Candle ThermalsThis indicator color candles based on their percentage price change, relative to the average, maximum, and minimum changes over the last 100 candles.
-It calculates the percentage change of all candles
-Calculates the minimum, maximum and average in the last 100 bars in percentage change
-Changes color of the candle based on the range between the current percent and min/max value
-The brightest candle provides the highest compound effect to you account if you act on it at the open.
-Candles that have a percentage close to the average then they are barely visible = lowest compound effect to your account
This indicator functions like a "heatmap" for candles, highlighting the relative volatility of price movements in both directions. Strong bullish candles are brighter green, and strong bearish candles are brighter red. It's particularly useful for traders wanting quick visual feedback on price volatility and strength trends within the last 100 bars.
Поиск скриптов по запросу "美元指数跌破100大关"
Global Index Spread RSI StrategyThis strategy leverages the relative strength index (RSI) to monitor the price spread between a global benchmark index (such as AMEX) and the currently opened asset in the chart window. By calculating the spread between these two, the strategy uses RSI to identify oversold and overbought conditions to trigger buy and sell signals.
Key Components:
Global Benchmark Index: The strategy compares the current asset with a predefined global index (e.g., AMEX) to measure relative performance. The choice of a global benchmark allows the trader to analyze the current asset's movement in the context of broader market trends.
Spread Calculation:
The spread is calculated as the percentage difference between the current asset's closing price and the global benchmark index's closing price:
Spread=Current Asset Close−Global Index CloseGlobal Index Close×100
Spread=Global Index CloseCurrent Asset Close−Global Index Close×100
This metric provides a measure of how the current asset is performing relative to the global index. A positive spread indicates the asset is outperforming the benchmark, while a negative spread signals underperformance.
RSI of the Spread: The RSI is then calculated on the spread values. The RSI is a momentum oscillator that ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions in asset prices. An RSI below 30 is considered oversold, indicating a potential buying opportunity, while an RSI above 70 is overbought, suggesting that the asset may be due for a pullback.
Strategy Logic:
Entry Condition: The strategy enters a long position when the RSI of the spread falls below the oversold threshold (default 30). This suggests that the asset may have been oversold relative to the global benchmark and might be due for a reversal.
Exit Condition: The strategy exits the long position when the RSI of the spread rises above the overbought threshold (default 70), indicating that the asset may have become overbought and a price correction is likely.
Visual Reference:
The RSI of the spread is plotted on the chart for visual reference, making it easier for traders to monitor the relative strength of the asset in relation to the global benchmark.
Overbought and oversold levels are also drawn as horizontal reference lines (70 and 30), along with a neutral level at 50 to show market equilibrium.
Theoretical Basis:
The strategy is built on the mean reversion principle, which suggests that asset prices tend to revert to a long-term average over time. When prices move too far from this mean—either being overbought or oversold—they are likely to correct back toward equilibrium. By using RSI to identify these extremes, the strategy aims to profit from price reversals.
Mean Reversion: According to financial theory, asset prices oscillate around a long-term average, and any extreme deviation (overbought or oversold conditions) presents opportunities for price corrections (Poterba & Summers, 1988).
Momentum Indicators (RSI): The RSI is widely used in technical analysis to measure the momentum of an asset. Its application to the spread between the asset and a global benchmark allows for a more nuanced view of relative performance and potential turning points in the asset's price trajectory.
Practical Application:
This strategy works best in markets where relative strength is a key factor in decision-making, such as in equity indices, commodities, or forex markets. By assessing the performance of the asset relative to a global benchmark and utilizing RSI to identify extremes in price movements, the strategy helps traders to make more informed decisions based on potential mean reversion points.
While the "Global Index Spread RSI Strategy" offers a method for identifying potential price reversals based on relative strength and oversold/overbought conditions, it is important to recognize that no strategy is foolproof. The strategy assumes that the historical relationship between the asset and the global benchmark will hold in the future, but financial markets are subject to a wide array of unpredictable factors that can lead to sudden changes in price behavior.
Risk of False Signals:
The strategy relies heavily on the RSI to trigger buy and sell signals. However, like any momentum-based indicator, RSI can generate false signals, particularly in highly volatile or trending markets. In such conditions, the strategy may enter positions too early or exit too late, leading to potential losses.
Market Context:
The strategy may not account for macroeconomic events, news, or other market forces that could cause sudden shifts in asset prices. External factors, such as geopolitical developments, monetary policy changes, or financial crises, can cause a divergence between the asset and the global benchmark, leading to incorrect conclusions from the strategy.
Overfitting Risk:
As with any strategy that uses historical data to make decisions, there is a risk of overfitting the model to past performance. This could result in a strategy that works well on historical data but performs poorly in live trading conditions due to changes in market dynamics.
Execution Risks:
The strategy does not account for slippage, transaction costs, or liquidity issues, which can impact the execution of trades in real-market conditions. In fast-moving markets, prices may move significantly between order placement and execution, leading to worse-than-expected entry or exit prices.
No Guarantee of Profit:
Past performance is not necessarily indicative of future results. The strategy should be used with caution, and risk management techniques (such as stop losses and position sizing) should always be implemented to protect against significant losses.
Traders should thoroughly test and adapt the strategy in a simulated environment before applying it to live trades, and consider seeking professional advice to ensure that their trading activities align with their risk tolerance and financial goals.
References:
Poterba, J. M., & Summers, L. H. (1988). Mean Reversion in Stock Prices: Evidence and Implications. Journal of Financial Economics, 22(1), 27-59.
