Power Trend with Tight Price ActionI've combined Mike Webster's Power Trend with a Tight Price Action detector. This script:
✅ Identifies Power Trends (green background when active)
✅ Marks Tight Price Action (blue bars when range is tight + volume dries up)
✅ Plots Buy Points on Breakouts (green triangles for breakouts on high volume)
Pine Script: Power Trend + Tight Price Action
pinescript
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//@version=5
indicator("Power Trend with Tight Price Action", overlay=true)
// Moving Averages
ema21 = ta.ema(close, 21)
sma50 = ta.sma(close, 50)
// === Power Trend Conditions ===
// 1. Low above 21 EMA for 10 days
low_above_ema21 = ta.lowest(low, 10) >= ta.lowest(ema21, 10)
// 2. 21 EMA above 50 SMA for 5 days
ema21_above_sma50 = ta.barssince(ema21 < sma50) >= 5
// 3. 50 SMA is in an uptrend
sma50_uptrend = sma50 > sma50
// 4. Market closes higher than the previous day
close_up = close > close
// Start & End Power Trend
startPowerTrend = low_above_ema21 and ema21_above_sma50 and sma50_uptrend and close_up
endPowerTrend = close < ema21 or sma50 < sma50
// Track Power Trend Status
var bool inPowerTrend = false
if (startPowerTrend)
inPowerTrend := true
if (endPowerTrend)
inPowerTrend := false
// === Tight Price Action (Low Volatility + Volume Dry-Up) ===
range10 = ta.highest(high, 10) - ta.lowest(low, 10)
tightBase = (range10 / close) < 0.07 // Price range is less than 7% of closing price
volAvg50 = ta.sma(volume, 50)
lowVolume = volume < (volAvg50 * 0.75) // Volume is 25% below 50-day avg
tightPriceAction = inPowerTrend and tightBase and lowVolume // Tight price inside Power Trend
// === Breakout on High Volume ===
breakoutHigh = ta.highest(high, 10)
breakout = close > breakoutHigh and volume > (volAvg50 * 1.5) // Price breaks out with 50%+ volume surge
// === Plot Features ===
// Background for Power Trend
bgcolor(inPowerTrend ? color.green : na, transp=85)
// Highlight Tight Price Action
barcolor(tightPriceAction ? color.blue : na)
// Mark Breakouts
plotshape(series=breakout, location=location.belowbar, color=color.green, style=shape.triangleup, title="Buy Point on Breakout")
// Power Trend Start/End Markers
plotshape(series=startPowerTrend, location=location.belowbar, color=color.green, style=shape.labelup, title="Power Trend Start")
plotshape(series=endPowerTrend, location=location.abovebar, color=color.red, style=shape.triangledown, title="Power Trend End")
// Alerts
alertcondition(startPowerTrend, title="Power Trend Started", message="Power Trend has started!")
alertcondition(endPowerTrend, title="Power Trend Ended", message="Power Trend has ended!")
alertcondition(breakout, title="Breakout on High Volume", message="Stock has broken out on high volume!")
How This Works:
🔹 Green Background = Power Trend is active
🔹 Blue Bars = Tight Price Action inside Power Trend (low volatility + volume dry-up)
🔹 Green Triangles = Breakout on High Volume
Volatilität
[GOG] Banana Split MABanana Split MA uses the MA100, EMA200 and MA300 to predict incoming high volatility moves. Look for opportunities when the three lines start converging and bending a certain direction to form a banana shape to enter a directional trade. The area between the lines fill with an opaque color when the three lines start to get close. Popularized by Krillin @lsdinmycoffee
Squeeze Volatility Scanner con ADXThis indicator works with the Bollinger bands and Kepler in addition on the ADX. The red band indicates the start of the Squeeze.
G-FRAMA | QuantEdgeBIntroducing G-FRAMA by QuantEdgeB
Overview
The Gaussian FRAMA (G-FRAMA) is an adaptive trend-following indicator that leverages the power of Fractal Adaptive Moving Averages (FRAMA), enhanced with a Gaussian filter for noise reduction and an ATR-based dynamic band for trade signal confirmation. This combination results in a highly responsive moving average that adapts to market volatility while filtering out insignificant price movements.
_____
1. Key Features
- 📈 Gaussian Smoothing – Utilizes a Gaussian filter to refine price input, reducing short-term noise while maintaining responsiveness.
