--- title: "How to Identify Market Regimes and Filter Strategies by Trend and Volatility" description: "A market-regime filter is a rule that only lets a strategy trade when the market is in a favourable state. QuantMonitor's version switches a strategy on only when the 50-period SMA is rising ( SMA(50)..." url: https://quantmonitor.net/how-to-identify-market-regimes-and-filter-strategies-by-trend-and-volatility/ date: 2025-11-02 modified: 2026-08-13 author: "Lukas" image: https://quantmonitor.net/wp-content/uploads/2025/10/portfolio-2.webp categories: ["Nezařazené"] type: post lang: en --- # How to Identify Market Regimes and Filter Strategies by Trend and Volatility A **market-regime filter** is a rule that only lets a strategy trade when the market is in a favourable state. QuantMonitor’s version switches a strategy on only when the 50-period SMA is rising (`SMA(50)[0] – SMA(50)[5] > 0`) *and* current volatility is below average (`ATR(14)/Close` under its own 100-period average) — otherwise the strategy is turned off (`StrategyEnabled = 0`). ## The exact market-regime filter formula ``` IF (SMA(50)[0] – SMA(50)[5]) > 0 AND (ATR(14)[0] / Close[0]) < MA(ATR(14)[0] / Close[0], 100) THEN StrategyEnabled = 1 ELSE StrategyEnabled = 0 ``` ## Recommended values for a market-regime filter | Parameter | Value | Why | | --- | --- | --- | | SMA period | 50 | Defines the trend baseline — a rising 50-period SMA marks an up-trend. | | Trend lookback | 5 bars | Slope window: SMA(50)[0] minus SMA(50)[5] > 0 confirms the trend is rising, not flat. | | ATR period | 14 | Measures current volatility, expressed as ATR% = ATR(14) / Close. | | Volatility baseline | 100-bar MA of ATR% | The regime counts as low volatility only when ATR% is below its own 100-period average. | | StrategyEnabled | 1 / 0 | Master switch: the strategy trades (1) only when both the trend and volatility tests pass; otherwise it stands aside (0). | ## How to add a market-regime filter to a StrategyQuant strategy 1. Open your strategy in StrategyQuant X (AlgoWizard or the strategy editor) and locate the entry conditions. 2. Add an **SMA(50)** indicator referenced at two shifts, and create the trend condition `SMA(50)[0] > SMA(50)[5]` to detect a rising trend. 3. Add an **ATR(14)** indicator and divide it by `Close` to build the relative-volatility value ATR%. 4. Add a 100-bar moving average of that ATR% value to serve as the volatility baseline. 5. Create the volatility condition `ATR% < MA(ATR%, 100)` so it passes only when current volatility is below average. 6. Combine both conditions with **AND** on the entry signal, so trades fire only when the filter equals 1. 7. For reuse, wrap the whole rule in a StrategyQuant X Custom Block / custom indicator in the Code Editor so it returns a single 1/0 flag you can drop into any strategy. 8. Re-run the backtest with and without the filter and compare the equity curves and drawdown before committing it live. New to the platform? See our [free StrategyQuant trial guide](https://quantmonitor.net/strategyquant-trial/) to set up the software first. ## **🔍 Introduction** One of the key factors that determines whether an algorithmic strategy succeeds or stagnates **is not the entry signal itself**, but the **market context** in which it operates. In other words — *when* the system trades is often more important than *how* it trades. In this article, we’ll build a **simple but highly effective market regime filter** based on classic indicators: - **Trend direction** (SMA50) - **Volatility level** (ATR%) The goal is to create a logic that keeps your strategy running only when the market is trending calmly (e.g. *Up_LowVol*) and pauses it during chaotic or bearish phases. --- ## **⚙️ Core Concept: Market Regime Filter** ### **1️⃣ Trend Condition** To determine whether the market is in an uptrend or downtrend, we use the 50-day simple moving average (SMA50) of the underlying index (e.g. NASDAQ100 / USTECHCash). The trend is considered **bullish (Up)** when the SMA50 is rising: SMA(50)[0] – SMA(50)[5] > 0 That means the 50-day average is higher today than it was 5 days ago. --- ### **2️⃣ Volatility Condition** For volatility, we use **ATR(14)** (Average True Range), expressed as a percentage of the current price: ATR%(t) = ATR(14)[t] / Close[t] This shows the average daily range as a percentage of price. For example, ATR% = 0.009 → the market moves ±0.9 % per day on average. We then compare current volatility to its 100-day average: (ATR(14)[0] / Close[0]) < MA(ATR(14)[0] / Close[0], 100) If the current ATR% is below its long-term average, volatility is “low.” --- ### **3️⃣ Combined Logic** The complete filter rule is: IF (SMA(50)[0] – SMA(50)[5]) > 0 AND (ATR(14)[0] / Close[0]) < MA(ATR(14)[0] / Close[0], 100) THEN StrategyEnabled = 1 ELSE StrategyEnabled = 0 ✅ The strategy is **enabled** when the market is trending up and volatility is below average. ❌ Otherwise, it remains **disabled** (no new positions opened). --- ## **📊 Example: NASDAQ (USTECHCash)** We applied this filter to NASDAQ (USTECHCash) daily data, calculating: - **TrendSlope** = SMA(50) − SMA(50)[−5] - **ATR%** = ATR(14)/Close - **ATR% MA(100)** = 100-day moving average of ATR% - **StrategyEnabled** = logic result from the above filter and combined it with the equity curve of a live [trading strategy](https://quantmonitor.net/gc-trading-strategy/). --- ### **🧩 Results by Market Regime** ![How to Identify Market Regimes and Filter Strategies by Trend and