✔ Your resource for automated trading strategy development
✔ Empowering retail traders to create efficient income streams
Why Automated Trading?
Efficiency & Diversification
Simultaneously trade multiple strategies, markets and timeframes, any time of the day.
Determine Strategy Viability
Backtest your strategy to quickly determine its long-term profitability and risk profile.
Keep emotions at bay with the automatic execution of your trading rules.
Use code TACT for 20% off:
The Schaff Trend Cycle is a unique combination of the MACD and Stochastic indicators. Here’s how you can use it to improve your trend following results!
The Kelly criterion is a famous mathematical formula that attempts to maximize your long-term capital growth. In this post, I’ll apply it to a EURUSD breakout strategy and explain some of its potential shortcomings when applied to forex trading.
Knowledge of intermarket correlations can improve your forex trading win rate. Here I explain three important types of correlations, and how you can use them to benefit your trading.
A temporary trading pause can improve your win rate if you’re trend following a volatile market. Here I’ll program a trading pause into a simple breakout strategy, and test its effectiveness on the Widow Maker – the GBPJPY.
Partial profit taking is a dilemma often faced by long-term trend followers. Could this benefit your overall strategy performance?
StrategyQuant offers a support & resistance ranking indicator to help detect upcoming breakouts. Use it to program an automated USDJPY strategy!
Your choice of backtest spread can certainly make or break a strategy. This post will show you how to study the intraday spread variations of your market, and suggest several ways to avoid paying ridiculous spreads.
Risk of ruin is a useful metric to help develop trading strategies that suit your risk appetite. This post explains how to calculate your risk of ruin, and how to use it to improve your trading!
Your strategy’s optimization profile often reveals its robustness, helping you select strategies that will remain profitable in live trading. Here I explain why an optimization profile is important, and how you can easily obtain one using StrategyQuant’s optimizer.
Understanding your backtest report is an essential part of being a successful strategy developer. Here I explain what the numbers mean, and how you can make use of each metric during strategy development.
Traders often use Monte Carlo simulations to estimate worst-case drawdowns, but did you know they can be used for out-of-sample testing too? This post demonstrates the use of StrategyQuant’s Monte Carlo simulator to randomize historical prices and strategy parameters, helping you select robust strategies for live trading.
We all want a large sample of trades in our backtests, but practical limitations such as data availability often get in the way. Here I’ll explain why 30 trades is insufficient, and how you can use standard error to quantify the uncertainty arising from a small sample size.
Are you taking too much risk for too little return? Perhaps the professional hedge funds can help define good trading performance.
Forex day trading seems to have a particular appeal to new traders. Here I highlight five hidden challenges of day trading, and offer some suggestions on how to overcome them.
Every day I come across a trading guru offering educational content on the internet. Many of them speak of huge returns with minimal effort. Should these be trusted? Here’s some tips on how to separate the wheat from the chaff.
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