- 📊 Fractal Adaptive Moving Average (FRAMA) – A self-adjusting moving average that adapts its sensitivity to market trends.
- 📉 ATR-Based Volatility Bands – Dynamic upper and lower bands based on the Average True Range (ATR), improving signal reliability.
- ⚡ Adaptive Trend Signals – Automatically detects shifts in market structure by evaluating price in relation to FRAMA and its ATR bands.
_____
2. How It Works
- Gaussian Filtering
The Gaussian function preprocesses the price data, giving more weight to recent values and smoothing fluctuations. This reduces whipsaws and allows the FRAMA calculation to focus on meaningful trend developments.
- Fractal Adaptive Moving Average (FRAMA)
Unlike traditional moving averages, FRAMA uses fractal dimension calculations to adjust its smoothing factor dynamically. In trending markets, it reacts faster, while in sideways conditions, it reduces sensitivity, filtering out noise.
- ATR-Based Volatility Bands
ATR is applied to determine upper and lower thresholds around FRAMA:
- 🔹 Long Condition: Price closes above FRAMA + ATR*Multiplier
- 🔻 Short Condition: Price closes below FRAMA - ATR
This setup ensures entries are volatility-adjusted, preventing premature exits or false signals in choppy conditions.
_____
3. Use Cases
✔ Adaptive Trend Trading – Automatically adjusts to different market conditions, making it ideal for both short-term and long-term traders.
✔ Noise-Filtered Entries – Gaussian smoothing prevents false breakouts, allowing for cleaner entries.
✔ Breakout & Volatility Strategies – The ATR bands confirm valid price movements, reducing false signals.
✔ Smooth but Aggressive Shorts – While the indicator is smooth in overall trend detection, it reacts aggressively to downside moves, making it well-suited for traders focusing on short opportunities.
_____
4. Customization Options
- Gaussian Filter Settings – Adjust length & sigma to fine-tune the smoothness of the input price. (Default: Gaussian length = 4, Gaussian sigma = 2.0, Gaussian source = close)
- FRAMA Length & Limits – Modify how quickly FRAMA reacts to price changes.(Default: Base FRAMA = 20, Upper FRAMA Limit = 8, Lower FRAMA Limit = 40)
- ATR Multiplier – Control how wide the volatility bands are for long/short entries.(Default: ATR Length = 14, ATR Multiplier = 1.9)
- Color Themes – Multiple visual styles to match different trading environments.
_____
Conclusion
The G-FRAMA is an intelligent trend-following tool that combines the adaptability of FRAMA with the precision of Gaussian filtering and volatility-based confirmation. It is versatile across different timeframes and asset classes, offering traders an edge in trend detection and trade execution.
____
🔹 Disclaimer: Past performance is not indicative of future results. No trading strategy can guarantee success in financial markets.
🔹 Strategic Advice: Always backtest, optimize, and align parameters with your trading objectives and risk tolerance before live trading.
EMA + Supertrend + ATR2 EMA bands + SUPERTREND + SCALED ATR
This combined allow to not have to much indicator at one but only one.
The ATR shows grey '°' when the value is above 50 on a scale.
Advanced Volatility Scanner by YaseenMedium-Term Trend Indicator
The Medium-Term Trend Indicator is designed to help traders analyze market trends using moving averages, momentum indicators, and volume-based confirmations. It provides a systematic approach to identifying potential trade opportunities based on well-established technical analysis principles.
Features:
Uses 50-period and 200-period Exponential Moving Averages (EMA) for trend identification
Incorporates the Moving Average Convergence Divergence (MACD) indicator for momentum confirmation
Includes the Relative Strength Index (RSI) to filter overbought and oversold conditions
Utilizes Average True Range (ATR) for dynamic stop-loss and take-profit levels
Applies a volume-based filter to ensure trades align with significant market activity
Implements the Average Directional Index (ADX) to confirm trend strength
How It Works:
The script evaluates price movements in relation to key moving averages while confirming trends with RSI, MACD, and ADX. It identifies conditions where strong trend momentum aligns with volume activity, helping traders assess market direction effectively.
Disclaimer:
This script is intended for educational purposes only and does not constitute financial advice. Users should conduct their own research and risk management before making trading decisions.