Chaikin's Money FlowOverview : Chaikin's Money Flow (CMF) is a momentum indicator that measures the buying and selling pressure of a financial instrument over a specified period. By incorporating both price and volume, CMF provides a comprehensive view of market sentiment, helping traders identify potential trend reversals and confirm the strength of existing trends.
Key Features:
Volume-Weighted : Unlike price-only indicators, CMF accounts for trading volume, offering deeper insights into the forces driving price movements.
Oscillatory Nature : CMF oscillates between positive and negative values, typically ranging from -100 to +100, indicating the balance between buying and selling pressure.
Trend Confirmation : Positive CMF values suggest accumulating buying pressure, while negative values indicate distributing selling pressure. This aids in confirming the direction and strength of trends.
Calculation Details :
Intraday Intensity (II) = 100 × (2×Close−High−Low) / (High−Low) × Volume
Condition: If High=Low, II is set to 0 to prevent division by zero.
II_smoothed = SMA(II, lookback)
Applies a Simple Moving Average (SMA) to the Intraday Intensity over the defined lookback period to smooth out short-term fluctuations.
Volume Smoothing:
V_smoothed = EMA(Volume, Volume Smoothing Period)
Utilizes an Exponential Moving Average (EMA) to smooth the volume over the specified smoothing period, giving more weight to recent data.
Money Flow Calculation:
Money Flow = II_smoothed / V_smoothed
Condition: If Vsmoothed=0Vsmoothed=0, Money Flow is set to 0 to avoid division by zero.
Usage Instructions:
Parameters Configuration:
Lookback Period: Determines the number of periods over which Intraday Intensity is averaged. A higher value results in a smoother indicator, reducing sensitivity to short-term price movements.
Volume Smoothing Period: Defines the period for the EMA applied to Volume. Adjusting this parameter affects the responsiveness of the Money Flow indicator to changes in trading volume.
Interpreting the Indicator:
Positive Values (>0): Indicate buying pressure. The higher the value, the stronger the buying interest.
Negative Values (<0): Signal selling pressure. The lower the value, the more intense the selling activity.
Crossovers: Watch for Money Flow crossing above the zero line as potential buy signals and crossing below as potential sell signals.
Divergence: Identify divergences between Money Flow and price movements to anticipate possible trend reversals.
Complementary Analysis:
Confluence with Other Indicators: Use CMF in conjunction with trend indicators like Moving Averages or oscillators like RSI to enhance signal reliability.
Volume Confirmation: CMF's volume-weighted approach makes it a powerful tool for confirming the validity of price trends and breakouts.
Acknowledgment: This implementation of Chaikin's Money Flow Indicator is inspired by and derived from the methodologies presented in "Statistically Sound Indicators" by Timothy Masters. The indicator has been meticulously translated to Pine Script to maintain the statistical integrity and effectiveness outlined in the source material.
Disclaimer: The Chaikin's Money Flow Indicator is a tool designed to assist in trading decisions. It does not guarantee profits and should be used in conjunction with other analysis methods. Trading involves risk, and it's essential to perform thorough testing and validation before deploying any indicator in live trading environments.
TrigWave Suite [InvestorUnknown]The TrigWave Suite combines Sine-weighted, Cosine-weighted, and Hyperbolic Tangent moving averages (HTMA) with a Directional Movement System (DMS) and a Relative Strength System (RSS).
Hyperbolic Tangent Moving Average (HTMA)
The HTMA smooths the price by applying a hyperbolic tangent transformation to the difference between the price and a simple moving average. It also adjusts this value by multiplying it by a standard deviation to create a more stable signal.
// Function to calculate Hyperbolic Tangent
tanh(x) =>
e_x = math.exp(x)
e_neg_x = math.exp(-x)
(e_x - e_neg_x) / (e_x + e_neg_x)
// Function to calculate Hyperbolic Tangent Moving Average
htma(src, len, mul) =>
tanh_src = tanh((src - ta.sma(src, len)) * mul) * ta.stdev(src, len) + ta.sma(src, len)
htma = ta.sma(tanh_src, len)
Sine-Weighted Moving Average (SWMA)
The SWMA applies sine-based weights to historical prices. This gives more weight to the central data points, making it responsive yet less prone to noise.
// Function to calculate the Sine-Weighted Moving Average
f_Sine_Weighted_MA(series float src, simple int length) =>
var float sine_weights = array.new_float(0)
array.clear(sine_weights) // Clear the array before recalculating weights
for i = 0 to length - 1
weight = math.sin((math.pi * (i + 1)) / length)
array.push(sine_weights, weight)
// Normalize the weights
sum_weights = array.sum(sine_weights)
for i = 0 to length - 1
norm_weight = array.get(sine_weights, i) / sum_weights
array.set(sine_weights, i, norm_weight)
// Calculate Sine-Weighted Moving Average
swma = 0.0
if bar_index >= length
for i = 0 to length - 1
swma := swma + array.get(sine_weights, i) * src
swma
Cosine-Weighted Moving Average (CWMA)
The CWMA uses cosine-based weights for data points, which produces a more stable trend-following behavior, especially in low-volatility markets.
f_Cosine_Weighted_MA(series float src, simple int length) =>
var float cosine_weights = array.new_float(0)
array.clear(cosine_weights) // Clear the array before recalculating weights
for i = 0 to length - 1
weight = math.cos((math.pi * (i + 1)) / length) + 1 // Shift by adding 1
array.push(cosine_weights, weight)
// Normalize the weights
sum_weights = array.sum(cosine_weights)
for i = 0 to length - 1
norm_weight = array.get(cosine_weights, i) / sum_weights
array.set(cosine_weights, i, norm_weight)
// Calculate Cosine-Weighted Moving Average
cwma = 0.0
if bar_index >= length
for i = 0 to length - 1
cwma := cwma + array.get(cosine_weights, i) * src
cwma
Directional Movement System (DMS)
DMS is used to identify trend direction and strength based on directional movement. It uses ADX to gauge trend strength and combines +DI and -DI for directional bias.
