Indicator
RSI Divergence + EMA Trend FilterDescription:
Divergence is one of the most discussed concepts in technical analysis and one of the most misapplied. The core idea is simple: when price makes a new high but the RSI makes a lower high, momentum is weakening even as price advances. That disconnect between price action and momentum is what divergence measures — and it often precedes reversals before price itself confirms the change in direction.
This strategy formalizes that concept into a rule-based, backtestable system with two components: RSI divergence detection and an EMA trend filter that determines which divergences to act on.
What divergence actually measures
RSI measures the speed and magnitude of price changes. When price reaches a new swing high but RSI fails to reach a correspondingly higher reading, it means the buying pressure behind the new high was weaker than the buying pressure behind the previous high. The market got to a higher price but required less momentum to do it — which suggests the move is losing conviction. Bearish divergence (price higher, RSI lower) signals potential exhaustion in an uptrend. Bullish divergence (price lower, RSI higher) signals potential exhaustion in a downtrend.
Important: divergence is a momentum signal, not a reversal guarantee. Price can continue making new highs with weakening RSI for a significant period before actually reversing. This is why divergence signals work best when combined with a trend filter that identifies the broader market context.
The EMA filter
The 200 EMA defines the dominant trend regime. Bearish divergence signals — where momentum is weakening on the upside — are only acted on when price is below the 200 EMA, meaning the broader trend is already bearish and divergence represents a potential resumption of that trend after a counter-trend bounce. Bullish divergence signals are only acted on when price is above the 200 EMA, where they represent potential continuations of the dominant uptrend after a pullback with improving momentum.
This filter deliberately reduces the total number of signals. Many valid divergences occur against the dominant trend and produce short-lived reversals that reverse again quickly. By requiring trend alignment, the strategy trades fewer setups but acts on the ones with a higher probability of following through.
How divergence is detected
The strategy identifies swing highs and swing lows using a lookback period — the number of bars on each side of a pivot that must be lower (for a high) or higher (for a low) to qualify as a genuine swing point. When two consecutive swing highs show price making a higher high but RSI making a lower high, bearish divergence is flagged. When two consecutive swing lows show price making a lower low but RSI making a higher low, bullish divergence is flagged.
The lookback length is the most important input to tune. A shorter lookback detects more swing points and generates more signals, but many will be minor pivots in the context of noise. A longer lookback requires more significant swing points and generates fewer, higher-quality signals. On daily charts, a lookback of 5 works well. On lower timeframes, 3 to 4 is more appropriate.
Exits
Positions exit at an ATR-based stop-loss and a fixed ATR-based take-profit. The stop is placed beyond the swing point that generated the divergence signal — for a bearish divergence, the stop sits above the swing high; for a bullish divergence, below the swing low. This is intentional: if price breaks through the very level that defined the divergence, the signal is invalidated regardless of what RSI was doing. The take-profit is set at 2x ATR to maintain a positive reward-to-risk ratio across the system.
What to evaluate in backtesting
Look at the signal distribution across different market environments. Divergence strategies tend to perform differently in trending versus ranging markets — in strong trending environments, bearish divergences against the dominant trend will produce many false signals even with the EMA filter. Look at whether the EMA filter is doing real work by temporarily disabling it and comparing signal quality. Check average trade duration — divergence signals that take too long to play out often give back open profit before the take-profit level is reached.
This is not a high-frequency strategy. On daily charts with a 5-bar lookback, signals may appear only a few times per month on a given instrument. That frequency is appropriate — divergence setups require specific conditions to form and should not be forced.
Shared for educational purposes and discussion. This is not investment advice. Backtest on your own instruments and timeframes before drawing conclusions about expected performance. Strategy
ATR Trailing Stop Strategy with EMA Trend FilterMost stop-loss approaches treat risk as a fixed number, a percentage, a dollar amount, a set number of points. The problem with fixed stops is that they ignore the market's actual behavior at any given moment. A 1% stop that makes sense in a low-volatility environment will get hit constantly in a high-volatility one. A wide fixed stop that survives a volatile period is needlessly large when the market quiets down.
ATR-based trailing stops solve this by scaling the stop distance to what the market is actually doing right now. ATR measures average true range, the average distance price moves per bar over a given period, including gaps. When volatility expands, the stop widens to give the trade room to breathe. When volatility contracts, the stop tightens to protect more of the open profit. The stop follows price as it moves in the trade's direction and never moves backward — only trailing further in the profitable direction or holding its level until price reverses through it and the trade closes.
The EMA filter is added for one specific reason: trailing stop systems are naturally reactive rather than predictive, which means without a trend filter they will generate signals in both directions during choppy, range-bound conditions. The 200 EMA acts as a simple regime gate.
Long trades are only considered when price is above the 200 EMA, broadly in an uptrend. Short trades are only considered when price is below it. This doesn't eliminate losing trades, but it meaningfully reduces the number of counter-trend entries that trail stop systems would otherwise generate in oscillating markets.
How the trailing stop works:
On each bar, the strategy calculates a long stop level at close - (ATR × multiplier) and a short stop level at close + (ATR × multiplier). When price is in an uptrend, the long stop ratchets upward with price but never moves down, it holds its highest reached level until price closes below it, at which point the trend flips to bearish and the stop becomes a downward-trailing short stop. The opposite applies in a downtrend. A trend flip from bearish to bullish generates a long entry signal if price is above the 200 EMA. A flip from bullish to bearish generates a short entry signal if price is below the 200 EMA.