Wyckoff Method with OBVこのコードを使った戦略は、WyckoffメソッドとOBV（On-Balance Volume）を活用したトレーディング戦略です。以下に、戦略の概要とその実行方法を説明します。
戦略の概要
OBVの分析:
OBVは、価格の動きとボリュームの関係を示す指標です。価格が上昇するときにボリュームが増加している場合、強い上昇トレンドが示唆されます。逆に、価格が下落するときにボリュームが増加している場合、強い下降トレンドが示唆されます。
高ボリュームと低ボリュームのシグナル:
ボリュームが平均の1.5倍を超えると高ボリュームシグナル（赤の三角形）が表示されます。この場合、トレンドの強化が示唆されます。
ボリュームが平均の0.5倍未満の場合、低ボリュームシグナル（緑の三角形）が表示されます。この場合、トレンドの減速や反転が示唆されることがあります。
OBVシグナルの背景色:
OBVの変化に基づいて、背景色が緑または赤に変わります。緑は上昇トレンド、赤は下降トレンドを示します。
戦略の実行方法
エントリーシグナル:
買いエントリー:
OBVが前日よりも増加しており（obvSignal == 1）、かつ高ボリュームシグナルが表示されている時に買いエントリーを検討します。
売りエントリー:
OBVが前日よりも減少しており（obvSignal == -1）、かつ低ボリュームシグナルが表示されている時に売りエントリーを検討します。
ストップロスとテイクプロフィット:
ストップロスを直近のサポートまたはレジスタンスレベルに設定し、利益目標はリスクリワード比を考慮して設定します。
トレンドの確認:
エントリーを行う前に、トレンドの確認を行うために他のテクニカル指標（例えば、移動平均やRSIなど）を併用することも推奨します。
注意点
この戦略は過去のデータに基づいており、将来のパフォーマンスを保証するものではありません。必ずバックテストを行い、自分のリスク許容度に合った設定を見つけることが重要です。
市場の状況によっては、ボリュームシグナルが誤ったシグナルを出す場合もあるため、他の指標やファンダメンタル分析と併用することをお勧めします。
Smart MA Crossover BacktesterSmart MA Crossover Backtester - Strategy Overview
Strategy Name: Smart MA Crossover Backtester
Published on: TradingView
Applicable Markets: Works well on crypto (tested profitably on ETH)
Strategy Concept
The Smart MA Crossover Backtester is an improved Moving Average (MA) crossover strategy that incorporates a trend filter and an ATR-based stop loss & take profit mechanism for better risk management. It aims to capture trends efficiently while reducing false signals by only trading in the direction of the long-term trend.
Core Components & Logic
Moving Averages (MA) for Entry Signals
Fast Moving Average (9-period SMA)
Slow Moving Average (21-period SMA)
A trade signal is generated when the fast MA crosses the slow MA.
Trend Filter (200-period SMA)
Only enters long positions if price is above the 200-period SMA (bullish trend).
Only enters short positions if price is below the 200-period SMA (bearish trend).
This helps in avoiding counter-trend trades, reducing whipsaws.
ATR-Based Stop Loss & Take Profit
Uses the Average True Range (ATR) with a multiplier of 2 to calculate stop loss.
Risk-Reward Ratio = 1:2 (Take profit is set at 2x ATR).
This ensures dynamic stop loss and take profit levels based on market volatility.
Trading Rules
✅ Long Entry (Buy Signal):
Fast MA (9) crosses above Slow MA (21)
Price is above the 200 MA (bullish trend filter active)
Stop Loss: Below entry price by 2× ATR
Take Profit: Above entry price by 4× ATR
✅ Short Entry (Sell Signal):
Fast MA (9) crosses below Slow MA (21)
Price is below the 200 MA (bearish trend filter active)
Stop Loss: Above entry price by 2× ATR
Take Profit: Below entry price by 4× ATR
Why This Strategy Works Well for Crypto (ETH)?
🔹 Crypto markets are highly volatile – ATR-based stop loss adapts dynamically to market conditions.
🔹 Long-term trend filter (200 MA) ensures trading in the dominant direction, reducing false signals.
🔹 Risk-reward ratio of 1:2 allows for profitable trades even with a lower win rate.
