You are currently accessing the institutional-grade blueprint for <b>Introduction to Algorithmic Trading 1: Backtesting – Tradimo</b>. Instant digital deployment and lifetime encrypted access are guaranteed immediately upon transaction clearance.
Backtesting is the process of feeding historical data to an automated trading strategy and see how it would have performed. We will study various common backtest performance metrics. Backtest performance can easily be made unrealistic and un-predictive of future returns due to a long list of pitfalls, …
Purchase Introduction to Algorithmic Trading 1: Backtesting – Tradimo courses at here with PRICE $500 $77
Backtesting is the process of feeding historical data to an automated trading strategy and see how it would have performed. We will study various common backtest performance metrics. Backtest performance can easily be made unrealistic and un-predictive of future returns due to a long list of pitfalls, which will be examined in this course. The choice of a software platform for backtesting is also important, and criteria for this choice will be discussed. Illustrative examples are drawn from a futures strategy and a stock portfolio trading strategy.
Upon completion, students will be able to backtest their own strategies, and use that to decide whether to trade them live.
This is a pre-recorded workshop conducted by Ernest Chan (www.epchan.com). Free MATLAB trial licenses will be arranged for extensive in-class exercises. No prior knowledge of MATLAB is assumed, but some programming experience is necessary. The math requirement assumed is basic college-level statistics. Q&A via emails is included.
Total hours: 7 hours of recorded session.
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Purchase Introduction to Algorithmic Trading 1: Backtesting – Tradimo courses at here with PRICE $500 $77
Trading foreign exchange, cryptocurrencies, and algorithmic assets on margin carries a high level of risk and may not be suitable for all investors. Past performance of any trading system or quantitative blueprint does not guarantee future results.<br><br><b>Asset Classification:</b>

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