Volatility](https://quantmonitor.net/wp-content/uploads/2025/10/Equity-vs-Index-Normalized-separate-scales.webp) | **Market Regime** | **Days** | **Total Profit** | **Filter Active (%)** | **Enabled Days** | | --- | --- | --- | --- | --- | | **Up_LowVol** | 556 | 219 | **95.5 %** | 531 | | **Up_MidVol** | 512 | 143 | 59 % | 303 | | **Up_HighVol** | 270 | 71 | 59 % | 160 | | **Down_MidVol** | 153 | 116 | 0 % | 0 | | **Down_HighVol** | 404 | 59 | 0 % | 0 | | **Down_LowVol** | 35 | 19 | 0 % | 0 | ![How to Identify Market Regimes and Filter Strategies by Trend and Volatility](https://quantmonitor.net/wp-content/uploads/2025/10/USTECHCash-with-color-coded-Market-Regimes-2000x992.webp) ### **🧠 Interpretation** - **Up_LowVol (bullish + calm)**→ Strategy runs ~95 % of the time and generates the largest profit.Ideal “core” regime. - **Up_MidVol / Up_HighVol (bullish + choppy)**→ Strategy trades about 60 % of the time, focusing on quieter sub-periods within volatile uptrends. - **Down_***→ Filter completely disables trading.The system stays out during bear or chaotic conditions. This behavior is exactly what we want: trade only in stable, rising environments and pause during turbulence. ## **💡 Summary** | **Metric** | **Without Filter** | **With Market Filter** | | --- | --- | --- | | Active Trading Days | 100 % | ~55 % | | Drawdown | Higher | Significantly lower | | Stagnation | Frequent | Reduced | | Market Awareness | None | Context-aware | | Best For | Any system | Momentum / Breakout | ## **📘 Conclusion** A **market regime filter** built from simple **SMA(50)** and **ATR%** logic is a powerful and lightweight tool to stabilize performance. It helps your strategy **trade only when conditions are statistically favorable** and stay flat when the environment turns hostile. This approach can be implemented directly in **StrategyQuant X**, as a **custom filter indicator**, or even as an external signal module for MT5 or TradingView. ## **🚀 Real-World Validation** This approach to **market regime analysis** isn’t just theoretical. About a year ago, I applied this exact framework — combining **trend slope and volatility filters** — in my live algorithmic trading project focused on the **U.S. indices portfolio**. By accurately identifying the *Up-LowVol* regime and dynamically scaling exposure only during favorable conditions, the system achieved over **+100 % performance within several months**. ![How to Identify Market Regimes and Filter Strategies by Trend and Volatility](https://quantmonitor.net/wp-content/uploads/2025/10/eq-2000x992.webp) The full verified record is publicly available on FXBlue: 👉 [https://www.fxblue.com/users/lukasmelka1](https://www.fxblue.com/users/lukasmelka1) This result perfectly illustrates how understanding market regimes can turn an average algorithm into a consistent performer — not through curve-fitting, but through correct timing and adaptive exposure management. [FX Blue – free tools and services for FX and CFD traders](//www.fxblue.com) [FX Blue – free tools and services for FX and CFD traders](//www.fxblue.com) ## Market-regime filter FAQ ### What is a market-regime filter? A market-regime filter is a rule that only allows a strategy to trade when the market is in a favourable state, and disables it otherwise. QuantMonitor’s version enables trading only when the 50-period SMA is rising and ATR-based volatility is below its 100-period average. ### How do I add a regime filter to a StrategyQuant strategy? Add SMA(50) and ATR(14) indicators to your strategy in StrategyQuant X, build the trend condition `SMA(50)[0] > SMA(50)[5]` and the volatility condition `ATR% < MA(ATR%, 100)` (where ATR% = ATR(14)/Close), then combine them with AND on your entry signal. Wrapping the rule in a Custom Block lets you reuse the same 1/0 filter across strategies. ### What SMA and ATR values should I use for a regime filter? The QuantMonitor baseline is SMA period 50 with a 5-bar slope lookback for the trend, and ATR period 14 compared against a 100-bar average of ATR% for volatility. Treat these as reasonable starting defaults and re-test on your own instrument and timeframe before changing them. ### Does a regime filter improve backtest results? It can, by keeping a strategy out of the conditions where it historically loses. In the named-regime backtest on this page the filter stays active almost entirely in the calm up-trend (Up_LowVol) and is switched fully off across every down-trend regime, concentrating exposure where the edge lives. See more worked examples in the [free strategy portfolio on our blog](https://quantmonitor.net/blog/). ### Can I reuse the same regime filter across different markets? Yes — because the rule is expressed in relative terms (ATR% divided by price, and a moving average of itself) it is unit-free and transfers across instruments without rescaling. Always re-run the backtest on each new market, since the same regimes appear at different frequencies in equities, futures and FX. ## Related reading - [How to Algorithmically Detect Market Regimes](https://quantmonitor.net/how-to-algorithmically-detect-market-regimes/) — the full methodology for classifying trend and volatility states. - [Trial StrategyQuant: use the algorithmic trading software for free](https://quantmonitor.net/strategyquant-trial/) — set up the platform before you build the filter. - [Free trading strategies (blog portfolio)](https://quantmonitor.net/blog/) — worked examples that apply regime filtering across markets.