This strategy has been tested on Ethereum (ETH) and has shown profitable performance, making it a strong choice for crypto traders looking for trend-following setups with solid risk management. 🚀
Low Relative Volume Dry-Up (DUV)TradingView Pine Script – Low Relative Volume Dry-Up (DUV) Indicator
pinescript
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//@version=5 indicator("Low Relative Volume Dry-Up (DUV)", overlay=true) // Parameters length = input(50, title="Volume MA Length") // Lookback period for volume average threshold = input(0.3, title="Dry-Up Factor") // Volume must be below X% of average to qualify // Compute moving average of volume vol_ma = ta.sma(volume, length) // Calculate relative volume rel_vol = volume / vol_ma // Identify Dry-Up (DUV) bars dry_up = rel_vol < threshold // Highlight bars with color barcolor(dry_up ? color.blue : na) // Add DUV markers above bars plotshape(dry_up, location=location.abovebar, style=shape.labelup, color=color.blue, size=size.small, title="DUV", text="DUV") // Plot Volume Moving Average for reference plot(vol_ma, color=color.gray, title="Volume MA") // Alert when DUV occurs alertcondition(dry_up, title="DUV Alert", message="Dry-Up Volume Detected!")
The best Dry-Up Factor (DUF) for spotting breakouts depends on the stock’s liquidity and volatility. However, based on breakout trading principles, here are some guidelines:
Optimal Dry-Up Factor (DUF) Ranges for Breakouts
✅ 0.2 - 0.3 (20% - 30% of average volume)
Ideal for highly liquid stocks (e.g., large caps, blue chips).
Good for spotting extreme dry-up zones before volume expansion.
Best used in strong uptrends or consolidations before breakouts.
✅ 0.3 - 0.5 (30% - 50% of average volume)
Works well for mid-cap stocks with moderate volatility.
Captures normal low-volume consolidations before breakouts.
Balances false signals and breakout precision.
✅ 0.5 - 0.7 (50% - 70% of average volume)
Better for small caps and volatile stocks where volume fluctuations are common.
Detects early signs of accumulation before volume expansion.
Reduces the risk of false dry-up signals in choppy markets.
How to Find the Best DUF for Your Stocks
1️⃣ Check historical breakout cases – What was the relative volume before the move?
2️⃣ Test different thresholds – Start with 0.3 - 0.5 and adjust based on stock behavior.
3️⃣ Combine with price action – Look for tight consolidations, inside bars, or squeeze setups.
4️⃣ Use ATR or ADX – Validate if the stock is in a low-volatility phase before expanding.
Bollinger Band Signals with POCThe script will give you perfect buy and sell signals.
Red dot for buy signal. !But only when the trend is not downward.
Green dot for sell signal.
VolatilityThis is a filtering indicator Volatility in the CTA contract of BG Exchange. According to their introduction, it should be calculated using this simple method.
However, you may have seen the problem. According to the exchange's introduction, the threshold should still be divided by 100, which is in percentage form. The result I calculated, even if not divided by 100, still shows a significant difference, which may be due to the exchange's mistake. Smart netizens, do you know how the volatility of BG Exchange is calculated.
The official introduction of BG Exchange is as follows: Volatility (K, Fluctuation) is an additional indicator used to filter out positions triggered by CTA strategy signals in low volatility markets. Usage: Select the fluctuation range composed of the nearest K candlesticks, and choose the highest and lowest closing prices. Calculation: 100 * (highest closing price - lowest closing price) divided by the lowest closing price to obtain the recent amplitude. When the recent amplitude is greater than Fluctuation, it is considered that the current market volatility meets the requirements. When the CTA strategy's position building signal is triggered, position building can be executed. Otherwise, warehouse building cannot be executed.
