Are you an automated trader whose system overtrades and underperforms because it treats Bollinger Bands as simple buy or sell signals? In this episode, we break down why the common intuition—sell the upper band, buy the lower band—is a dangerous trap for Expert Advisors (EAs).

We explore why band touches are actually information regarding market strength rather than reversal signals, explaining how price can "walk the band" during strong trends. Instead of signal chasing, we discuss how professional traders use Bollinger Bands as a volatility measurement framework to classify market states.

Key topics covered:

• The Myth of the Signal: Why Bollinger Bands describe conditions rather than decisions.

• The Squeeze: Understanding volatility compression as a "state flag" to prepare your system for what comes next.

• Volatility Expansion: How to identify when the market is leaving balance and "waking up".

• Regime Detection: Using band width and flatness to distinguish between ranging markets and trending environments.

Stop fighting volatility and learn to use bands for entry permission, trade suppression, and adaptive stop sizing. This episode is for EA traders focused on structure and performance.

In this episode, we expose the hidden psychology behind one of the most common trading pitfalls: overtrading. While placing more trades often feels productive and exciting, data shows it usually results in "slow decay" for your account. We explain why the human brain craves the dopamine hit of trade execution and why action feels better than waiting, even when there is no clear edge. Tune in to learn why boredom and the "fear of missing out" are your worst enemies, and how to recognize when you are trading simply to restore a sense of control rather than to make a profit.

Many traders are misled by "beautiful backtest curves" and high profit factors that completely fail in live trading. In this episode, we break down why the MetaTrader 5 Strategy Tester is often too "forgiving" and how it produces "good-looking lies" when historical data is incomplete or spreads are fixed. Join us as we explore the 1kPips approach to making your testing models as "hostile and realistic as possible". We cover how to avoid the "optimization trap," why 99% modeling quality isn't a guarantee, and why backtesting should be about discovering how you might be wrong, rather than proving you are right.