Kalman Filter, Simple Moving Average Strategy

Discover a mean reversion trading strategy using the Kalman Filter and a 5-day Simple Moving Average. Learn entry & exit rules for indicator crosses.

Published · Updated · Methodology: Technical Indicators

Part of: Moving Average Strategies

  • Methodology: Technical Indicators
  • Content type: strategy
  • Timeframes: Not specified
  • Markets: Not specified

Indicators used

  • Kalman Filter
  • SMA

Source video

Decoded from: Kalman Filter Strategy (Backtest) by Quantified Strategies — watch the original

Key timestamps:

  • 0:00 - Introduction to Kalman Filter strategy
  • 1:10 - What is the Kalman Filter?
  • 2:30 - Kalman Filter calculation analogy
  • 3:20 - Strategy rules explained
  • 3:50 - Performance comparison

Strategy overview

A moving average answers one question — what is price's recent average — and a Kalman filter answers a related but different one: given noisy observations, what is the best current estimate of the underlying value, and how much should the newest data point move that estimate. The filter comes from control engineering and navigation rather than from charting, and it is recursive: instead of a fixed lookback window that treats every bar inside it the same way and drops the oldest one each period, it carries an estimate forward and corrects it as new information arrives, weighting that correction by how much uncertainty it assigns to the measurement. Pairing it with a simple moving average puts a familiar static smoother next to an adaptive one.

This entry was decoded from "Kalman Filter Strategy (Backtest)" by Quantified Strategies, a channel whose orientation is quantitative testing rather than chart tuition — and the running order shows it. More than half the video is spent on what the filter is and on an analogy for how the calculation works before any trading logic appears; the rules chapter arrives at 3:20, with the closing chapter given to a performance comparison. The weight of the video sits on understanding the estimator first, with the moving average in the role most viewers already know it in — the familiar reference against which something unfamiliar is measured.

A few things to note honestly. Despite "(Backtest)" in the title, no instrument, market, period or dataset is stated in what was catalogued here, and no timeframe is recorded; the performance comparison is the video's own closing segment, and no figures from it are reproduced on this page. No mechanical rule set was decoded for this entry, so what follows here is the concept and the source's framing rather than an extracted set of conditions — the video itself remains the place to see how its author states the rules.

Topics

kalman filter strategy · simple moving average strategy · mean reversion strategy · technical indicators · trading strategy · tradingview strategy · pine script strategy · sma strategy · algorithmic trading

Frequently asked questions

What is a Kalman filter in trading?

It is a recursive estimator borrowed from engineering that treats observed price as a noisy measurement of an underlying value. It carries an estimate forward bar by bar and corrects it as new data arrives, weighting each correction by the uncertainty it assigns to the observation — which is why traders use it as an adaptive smoother.

How is a Kalman filter different from a moving average?

A moving average is defined by a fixed lookback: it averages a set number of past bars and drops the oldest each period. A Kalman filter has no window in that sense — it updates a running estimate recursively and adjusts how strongly it reacts to new data based on estimated noise, so its responsiveness is not pinned to a single length setting.

Does this video show backtest results?

Its final chapter is a performance comparison, but the source as catalogued here does not state the instrument, timeframe, period or dataset behind it, and no figures are reproduced on this page. Treat the comparison as the video's own presentation and verify any claim on your own data.

How should I evaluate an adaptive filter before trading it?

Test it against the simplest alternative it is meant to improve on — usually a plain moving average — on the same instrument, timeframe and history, and check whether the smoother behavior survives out of sample rather than only looking better on the chart. Strategy Decoder catalogs strategies like this one from video sources; no mechanical rule set was decoded for this entry, so the source video remains the reference for exact conditions.

About this strategy page

This trading strategy was decoded by Strategy Decoder's AI from a public YouTube trading video and turned into a structured, reviewable specification. In the interactive app this page shows the full entry and exit logic, risk management settings, the indicators involved with their parameters, AlgoWizard-compatible logic and a Pine Script export ready for TradingView backtesting — plus an automated backtest verdict when one has been computed for this strategy.

Strategy Decoder catalogs 2,229 decoded strategies. Each one is extracted with confidence scoring, cross-linked to the indicators it uses, and kept up to date as new videos are processed daily. Load this page with JavaScript enabled to use the interactive tools, or start from the strategy explorer to filter by methodology, market and timeframe.

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