ai//@version=5
indicator("Scalping Master", overlay=true, shorttitle="SCALP_PRO")
// Input Parameters
input fastLength = 9
input slowLength = 21
input rsiLength = 14
input rsiOverbought = 70
input rsiOversold = 30
input volumeThreshold = 1.5
// Calculate Indicators
fastMA = ta.sma(close, fastLength)
slowMA = ta.sma(close, slowLength)
rsi = ta.rsi(close, rsiLength)
= ta.macd(close, 12, 26, 9)
volumeAvg = ta.sma(volume, 20)
volumeSpike = volume > volumeAvg * volumeThreshold
// Trend Direction Conditions
bullishTrend = fastMA > slowMA and fastMA > fastMA
bearishTrend = fastMA < slowMA and fastMA < fastMA
// Entry Signals
longCondition =
ta.crossover(fastMA, slowMA) and
rsi < rsiOversold and
macdLine > signalLine and
volumeSpike
shortCondition =
ta.crossunder(fastMA, slowMA) and
rsi > rsiOverbought and
macdLine < signalLine and
volumeSpike
// Exit Signals
exitLong = ta.crossunder(fastMA, slowMA) or rsi >= rsiOverbought
exitShort = ta.crossover(fastMA, slowMA) or rsi <= rsiOversold
// Visual Alerts
plotshape(series=longCondition, style=shape.triangleup, color=color.new(color.green, 0), location=location.belowbar, size=size.small)
plotshape(series=shortCondition, style=shape.triangledown, color=color.new(color.red, 0), location=location.abovebar, size=size.small)
// Plot Moving Averages
plot(fastMA, color=color.blue, linewidth=2)
plot(slowMA, color=color.orange, linewidth=2)
// Alert Conditions
alertcondition(longCondition, title="Long Entry", message="Bullish Entry Signal")
alertcondition(shortCondition, title="Short Entry", message="Bearish Entry Signal")
// Strategy Configuration (for backtesting)
if (longCondition)
strategy.entry("Long", strategy.long)
if (shortCondition)
strategy.entry("Short", strategy.short)
if (exitLong)
strategy.close("Long")
if (exitShort)
strategy.close("Short")
Clean Overlay: VWMA /ADX Trend + VWAP Separation + ATRIf you find this indicator useful to your setup, please consider donating! It took me a lot of time and coding work to create this and any support would be AMAZING! Venmo: @TimInColor Thanks so much!
Clean Overlay: VWMA/ADX Trend + VWAP Separation + ATR
This indicator provides a clean and efficient way to monitor key market metrics directly on your chart without cluttering it with unnecessary lines or plots. It combines VWMA/ADX-based true trend, VWAP separation, and ATR into a single, customizable table overlay. By replacing traditional lines and plots with numerical values, this indicator helps you focus on the most important information while keeping your chart clean and easy to read.
Key Features:
VWMA Trend Direction:
Determines the overall trend using VWMA and VWAP.
Uses ADX to confirm trend strength.
Displays Bullish, Bearish, or Caution based on price position and ADX value.
Trend direction updates only on candle close to avoid false signals.
VWAP Separation:
Shows the distance between the real-time price and VWAP in points above or below.
Helps identify overextended price levels relative to the volume-weighted average price.
ATR (Average True Range): [/b
Displays the current ATR numerical value, which measures market volatility.
Useful for setting stop-loss levels, position sizing, and identifying periods of high or low volatility.
Customizable Table:
The table is fully customizable, including:
Position: Top-right, bottom-right, or bottom-left.
Colors: Customize text and background colors for each metric.
Opacity: Adjust the table’s transparency.
Border: Customize border width and color.
Alerts:
Set alerts for:
VWAP Separation: When the separation crosses a user-defined threshold.
ATR: When the ATR crosses a user-defined threshold.
Trend Direction: When the trend changes (e.g., from Bullish to Bearish or Caution).
How It Helps Clean Up Your Charts:
Replaces Lines with Numerical Values:
Instead of cluttering your chart with multiple lines (e.g., VWMA, VWAP, ATR bands, etc), this indicator consolidates the information into a single table with numerical values.
This makes your chart cleaner and easier to interpret, especially when using multiple timeframes or instruments.
Focus on Key Metrics:
By displaying only the most important metrics (trend direction, VWAP separation, and ATR), this indicator helps you focus on what matters most for your trading decisions.
Customizable and Non-Intrusive:
The table overlay is fully customizable and can be positioned in multiple places on the chart, ensuring it doesn’t interfere with your analysis or trading setup.
How to Use:
Add the Indicator:
Apply the script to your chart in TradingView.
Customize the settings in the Inputs tab to match your preferences.
Interpret the Table:
Top Row (Trend Direction):
Bullish: Price is above both the VWMA and VWAP, and ADX is above the threshold.
Bearish: Price is below both the VWMA and VWAP, and ADX is above the threshold.
Caution: ADX is below or equal to the threshold, OR price is between the VWMA and VWAP.