Parameters worth adjusting:
The ATR multiplier controls the sensitivity of the trailing stop. A lower multiplier (1.5x or below) produces a tighter stop that flips trend direction more frequently, useful on lower timeframes where you want faster reaction but will generate more signals. A higher multiplier (2.5x or above) produces a wider stop that flips less often, better suited for higher timeframes where you want to stay in a trend longer and can tolerate larger drawdowns on individual trades before exit. The ATR length controls how many bars the average is computed over. Shorter lengths react faster to recent volatility changes; longer lengths smooth out volatility spikes.
The EMA length can be adjusted depending on your timeframe. 200 periods is the standard for daily charts. On a 4-hour chart, 100 to 150 periods covers a similar calendar range. On a 1-hour chart, 50 to 100 periods is reasonable. The goal is for the EMA to represent the dominant trend, not a short-term moving average that whipsaws with every swing.
What this is not:
This strategy does not predict market direction. It reacts to price behavior and exits when price reverses by a defined volatility-adjusted distance. It will produce losing trades, every trailing stop system does, and sequences of losses in choppy conditions are expected behavior, not a flaw. The expectation is that winning trades capture significantly more than they risk because the stop trails and locks in profit, while losing trades are cut at a defined ATR-based distance.
Evaluate this on your own instruments and timeframes with realistic backtest conditions before drawing any conclusions about expected performance.
Shared for educational purposes. This is not investment advice. Always backtest thoroughly and size positions according to your own risk tolerance. Strategy
Bollinger Squeeze Breakout + VolumeA volatility contraction often precedes a volatility expansion. When Bollinger Bands narrow significantly, it signals that the market has entered a period of low energy, and low energy rarely lasts. This strategy is built around that principle: it waits for a genuine squeeze, then enters only when price breaks out of the bands with volume confirming that the move has real participation behind it, not just noise.
The logic
A squeeze is identified when the Bollinger Band width (the distance between the upper and lower bands relative to price) falls below its own recent average,meaning volatility is unusually compressed compared to the recent past. Once that condition is met, the strategy watches for price to close outside either band. A long entry triggers when price closes above the upper band during a squeeze, confirmed by volume exceeding its 20-period average. A short entry triggers under the mirrored condition on the lower band. Stops and targets are based on ATR, since the appropriate distance for both should scale with the market's actual movement at the time of entry, not a fixed number.
This approach tends to filter out the false breakouts that occur during already-volatile, choppy conditions, since the entry only fires after a genuine period of compression, which is when breakouts have historically had more follow-through.
Notes on use
The squeeze threshold and lookback length are the two inputs worth tuning per instrument, a 50-period lookback works reasonably well on daily and 4-hour charts, but lower timeframes may benefit from a shorter lookback to react faster to genuine volatility shifts. As with any breakout strategy, backtest across both trending and range-bound periods before drawing conclusions, since this approach is built specifically to perform during regime transitions and may underperform in markets that stay range-bound for extended periods without ever truly compressing.
This is shared for educational and discussion purposes. As always, backtest thoroughly on your own instruments and timeframes, and treat this as a starting framework rather than a finished system. Feedback and variations are welcome in the comments. Strategy
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Market Breadth Trend StrategyOverview
Many traders focus on major indexes such as the S&P 500 or Nasdaq when evaluating market conditions. While indexes show overall price movement, they do not always reflect how broadly that movement is supported across the market.
Market breadth is a way of studying participation. It can help traders understand whether strength or weakness is concentrated in a small group of stocks or spread across a wider portion of the market.
A market move supported by broad participation may provide different context than a move driven by only a few heavily weighted stocks.
Understanding Market Participation
Market breadth generally refers to the number of securities contributing to a market move.
Examples of breadth-related observations include:
The balance between advancing and declining stocks
The number of stocks reaching new highs or lows
The percentage of stocks trading above key moving averages
These measurements can provide additional perspective alongside price action and trend analysis.
Why Traders Monitor Breadth
Participation Matters
Strong participation may indicate that market activity is occurring across a wider group of stocks rather than being concentrated in a few names.
Additional Context
Breadth can be used as a supplementary tool when evaluating trends, momentum, and overall market conditions.
Market Observation
Some traders monitor breadth metrics to better understand changes in participation over time and how those changes compare with index performance.
Strategy Concept
This script uses a simplified breadth-style proxy derived from the chart's relationship to a long-term moving average.
It is important to note that this script does not use actual exchange-wide market breadth data. Instead, it creates a participation-style filter using price behavior on the current chart.
The strategy combines:
Trend identification using moving averages
A breadth-style participation filter
ATR-based risk management
The objective is to demonstrate how participation concepts can be incorporated into a trend-following framework for research and testing purposes.
Important Notes
This script uses a simplified participation-style filter and is not a substitute for exchange-wide breadth indicators.
Results will vary across symbols, timeframes, and market conditions.
The script is intended for educational, research, and testing purposes.
Disclaimer
This script is provided for educational and research purposes only. It demonstrates one way to combine trend analysis with a breadth-style participation filter. It is not financial advice and should be tested across different symbols, market conditions, and timeframes before being used in any trading workflow.
This version avoids performance claims, avoids implying predictive ability, and clearly explains the limitations of the breadth proxy. Strategy
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