// Function to calculate Directional Movement System
f_DMS(simple int dmi_len, simple int adx_len) =>
up = ta.change(high)
down = -ta.change(low)
plusDM = na(up) ? na : (up > down and up > 0 ? up : 0)
minusDM = na(down) ? na : (down > up and down > 0 ? down : 0)
trur = ta.rma(ta.tr, dmi_len)
plus = fixnan(100 * ta.rma(plusDM, dmi_len) / trur)
minus = fixnan(100 * ta.rma(minusDM, dmi_len) / trur)
sum = plus + minus
adx = 100 * ta.rma(math.abs(plus - minus) / (sum == 0 ? 1 : sum), adx_len)
dms_up = plus > minus and adx > minus
dms_down = plus < minus and adx > plus
dms_neutral = not (dms_up or dms_down)
signal = dms_up ? 1 : dms_down ? -1 : 0
Relative Strength System (RSS)
RSS employs RSI and an adjustable moving average type (SMA, EMA, or HMA) to evaluate whether the market is in a bullish or bearish state.
// Function to calculate Relative Strength System
f_RSS(rsi_src, rsi_len, ma_type, ma_len) =>
rsi = ta.rsi(rsi_src, rsi_len)
ma = switch ma_type
"SMA" => ta.sma(rsi, ma_len)
"EMA" => ta.ema(rsi, ma_len)
"HMA" => ta.hma(rsi, ma_len)
signal = (rsi > ma and rsi > 50) ? 1 : (rsi < ma and rsi < 50) ? -1 : 0
ATR Adjustments
To minimize false signals, the HTMA, SWMA, and CWMA signals are adjusted with an Average True Range (ATR) filter:
// Calculate ATR adjusted components for HTMA, CWMA and SWMA
float atr = ta.atr(atr_len)
float htma_up = htma + (atr * atr_mult)
float htma_dn = htma - (atr * atr_mult)
float swma_up = swma + (atr * atr_mult)
float swma_dn = swma - (atr * atr_mult)
float cwma_up = cwma + (atr * atr_mult)
float cwma_dn = cwma - (atr * atr_mult)
This adjustment allows for better adaptation to varying market volatility, making the signal more reliable.
Signals and Trend Calculation
The indicator generates a Trend Signal by aggregating the output from each component. Each component provides a directional signal that is combined to form a unified trend reading. The trend value is then converted into a long (1), short (-1), or neutral (0) state.
Backtesting Mode and Performance Metrics
The Backtesting Mode includes a performance metrics table that compares the Buy and Hold strategy with the TrigWave Suite strategy. Key statistics like Sharpe Ratio, Sortino Ratio, and Omega Ratio are displayed to help users assess performance. Note that due to labels and plotchar use, automatic scaling may not function ideally in backtest mode.
Alerts and Visualization
Trend Direction Alerts: Set up alerts for long and short signals
Color Bars and Gradient Option: Bars are colored based on the trend direction, with an optional gradient for smoother visual feedback.
Important Notes
Customization: Default settings are experimental and not intended for trading/investing purposes. Users are encouraged to adjust and calibrate the settings to optimize results according to their trading style.
Backtest Results Disclaimer: Please note that backtest results are not indicative of future performance, and no strategy guarantees success.
Advanced Physics Financial Indicator Each component represents a scientific theory and is applied to the price data in a way that reflects key principles from that theory.
Detailed Explanation
1. Fractal Geometry - High/Low Signal
Concept: Fractal geometry studies self-similar patterns that repeat at different scales. In markets, fractals can be used to detect recurring patterns or turning points.
Implementation: The script detects pivot highs and lows using ta.pivothigh and ta.pivotlow, representing local turning points in price. The fractalSignal is set to 1 for a pivot high, -1 for a pivot low, and 0 if there is no signal. This logic reflects the cyclical, self-similar nature of price movements.
Practical Use: This signal is useful for identifying local tops and bottoms, allowing traders to spot potential reversals or consolidation points where fractal patterns emerge.
2. Quantum Mechanics - Probabilistic Monte Carlo Simulation
Concept: Quantum mechanics introduces uncertainty and probability into systems, much like how future price movements are inherently uncertain. Monte Carlo simulations are used to model a range of possible outcomes based on random inputs.
Implementation: In this script, we simulate 100 random outcomes by generating a random number between -1 and 1 for each iteration. These random values are stored in an array, and the average of these values is calculated to represent the Quantum Signal.
Practical Use: This probabilistic signal provides a sense of randomness and uncertainty in the market, reflecting the possibility of price movement in either direction. It simulates the market’s chaotic nature by considering multiple possible outcomes and their average.
3. Thermodynamics - Efficiency Ratio Signal
Concept: Thermodynamics deals with energy efficiency and entropy in systems. The efficiency ratio in financial terms can be used to measure how efficiently the price is moving relative to volatility.