Middle Row (ATR):
Displays the current timeframe's ATR value, which indicates market volatility.
Bottom Row (VWAP Separation):
Shows the distance between the real-time price and VWAP. Positive values indicate price is above VWAP, while negative values indicate price is below VWAP.
Set Alerts:
Go to the Alerts tab in TradingView and create alerts for:
VWAP Separation: Triggered when the separation crosses a user-defined threshold.
ATR: Triggered when the ATR crosses a user-defined threshold.
Trend Direction: Triggered when the trend changes (e.g., from Bullish to Bearish or Caution).
Customize the Table:
Adjust the table’s position, colors, opacity, and borders to fit your chart’s style.
Example Use Cases:
Trend Following:
Use the Trend Direction to identify the overall market trend and align your trades accordingly.
For example, go long in a Bullish trend and short in a Bearish trend.
Volatility-Based Strategies:
Use the ATR value to adjust your position sizing or set stop-loss levels based on current market volatility.
Mean Reversion:
Use the VWAP Separation to identify overextended price levels and look for mean reversion opportunities.
Custom Interpretations and Uses
This information can be used for a bevy of different uses, and can fit into virtually every strategy as additional confluences and information.
Customization Options:
ADX Threshold: Set the threshold for determining trend strength (default is 23).
Table Position: Choose between Top Right, Bottom Right, or Bottom Left.
Text Colors: Customize the text colors for VWAP Separation, ATR, and Trend Direction.
Borders: Adjust borders width and color.
Example Output:
The table will look something like this:
Bullish
ATR: 1.45
VWAP Separation: 48
Final Notes:
This indicator is designed to simplify your chart analysis by consolidating key metrics into a single, customizable table that provides clear values for trend direction, VWAP separation, and ATR, helping you make informed trading decisions. By replacing unnecessary lines with numerical values, it helps you focus on what matters most while keeping your chart clean and easy to interpret. Whether you’re a trend follower, volatility trader, or mean reversion enthusiast, this indicator provides the tools you need to make informed trading decisions, and in a super clean and simple overlay.
If you find this indicator useful to your setup, please consider donating! It took me a lot of time and coding work to create this and any support would be AMAZING! Venmo: @TimInColor Thanks so much!
Anchored VWAP with Buy/Sell SignalsAnchored VWAP Calculation:
The script calculates the AVWAP starting from a user-defined anchor point (anchor_date).
The AVWAP is calculated using the formula:
AVWAP
=
∑
(
Volume
×
Average Price
)
∑
Volume
AVWAP=
∑Volume
∑(Volume×Average Price)
where the average price is
(
h
i
g
h
+
l
o
w
+
c
l
o
s
e
)
/
3
(high+low+close)/3.
Buy Signal:
A buy signal is generated when the price closes above the AVWAP (ta.crossover(close, avwap)).
Sell Signal:
A sell signal is generated when the price closes below the AVWAP (ta.crossunder(close, avwap)).
Plotting:
The AVWAP is plotted on the chart.
Buy and sell signals are displayed as labels on the chart.
Background Highlighting:
The background is highlighted in green for buy signals and red for sell signals (optional).
True Range & ATRDescription : This indicator plots both the True Range (TR) and the Average True Range (ATR) in a separate pane below the main chart.
- TR represents the absolute price movement range within each candle.
- ATR is a smoothed version of TR over a user-defined period (default: 14), providing insight into market volatility.
- TR is displayed as a histogram for a clearer view of individual candle ranges.
- ATR is plotted as a line to show the smoothed trend of volatility.
This indicator helps traders assess market volatility and potential price movements.
ADX with Dynamic Color Change This Indicator gives clear visualisation of the strength of the trend and direction
Індекс Ентропії Ринку та Математичне ОчікуванняЯкщо індикатор показує значення математичного очікування ентропії
𝐸
=
2
E =2, ось як це можна розшифрувати:
Сутність показника:
𝐸
E є середньою ентропією, що розраховується як зважена сума ентропій для кожного макростану (ріст, зниження, консолідація). Ентропія кожного макростану визначається за формулою
𝑆
=
ln
(
Ω
)
S=ln(Ω), де
Ω
Ω — кількість унікальних мікростанів (комбінацій параметрів
𝑃
P,
Δ
Δ,
𝑉
ℎ
𝑜
𝑢
𝑟
V
hour
), які формують цей макростан.
