Python Finance Hub

  Introduction Most traders lose money because they guess. Pro traders test. Before you risk a single Dollar on Bitcoin, wouldn't you want to know how your strategy performed during the 2022 crash or the 2024 rally? That is the power of backtesting —simulating a trading strategy on historical data to see if it actually holds water. In this tutorial, we are going to build a backtesting engine from scratch. We will test one of the most famous indicators in crypto: the SMA Crossover (Golden Cross) . Note: In our previous Data Visualization Guide , we learned how to fetch Bitcoin data and plot price charts. Now, let's use that data to find profitable signals. Prerequisites To follow along, you’ll need Python installed and the following libraries. If you haven't installed them yet, run this in your terminal: pip install pandas numpy yfinance matplotlib The Strategy: The Golden Cross For this backtest, we will use a classic trend-following strategy: The Indicators: We use two Si...

Do you really need to be a professional coder to automate your trades? For years, the barrier to entry for algorithmic trading was incredibly high. You needed a computer science degree, expensive server infrastructure, and direct market access. Today, that wall has crumbled. Now, retail traders are faced with a different dilemma: The Choice of Tool. In the red corner, we have TradingView , the undisputed king of charting with its specialized language, Pine Script. In the blue corner, we have Python , the heavyweight champion of data science and backend development. We've already shown you how to build a Python bot from scratch , but is it always the best choice? Sometimes, the best tool isn't the most powerful one—it’s the one that fits the job. Let’s break down the "Pine Script vs Python" debate to help you decide which route automates your alpha best. Round 1: Ease of Use & Speed to Deploy Winner: TradingView (Pine Script) If your goal is to go from a trading i...

Introduction Numbers on a screen are boring. To trade successfully, you need to see the market trends. If you are staring at a raw spreadsheet of Bitcoin prices, you are missing the story the market is trying to tell you. Volatility is the heartbeat of the market. It tells us how "nervous" or "confident" traders are. By visualizing this volatility, you aren't just looking at price—you are looking at risk. This is the first step in moving from a gambler to a quantitative trader. If you've already built your DCA Bot using our previous guide , now it's time to learn how to analyze the market data behind it. Today, we are going to build a tool that lets you see the chaos of the crypto markets clearly. Prerequisites To follow along, you need a Python environment installed. We will be using three essential libraries for python financial analysis : Pandas: The Excel of Python. It handles our data. Matplotlib: The artist. It draws our charts. yfinance: The da...

If you’ve ever stared at a chart at 3 AM, sweating over whether to buy now or wait for a "dip," you know that emotional trading is a quick way to lose money. The market is volatile, but your strategy shouldn't be. Enter Dollar Cost Averaging (DCA) . It is the antidote to volatility. Instead of trying to time the market (which even pros struggle with), you buy a fixed dollar amount of an asset at regular intervals—regardless of the price. This smooths out your average entry price over time and, most importantly, removes emotion from the equation. In this tutorial, I’m going to show you exactly how to automate Bitcoin trading using a simple Python crypto bot . We will build a "set it and forget it" system that buys crypto for you, rain or shine. 🛠️ Prerequisites Before we write a single line of code, you need a few tools in your belt. Python Installed: Make sure you have Python (version 3.7 or higher) installed. The ccxt Library: This is the industry standard...