Implementation: The Efficiency Ratio is calculated as the absolute price change over n periods divided by the sum of absolute changes for each period within n. This ratio shows how much of the price movement is directional versus random, mimicking the concept of efficiency in thermodynamic systems.
Practical Use: A high efficiency ratio suggests that the market is trending smoothly (high efficiency), while a low ratio indicates choppy, non-directional movement (low efficiency, or high entropy).
4. Chaos Theory - ATR Signal
Concept: Chaos theory studies how complex systems are highly sensitive to initial conditions, leading to unpredictable behavior. In markets, chaotic price movements can often be captured through volatility indicators.
Implementation: The script uses a very long ATR period (1000) to reflect slow-moving chaos over time. The Chaos Signal is computed by measuring the deviation of the current price from its long-term average (SMA), normalized by ATR. This captures price deviations over time, hinting at chaotic market behavior.
Practical Use: The signal measures how far the price deviates from its long-term average, which can signal the degree of chaos or extreme behavior in the market. High deviations indicate chaotic or volatile conditions, while low deviations suggest stability.
5. Network Theory - Correlation with BTC
Concept: Network theory studies how different components within a system are interconnected. In markets, assets are often correlated, meaning that price movements in one asset can influence or be influenced by another.
Implementation: This indicator calculates the correlation between the asset’s price and the price of Bitcoin (BTC) over 30 periods. The Network Signal shows how connected the asset is to BTC, reflecting broader market dynamics.
Practical Use: In a highly correlated market, BTC can act as a leading indicator for other assets. A strong correlation with BTC might suggest that the asset is likely to move in line with Bitcoin, while a weak or negative correlation might indicate that the asset is moving independently.
6. String Theory - RSI & MACD Interaction
Concept: String theory attempts to unify the fundamental forces of nature into a single framework. In trading, we can view the RSI and MACD as interacting forces that provide insights into momentum and trend.
Implementation: The script calculates the RSI and MACD and combines them into a single signal. The formula for String Signal is (RSI - 50) / 100 + (MACD Line - Signal Line) / 100, normalizing both indicators to a scale where their contributions are additive. The RSI represents momentum, and MACD shows trend direction and strength.
Practical Use: This signal helps in detecting moments where momentum (RSI) and trend strength (MACD) align, giving a clearer picture of the asset's direction and overbought/oversold conditions. It unifies these two indicators to create a more holistic view of market behavior.
7. Fluid Dynamics - On-Balance Volume (OBV) Signal
Concept: Fluid dynamics studies how fluids move and flow. In markets, volume can be seen as a "flow" that drives price movement, much like how fluid dynamics describe the flow of liquids.
Implementation: The script uses the OBV (On-Balance Volume) indicator to track the cumulative flow of volume based on price changes. The signal is further normalized by its moving average to smooth out fluctuations and make it more reflective of price pressure over time.
Practical Use: The Fluid Signal shows how the flow of volume is driving price action. If the OBV rises significantly, it suggests that there is strong buying pressure, while a falling OBV indicates selling pressure. It’s analogous to how pressure builds in a fluid system.
8. Final Signal - Combining All Physics-Based Indicators
Implementation: Each of the seven physics-inspired signals is combined into a single Final Signal by averaging their values. This approach blends different market insights from various scientific domains, creating a comprehensive view of the market’s condition.
Practical Use: The final signal gives you a holistic, multi-dimensional view of the market by merging different perspectives (fractal behavior, quantum probability, efficiency, chaos, correlation, momentum/trend, and volume flow). This approach helps traders understand the market's dynamics from multiple angles, offering deeper insights than any single indicator.
9. Color Coding Based on Signal Extremes
Concept: The color of the final signal plot dynamically reflects whether the market is in an extreme state.
Implementation: The signal color is determined using percentiles. If the Final Signal is in the top 55th percentile of its range, the signal is green (bullish). If it is between the 45th and 55th percentiles, it is orange (neutral). If it falls below the 45th percentile, it is red (bearish).
Practical Use: This visual representation helps traders quickly identify the strength of the signal. Bullish conditions (green), neutral conditions (orange), and bearish conditions (red) are clearly distinguished, simplifying decision-making.
Volumatic Variable Index Dynamic Average [BigBeluga]The Volumatic VIDYA (Variable Index Dynamic Average) indicator is a trend-following tool that calculates and visualizes both the current trend and the corresponding buy and sell pressure within each trend phase. Using the Variable Index Dynamic Average as the core smoothing technique, this indicator also plots volume levels of lows and highs based on market structure pivot points, providing traders with key insights into price and volume dynamics.
Additionally, it generates delta volume values to help traders evaluate buy-sell pressure balance during each trend, making it a powerful tool for understanding market sentiment shifts.
BTC:
TSLA:
🔵 IDEA
The Volumatic VIDYA indicator's core idea is to provide a dynamic, adaptive smoothing tool that identifies trends while simultaneously calculating the volume pressure behind them. The VIDYA line, based on the Variable Index Dynamic Average, adjusts according to the strength of the price movements, offering a more adaptive response to the market compared to standard moving averages.
By calculating and displaying the buy and sell volume pressure throughout each trend, the indicator provides traders with key insights into market participation. The horizontal lines drawn from the highs and lows of market structure pivots give additional clarity on support and resistance levels, backed by average volume at these points. This dual analysis of trend and volume allows traders to evaluate the strength and potential of market movements more effectively.