Що означає значення 2:
Значення
𝑆
=
2
S=2 вказує на те, що ефективна кількість мікростанів для даного макростану становить
𝑒
2
≈
7.4
e
2
≈7.4. Це означає, що у середньому існує приблизно 7–8 різних комбінацій мікростанів, які приводять до формування спостережуваного макростану.
Інтерпретація в контексті ринку:
Середній рівень різноманіття: Значення 2 свідчить про помірний рівень різноманіття ринкових станів. Ринок проявляє не надто просту, але й не надто хаотичну поведінку.
Стійкість або невизначеність: Якщо порівнювати, то нижчі значення
𝐸
E (наприклад, близько 0-1) можуть вказувати на вузький набір умов, які формують тренд (менша стійкість, потенційно більша вразливість до змін), а вищі значення (вище 2) — на дуже різноманітні умови, що можуть свідчити про більшу невизначеність або складність ринкової поведінки.
Потенційна застосовність: Такий рівень ентропії може допомогти трейдеру оцінити, наскільки "розгалуженим" є процес формування тренду на ринку. Помірне значення може бути індикатором того, що ринок має достатньо варіантів для адаптації, але водночас не перебуває у стані надмірного хаосу.
Таким чином, коли
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E знаходиться на рівні 2, це означає, що ринок демонструє середній рівень розмаїтості мікростанів, що формують загальний тренд. Це може бути ознакою збалансованості, але також може сигналізувати про певну гнучкість у ринкових умовах.
SMA10, SMA50, and Bollinger Bands with Fill"This is an indicator that combines MA10, SMA50, and Bollinger Bands. The background color changes when the price is within the ±1σ range of the Bollinger Bands."
Let me know if you'd like any further adjustments!
House of Traders: ATR vs Day Trading Range and PercentageThis indicator compares the current Daily ATR (Average True Range) with the current Day Range and displays the percentage difference.
For example, if the Daily ATR is $5 and the current Day Range is $10, this would be 200% of the ATR value.
The indicator is color-coded (and customizable) to change color based on the percentage difference:
Orange when the Day Range is below the low threshold.
Green when the Day Range is between the low and high thresholds.
Red when the Day Range exceeds the high threshold.
The ATR calculation is always based on the Daily timeframe, regardless of the chart's timeframe. For example, if you are using a 30-minute chart, the ATR is still calculated from the Daily timeframe.
About House of Traders:
House of Traders offers comprehensive coaching and training for day traders, focusing on building consistent trading skills through personalized guidance. Whether you're a beginner or experienced trader, House of Traders provides a proven framework to help you trade with confidence. Join the community and start mastering the U.S. stock market with expert insights and daily trade ideas tailored to your goals.
Important Links
www.houseoftraders.nl
instagram.com
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Smoothed Low-Pass Butterworth Filtered Median [AlphaAlgos]Smoothed Low-Pass Butterworth Filtered Median
This indicator is designed to smooth price action and filter out noise while maintaining the dominant trend. By combining a Butterworth low-pass filter with a median-based smoothing approach , it effectively reduces short-term fluctuations, allowing traders to focus on the true market direction.
How It Works
Median Smoothing: The indicator calculates the 50th percentile (median) of closing prices over a customizable period , making it more robust against outliers compared to traditional moving averages.
Butterworth Filtering: A low-pass filter is applied using an approximation of the Butterworth formula , controlled by the Cutoff Frequency , helping to eliminate high-frequency noise while preserving trends.
EMA Refinement: A 7-period EMA is applied to further smooth the signal, providing a more reliable trend representation.
Features
Trend Smoothing: Reduces market noise and highlights the dominant trend.
Dynamic Color Signals: The EMA line changes color to indicate trend strength and direction.
Configurable Parameters: Customize the median length, cutoff frequency, and EMA length to fit your strategy.
Versatile Use Case: Suitable for both trend-following and mean-reversion strategies.
How to Use
Bullish Signal: When the EMA is below the price and rising , indicating upward momentum.
Bearish Signal: When the EMA is above the price and falling , signaling a potential downtrend.
Reversal Zones: Monitor for trend shifts when the color of the EMA changes.
