Stock Trading System

  The SuperTrend indicator is a popular trend-following technical analysis tool used by traders. It is based on the Average True Range (ATR) and the closing price , making it a dynamic support and resistance level that moves with price. In this blog, we'll explore: What is the SuperTrend indicator? How to calculate it How to use it for trading signals Python code to backtest and visualize it 🔍 What is SuperTrend? The SuperTrend indicator helps determine the current market trend . It uses: ATR : to calculate market volatility Multiplier : to adjust the sensitivity Formula : Upper Band = (High + Low) / 2 + Multiplier * ATR Lower Band = (High + Low) / 2 - Multiplier * ATR When price closes above the SuperTrend → Buy Signal When price closes below the SuperTrend → Sell Signal 🛠️ Python Implementation ✅ Requirements pip install pandas numpy matplotlib yfinance 📜 Python Code import pandas as pd import numpy as np import yfinanc...

  Trading in the stock market can be exciting and challenging! One popular strategy many traders use is the Opening Range Breakout (ORB) . Today, we'll learn what ORB is and see a simple Python example to understand how it works. What is Opening Range Breakout? The Opening Range is the price range (high and low) of a stock during the first few minutes after the market opens—usually the first 30 to 60 minutes . The breakout happens when the price moves above the high or below the low of this opening range. Traders use this breakout to decide when to enter or exit a trade. Why use ORB? It helps catch early momentum in the market. Simple and easy to understand. Works well in volatile markets. How Does ORB Work? Watch the stock price for the first 30–60 minutes after market open. Note the highest price (high) and lowest price (low) in this time. If the price breaks above the high , it’s a signal to buy (go long). If the price breaks below t...

 Looking to improve your stock trading signals? Combining two powerful indicators — MACD (Moving Average Convergence Divergence) and RSI (Relative Strength Index) — can help filter false signals and improve your accuracy. In this blog, you'll learn: What MACD and RSI are How to use them together in a trading strategy Python code to backtest this strategy 🔍 What is MACD? MACD helps detect trend direction and momentum. MACD = EMA 12 − EMA 26 \text{MACD} = \text{EMA}_{12} - \text{EMA}_{26} MACD = EMA 12 ​ − EMA 26 ​ Signal line = 9-period EMA of MACD Buy signal : MACD crosses above signal line Sell signal : MACD crosses below signal line 💡 What is RSI? RSI detects overbought/oversold conditions. RSI = 100 − ( 100 1 + R S ) \text{RSI} = 100 - \left( \frac{100}{1 + RS} \right) RSI = 100 − ( 1 + RS 100 ​ ) Buy signal : RSI < 30 Sell signal : RSI > 70 ⚙️ Strategy Rules (MACD + RSI Combo) Buy when : MACD crosses above the Signal Line ...

  The stock market reacts to news in real-time — but what if you could read the mood of the news automatically and trade before everyone else? Welcome to the world of News Sentiment Analysis using NLP (Natural Language Processing) . In this blog, we’ll explore how to use headlines, AI, and Python to create a headline-based trading strategy . 🤔 Why Use News for Trading? Market-moving news breaks every second — earnings results, government policies, global crises, etc. Traders often react emotionally, but AI can do it smarter. By using NLP models , we can: Detect positive or negative market sentiment Predict possible stock movement Automate buy/sell decisions using logic, not fear 🔍 What is Sentiment Analysis? Sentiment analysis is an NLP technique used to determine the emotional tone behind a piece of text. Example: “TCS reports record profit” → Positive “Infosys faces data breach scandal” → Negative We can score these headlines and align them with...

  Simple Yet Powerful Way to Trade Trends If you're exploring algorithmic trading or want to build your first trading system, the Dual Moving Average (DMA) strategy is an excellent place to start. It's simple, logical, and easy to implement — even with basic Python skills. In this blog, we’ll explain what the strategy is and how you can build and backtest it in Python using historical stock data. 🧠 What Is a Dual Moving Average Strategy? A Dual Moving Average strategy uses two different moving averages: A short-term moving average (fast MA) A long-term moving average (slow MA) ✅ Entry Signal: Buy when the short-term MA crosses above the long-term MA (bullish crossover). ❌ Exit Signal: Sell when the short-term MA crosses below the long-term MA (bearish crossover). 🛠️ What You Need You’ll need the following Python libraries installed: pip install yfinance pandas matplotlib 🧪 Step-by-Step: Coding DMA Strategy in Python import yfinance as y...

  list of 100 trading strategies , grouped by type and each briefly summarized for clarity. This will give you a broad overview of how traders approach markets across different conditions. 📈 Trend-Following Strategies Moving Average Crossover – Buy when short MA crosses above long MA. Dual Moving Average – Uses two MAs to confirm entry/exit. Triple Moving Average – Adds a third MA for stronger signals. MACD Crossover – Momentum-based MA system. SuperTrend Strategy – Follows trend using ATR and price. ADX Trend Strategy – Uses Average Directional Index to confirm trend strength. Turtle Trading – Classic breakout and trend-following method. Donchian Channel Breakout – Enters when price breaks 20-day high/low. Parabolic SAR – Indicator-based trend following system. Heikin Ashi Trend Strategy – Uses smoothed candlesticks to ride trends. 💥 Momentum Strategies RSI with Moving Average – ...