🔵 KEY FEATURES & USAGE
VIDYA Calculation:
The Variable Index Dynamic Average (VIDYA) is a special type of moving average that adjusts dynamically to the market’s volatility and momentum. Unlike traditional moving averages that use fixed periods, VIDYA adjusts its smoothing factor based on the relative strength of the price movements, using the Chande Momentum Oscillator (CMO) to capture the magnitude of price changes. When momentum is strong, VIDYA adapts and smooths out price movements quicker, making it more responsive to rapid price changes. This makes VIDYA more adaptable to volatile markets compared to traditional moving averages such as the Simple Moving Average (SMA) or the Exponential Moving Average (EMA), which are less flexible.
// VIDYA (Variable Index Dynamic Average) function
vidya_calc(src, vidya_length, vidya_momentum) =>
float momentum = ta.change(src)
float sum_pos_momentum = math.sum((momentum >= 0) ? momentum : 0.0, vidya_momentum)
float sum_neg_momentum = math.sum((momentum >= 0) ? 0.0 : -momentum, vidya_momentum)
float abs_cmo = math.abs(100 * (sum_pos_momentum - sum_neg_momentum) / (sum_pos_momentum + sum_neg_momentum))
float alpha = 2 / (vidya_length + 1)
var float vidya_value = 0.0
vidya_value := alpha * abs_cmo / 100 * src + (1 - alpha * abs_cmo / 100) * nz(vidya_value )
ta.sma(vidya_value, 15)
When momentum is strong, VIDYA adapts and smooths out price movements quicker, making it more responsive to rapid price changes. This makes VIDYA more adaptable to volatile markets compared to traditional moving averages
Triangle Trend Shift Signals:
The indicator marks trend shifts with up and down triangles, signaling a potential change in direction. These signals appear when the price crosses above a VIDYA during an uptrend or crosses below during a downtrend.
Volume Pressure Calculation:
The Volumatic VIDYA tracks the buy and sell pressure during each trend, calculating the cumulative volume for up and down bars. Positive delta volume occurs during uptrends due to higher buy pressure, while negative delta volume reflects higher sell pressure during downtrends. The delta is displayed in real-time on the chart, offering a quick view of volume imbalances.
Market Structure Pivot Lines with Volume Labels:
The indicator draws horizontal lines based on market structure pivots, which are calculated using the highs and lows of price action. These lines are extended on the chart until price crosses them. The indicator also plots the average volume over a 6-bar range to provide a clearer understanding of volume dynamics at critical points.
🔵 CUSTOMIZATION
VIDYA Length & Momentum: Control the sensitivity of the VIDYA line by adjusting the length and momentum settings, allowing traders to customize the smoothing effect to match their trading style.
Volume Pivot Detection: Set the number of bars to consider for identifying pivots, which influences the calculation of the average volume at key levels.
Band Distance: Adjust the band distance multiplier for controlling how far the upper and lower bands extend from the VIDYA line, based on the ATR (Average True Range).
Multi-timeframe 24 moving averages + BB+SAR+Supertrend+VWAP █ OVERVIEW
The script allows to display up to 24 moving averages ("MA"'s) across 5 timeframes plus two bands (Bollinger Bands or Supertrend or Parabolic SAR or VWAP bands) each from its own timeframe.
The main difference of this script from many similar ones is the flexibility of its settings:
- Bulk enable/disable and/or change properties of several MAs at once.
- Save 3 of your frequently used templates as presets using CSV text configurations.
█ HOW TO USE
Some use examples:
In order to "show 31, 50, 200 EMAs and 20, 100, 200 SMAs for each of 1H, 4H, D, W, M timeframes using blue for short MA, yellow for mid MA and red for long MA" use the settings as shown on a screenshot below.
In order to "Show a band of chart timeframe MA's of lengths 5, 8, 13, 21, 34, 55, 100 and 200 plus some 1H, 4H, D and W MAs. Be able to quickly switch off the band of chart tf's MAs. For chart timeframe MA's only show labels for 21, 100 and 200 EMAs". You can set TF1 and TF2 to chart's TF and set you fib MAs there and configure fixed higher timeframe MAs using TF3, TF4 and TF5 (e.g. using 1H, D and W timeframes and using 1H 800 in place of 4H 200 MA). However, quicker way may be using CSV - the syntax is very simple and intuitive, see Preset 2 as it comes in the script. You can easily switch chart tf's band of MAs by toggling on/off your chart timeframe TF's (in our example, TF1 and TF2).
The settings are either obvious or explained in tooltips.
Note 1: When using group settings and CSV presets do not forget that individual setting affected will no have any effect. So, if some setting does not work, check whether it is overridden with some group setting or a CSV preset.
Note 2: Sometimes you can notice parts of MA's hanging in the air, not lasting up to the last bar. This is not a bug as explained on this screenshot:
█ FOR DEVELOPERS
The script is a use case of my CSVParser library, which in turn uses Autotable library, both of which I hope will be quite helpful. Autotable is so powerful and comprehensive that you will hardly ever wish to use normal table functions again for complex tables.
The indicator was inspired by Pablo Limonetti's url=https://www.tradingview.com/script/nFs56VUZ/]Multi Timeframe Moving Averages and Raging @RagingRocketBull's # Multi SMA EMA WMA HMA BB (5x8 MAs Bollinger Bands) MAX MTF - RRB
KAMA Cloud STIndicator:
Description:
The KAMA Cloud indicator is a sophisticated trading tool designed to provide traders with insights into market trends and their intensity. This indicator is built on the Kaufman Adaptive Moving Average (KAMA), which dynamically adjusts its sensitivity to filter out market noise and respond to significant price movements. The KAMA Cloud leverages multiple KAMAs to gauge trend direction and strength, offering a visual representation that is easy to interpret.