This indicator provides a clear, noise-free view of market trends , making it ideal for traders seeking improved trend identification and entry signals .
Dynamic Stop Loss & Take ProfitDynamic Stop Loss & Take Profit is a versatile risk management indicator that calculates dynamic stop loss and take profit levels based on the Average True Range (ATR). This indicator helps traders set adaptive exit points by using a configurable ATR multiplier and defining whether they are in a Long (Buy) or Short (Sell) trade.
How It Works
ATR Calculation – The indicator calculates the ATR value over a user-defined period (default: 14).
Stop Loss and Take Profit Multipliers – The ATR value is multiplied by a configurable factor (ranging from 1.5 to 4) to determine volatility-adjusted stop loss and take profit levels.
Trade Type Selection – The user can specify whether they are in a Long (Buy) or Short (Sell) trade.
Long (Buy) Trade:
Stop Loss = Entry Price - (ATR × Stop Loss Multiplier)
Take Profit = Entry Price + (ATR × Take Profit Multiplier)
Short (Sell) Trade:
Stop Loss = Entry Price + (ATR × Stop Loss Multiplier)
Take Profit = Entry Price - (ATR × Take Profit Multiplier)
Features
Configurable ATR length and multipliers
Supports both long and short trades
Clearly plotted Stop Loss (red) and Take Profit (green) levels on the chart
Helps traders manage risk dynamically based on market volatility
This indicator is ideal for traders looking to set adaptive stop loss and take profit levels without relying on fixed price targets.
Stochastic-Dynamic Volatility Band ModelThe Stochastic-Dynamic Volatility Band Model is a quantitative trading approach that leverages statistical principles to model market volatility and generate buy and sell signals. The strategy is grounded in the concepts of volatility estimation and dynamic market regimes, where the core idea is to capture price fluctuations through stochastic models and trade around volatility bands.
Volatility Estimation and Band Construction
The volatility bands are constructed using a combination of historical price data and statistical measures, primarily the standard deviation (σ) of price returns, which quantifies the degree of variation in price movements over a specific period. This methodology is based on the classical works of Black-Scholes (1973), which laid the foundation for using volatility as a core component in financial models. Volatility is a crucial determinant of asset pricing and risk, and it plays a pivotal role in this strategy's design.
Entry and Exit Conditions
The entry conditions are based on the price’s relationship with the volatility bands. A long entry is triggered when the price crosses above the lower volatility band, indicating that the market may have been oversold or is experiencing a reversal to the upside. Conversely, a short entry is triggered when the price crosses below the upper volatility band, suggesting overbought conditions or a potential market downturn.
These entry signals are consistent with the mean reversion theory, which asserts that asset prices tend to revert to their long-term average after deviating from it. According to Poterba and Summers (1988), mean reversion occurs due to overreaction to news or temporary disturbances, leading to price corrections.
The exit condition is based on the number of bars that have elapsed since the entry signal. Specifically, positions are closed after a predefined number of bars, typically set to seven bars, reflecting a short-term trading horizon. This exit mechanism is in line with short-term momentum trading strategies discussed in literature, where traders capitalize on price movements within specific timeframes (Jegadeesh & Titman, 1993).
Market Adaptability
One of the key features of this strategy is its dynamic nature, as it adapts to the changing volatility environment. The volatility bands automatically adjust to market conditions, expanding in periods of high volatility and contracting when volatility decreases. This dynamic adjustment helps the strategy remain robust across different market regimes, as it is capable of identifying both trend-following and mean-reverting opportunities.
This dynamic adaptability is supported by the adaptive market hypothesis (Lo, 2004), which posits that market participants evolve their strategies in response to changing market conditions, akin to the adaptive nature of biological systems.
References:
Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637-654.
Bollinger, J. (1980). Bollinger on Bollinger Bands. Wiley.
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), 65-91.
Lo, A. W. (2004). The Adaptive Markets Hypothesis: Market Efficiency from an Evolutionary Perspective. Journal of Portfolio Management, 30(5), 15-29.
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Multi-Asset Ratio (20 vs 5) - LuchapThis indicator calculates and displays the ratio between the sum of the prices of several base assets and the sum of the prices of several quote assets. You can select up to 20 base assets and 5 quote assets, and enable or disable each asset individually to refine your analysis. This ratio allows you to quickly evaluate the relative performance of different groups of assets.
