
Diversification
Don't put all capital in one strategy or instrument. Diversify across strategies, timeframes, and instruments to reduce correlation and overall portfolio risk.
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