How It Works:
The KAMA Cloud uses twenty different KAMA calculations, each set to a distinct lookback period ranging from 5 to 100. These KAMAs are calculated using the average of the open, high, low, and close prices (OHLC4), ensuring a balanced view of price action. The relative positioning of these KAMAs helps determine the direction of the market trend and its momentum.
By measuring the cumulative relative distance between these KAMAs, the indicator effectively assesses the overall trend strength, akin to how the Average True Range (ATR) measures market volatility. This cumulative measure helps in identifying the trend’s robustness and potential sustainability.
The visualization component of the KAMA Cloud is particularly insightful. It plots a 'cloud' formed between the base KAMA (set at a 100-period lookback) and an adjusted KAMA that incorporates the cumulative relative distance scaled up. This cloud changes color based on the trend direction — green for upward trends and red for downward trends, providing a clear, visual representation of market conditions.
How the Strategy Works:
The KAMA Cloud ST strategy employs multiple KAMA calculations with varying lengths to capture the nuances of market trends. It measures the relative distances between these KAMAs to determine the trend's direction and strength, much like the original indicator. The strategy enhances decision-making by plotting a 'cloud' formed between the base KAMA (set to a 100-period lookback) and an adjusted KAMA that scales according to the cumulative relative distance of all KAMAs.
Key Components of the Strategy:
Multiple KAMA Layers: The strategy calculates KAMAs for periods ranging from 5 to 100 to analyze short to long-term market trends.
Dynamic Cloud: The cloud visually represents the trend’s strength and direction, updating in real-time as the market evolves.
Signal Generation: Trade signals are generated based on the orientation of the cloud relative to a smoothed version of the upper KAMA boundary. Long positions are initiated when the market trend is upward, and the current cloud value is above its smoothed average. Conversely, positions are closed when the trend reverses, indicated by the cloud falling below the smoothed average.
Suggested Usage:
Market: Stocks, not cryptocurrency
Timeframe: 1 Hour
Indicator:
Mean Reversion Cloud (Ornstein-Uhlenbeck) // AlgoFyreThe Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator detects mean-reversion opportunities by applying the Ornstein-Uhlenbeck process. It calculates a dynamic mean using an Exponential Weighted Moving Average, surrounded by volatility bands, signaling potential buy/sell points when prices deviate.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Adaptive Mean Calculation
🔸Volatility-Based Cloud
🔸Speed of Reversion (θ)
🔶 FUNCTIONALITY
🔸Dynamic Mean and Volatility Bands
🞘 How it works
🞘 How to calculate
🞘 Code extract
🔸Visualization via Table and Plotshapes
🞘 Table Overview
🞘 Plotshapes Explanation
🞘 Code extract
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
🞘 Understanding What to Look For on the Chart
🞘 Possible Entry Signals
🞘 Possible Take Profit Strategies
🞘 Possible Stop-Loss Levels
🞘 Additional Tips
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) is a unique indicator that applies the Ornstein-Uhlenbeck stochastic process to identify mean-reverting behavior in asset prices. Unlike traditional moving average-based indicators, this model uses an Exponentially Weighted Moving Average (EWMA) to calculate the long-term mean, dynamically adjusting to recent price movements while still considering all historical data. It also incorporates volatility bands, providing a "cloud" that visually highlights overbought or oversold conditions. By calculating the speed of mean reversion (θ) through the autocorrelation of log returns, this indicator offers traders a more nuanced and mathematically robust tool for identifying mean-reversion opportunities. These innovations make it especially useful for markets that exhibit range-bound characteristics, offering timely buy and sell signals based on statistical deviations from the mean.
🔸Adaptive Mean Calculation Traditional MA indicators use fixed lengths, which can lead to lagging signals or over-sensitivity in volatile markets. The Mean Reversion Cloud uses an Exponentially Weighted Moving Average (EWMA), which adapts to price movements by dynamically adjusting its calculation, offering a more responsive mean.
🔸Volatility-Based Cloud Unlike simple moving averages that only plot a single line, the Mean Reversion Cloud surrounds the dynamic mean with volatility bands. These bands, based on standard deviations, provide traders with a visual cue of when prices are statistically likely to revert, highlighting potential reversal zones.
🔸Speed of Reversion (θ) The indicator goes beyond price averages by calculating the speed at which the price reverts to the mean (θ), using the autocorrelation of log returns. This gives traders an additional tool for estimating the likelihood and timing of mean reversion, making the signals more reliable in practice.
🔶 FUNCTIONALITY The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator is designed to detect potential mean-reversion opportunities in asset prices by applying the Ornstein-Uhlenbeck stochastic process. It calculates a dynamic mean through the Exponentially Weighted Moving Average (EWMA) and plots volatility bands based on the standard deviation of the asset's price over a specified period. These bands create a "cloud" that represents expected price fluctuations, helping traders to identify overbought or oversold conditions. By calculating the speed of reversion (θ) from the autocorrelation of log returns, the indicator offers a more refined way of assessing how quickly prices may revert to the mean. Additionally, the inclusion of volatility provides a comprehensive view of market conditions, allowing for more accurate buy and sell signals.
Let's dive into the details:
🔸Dynamic Mean and Volatility Bands The dynamic mean (μ) is calculated using the EWMA, giving more weight to recent prices but considering all historical data. This process closely resembles the Ornstein-Uhlenbeck (OU) process, which models the tendency of a stochastic variable (such as price) to revert to its mean over time. Volatility bands are plotted around the mean using standard deviation, forming the "cloud" that signals overbought or oversold conditions. The cloud adapts dynamically to price fluctuations and market volatility, making it a versatile tool for mean-reversion strategies. 🞘 How it works Step one: Calculate the dynamic mean (μ) The Ornstein-Uhlenbeck process describes how a variable, such as an asset's price, tends to revert to a long-term mean while subject to random fluctuations. In this indicator, the EWMA is used to compute the dynamic mean (μ), mimicking the mean-reverting behavior of the OU process. Use the EWMA formula to compute a weighted mean that adjusts to recent price movements. Assign exponentially decreasing weights to older data while giving more emphasis to current prices. Step two: Plot volatility bands Calculate the standard deviation of the price over a user-defined period to determine market volatility. Position the upper and lower bands around the mean by adding and subtracting a multiple of the standard deviation. 🞘 How to calculate Exponential Weighted Moving Average (EWMA)
The EWMA dynamically adjusts to recent price movements:
mu_t = lambda * mu_{t-1} + (1 - lambda) * P_t
Where mu_t is the mean at time t, lambda is the decay factor, and P_t is the price at time t. The higher the decay factor, the more weight is given to recent data.
Autocorrelation (ρ) and Standard Deviation (σ)
To measure mean reversion speed and volatility: rho = correlation(log(close), log(close ), length) Where rho is the autocorrelation of log returns over a specified period.
To calculate volatility:
sigma = stdev(close, length)
Where sigma is the standard deviation of the asset's closing price over a specified length.
Upper and Lower Bands
The upper and lower bands are calculated as follows:
upper_band = mu + (threshold * sigma)
lower_band = mu - (threshold * sigma)
Where threshold is a multiplier for the standard deviation, usually set to 2. These bands represent the range within which the price is expected to fluctuate, based on current volatility and the mean.
🞘 Code extract // Calculate Returns
returns = math.log(close / close )
// Calculate Long-Term Mean (μ) using EWMA over the entire dataset
var float ewma_mu = na // Initialize ewma_mu as 'na'
ewma_mu := na(ewma_mu ) ? close : decay_factor * ewma_mu + (1 - decay_factor) * close
mu = ewma_mu
// Calculate Autocorrelation at Lag 1
rho1 = ta.correlation(returns, returns , corr_length)
// Ensure rho1 is within valid range to avoid errors
rho1 := na(rho1) or rho1 <= 0 ? 0.0001 : rho1
// Calculate Speed of Mean Reversion (θ)
theta = -math.log(rho1)
// Calculate Volatility (σ)
sigma = ta.stdev(close, corr_length)
// Calculate Upper and Lower Bands
upper_band = mu + threshold * sigma
lower_band = mu - threshold * sigma
🔸Visualization via Table and Plotshapes
The table shows key statistics such as the current value of the dynamic mean (μ), the number of times the price has crossed the upper or lower bands, and the consecutive number of bars that the price has remained in an overbought or oversold state.
Plotshapes (diamonds) are used to signal buy and sell opportunities. A green diamond below the price suggests a buy signal when the price crosses below the lower band, and a red diamond above the price indicates a sell signal when the price crosses above the upper band.
The table and plotshapes provide a comprehensive visualization, combining both statistical and actionable information to aid decision-making.
🞘 Code extract // Reset consecutive_bars when price crosses the mean
var consecutive_bars = 0
if (close < mu and close >= mu) or (close > mu and close <= mu)
consecutive_bars := 0
else if math.abs(deviation) > 0
consecutive_bars := math.min(consecutive_bars + 1, dev_length)
transparency = math.max(0, math.min(100, 100 - (consecutive_bars * 100 / dev_length)))
🔶 INSTRUCTIONS
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator can be set up by adding it to your TradingView chart and configuring parameters such as the decay factor, autocorrelation length, and volatility threshold to suit current market conditions. Look for price crossovers and deviations from the calculated mean for potential entry signals. Use the upper and lower bands as dynamic support/resistance levels for setting take profit and stop-loss orders. Combining this indicator with additional trend-following or momentum-based indicators can improve signal accuracy. Adjust settings for better mean-reversion detection and risk management.
🔸Step-by-Step Guidelines
🞘 Setting Up the Indicator
Adding the Indicator to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "Mean Reversion Cloud (Ornstein-Uhlenbeck)" in the indicators list.
Click on the indicator to add it to your chart.
Configuring the Indicator:
Open the indicator settings by clicking on the gear icon next to its name on the chart.
Decay Factor: Adjust the decay factor (λ) to control the responsiveness of the mean calculation. A higher value prioritizes recent data.
Autocorrelation Length: Set the autocorrelation length (θ) for calculating the speed of mean reversion. Longer lengths consider more historical data.
Threshold: Define the number of standard deviations for the upper and lower bands to determine how far price must deviate to trigger a signal.
Chart Setup:
Select the appropriate timeframe (e.g., 1-hour, daily) based on your trading strategy.
Consider using other indicators such as RSI or MACD to confirm buy and sell signals.
🞘 Understanding What to Look For on the Chart
Indicator Behavior:
Observe how the price interacts with the dynamic mean and volatility bands. The price staying within the bands suggests mean-reverting behavior, while crossing the bands signals potential entry points.
The indicator calculates overbought/oversold conditions based on deviation from the mean, highlighted by color-coded cloud areas on the chart.
Crossovers and Deviation:
Look for crossovers between the price and the mean (μ) or the bands. A bullish crossover occurs when the price crosses below the lower band, signaling a potential buying opportunity.
A bearish crossover occurs when the price crosses above the upper band, suggesting a potential sell signal.
Deviations from the mean indicate market extremes. A large deviation indicates that the price is far from the mean, suggesting a potential reversal.
Slope and Direction:
Pay attention to the slope of the mean (μ). A rising slope suggests bullish market conditions, while a declining slope signals a bearish market.
The steepness of the slope can indicate the strength of the mean-reversion trend.
🞘 Possible Entry Signals
Bullish Entry:
Crossover Entry: Enter a long position when the price crosses below the lower band with a positive deviation from the mean.
Confirmation Entry: Use additional indicators like RSI (above 50) or increasing volume to confirm the bullish signal.
Bearish Entry:
Crossover Entry: Enter a short position when the price crosses above the upper band with a negative deviation from the mean.
Confirmation Entry: Look for RSI (below 50) or decreasing volume to confirm the bearish signal.
Deviation Confirmation:
Enter trades when the deviation from the mean is significant, indicating that the price has strayed far from its expected value and is likely to revert.
🞘 Possible Take Profit Strategies
Static Take Profit Levels:
Set predefined take profit levels based on historical volatility, using the upper and lower bands as guides.
Place take profit orders near recent support/resistance levels, ensuring you're capitalizing on the mean-reversion behavior.
Trailing Stop Loss:
Use a trailing stop based on a percentage of the price deviation from the mean to lock in profits as the trend progresses.
Adjust the trailing stop dynamically along the calculated bands to protect profits as the price returns to the mean.
Deviation-Based Exits:
Exit when the deviation from the mean starts to decrease, signaling that the price is returning to its equilibrium.
🞘 Possible Stop-Loss Levels
Initial Stop Loss:
Place an initial stop loss outside the lower band (for long positions) or above the upper band (for short positions) to protect against excessive deviations.
Use a volatility-based buffer to avoid getting stopped out during normal price fluctuations.
Dynamic Stop Loss:
Move the stop loss closer to the mean as the price converges back towards equilibrium, reducing risk.
Adjust the stop loss dynamically along the bands to account for sudden market movements.
🞘 Additional Tips
Combine with Other Indicators:
Enhance your strategy by combining the Mean Reversion Cloud with momentum indicators like MACD, RSI, or Bollinger Bands to confirm market conditions.
Backtesting and Practice:
Backtest the indicator on historical data to understand how it performs in various market environments.
Practice using the indicator on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The indicator reacts to price data and might not account for news-driven events that can cause large deviations.
🔸Customize settings 🞘 Decay Factor (λ): Defines the weight assigned to recent price data in the calculation of the mean. A value closer to 1 places more emphasis on recent prices, while lower values create a smoother, more lagging mean.
🞘 Autocorrelation Length (θ): Sets the period for calculating the speed of mean reversion and volatility. Longer lengths capture more historical data, providing smoother calculations, while shorter lengths make the indicator more responsive.
🞘 Threshold (σ): Specifies the number of standard deviations used to create the upper and lower bands. Higher thresholds widen the bands, producing fewer signals, while lower thresholds tighten the bands for more frequent signals.
🞘 Max Gradient Length (γ): Determines the maximum number of consecutive bars for calculating the deviation gradient. This setting impacts the transparency of the plotted bands based on the length of deviation from the mean.
🔶 CONCLUSION
The Mean Reversion Cloud (Ornstein-Uhlenbeck) indicator offers a sophisticated approach to identifying mean-reversion opportunities by applying the Ornstein-Uhlenbeck stochastic process. This dynamic indicator calculates a responsive mean using an Exponentially Weighted Moving Average (EWMA) and plots volatility-based bands to highlight overbought and oversold conditions. By incorporating advanced statistical measures like autocorrelation and standard deviation, traders can better assess market extremes and potential reversals. The indicator’s ability to adapt to price behavior makes it a versatile tool for traders focused on both short-term price deviations and longer-term mean-reversion strategies. With its unique blend of statistical rigor and visual clarity, the Mean Reversion Cloud provides an invaluable tool for understanding and capitalizing on market inefficiencies.
Prometheus StochasticThe Stochastic indicator is a popular indicator developed in the 1950s. It is designed to identify overbought and oversold scenarios on different assets. A value above 80 is considered overbought and a value below 20 is considered oversold.
The formula is as follows:
%k = ((Close - Low_i) / (High_i / Low_i)) * 100
Low_i and High_i represent the lowest low and highest high of the selected period.
The Prometheus version takes a slightly different approach:
%k = ((High - Lowest_Close_i) / (High_i / Low_i)) * 100
Using the Current High minus the Lowest Close provides us with a more robust range that can be slightly more sensitive to moves and provide a different perspective.
Code:
stoch_func(src_close, src_high, src_low, length) =>
100 * (src_high - ta.lowest(src_close, length)) / (ta.highest(src_high, length) - ta.lowest(src_low, length))
This is the function that returns our Stochastic indicator.
What period do we use for the calculation? Let Prometheus handle that, we utilize a Sum of Squared Error calculation to find what lookback values can be most useful for a trader. How we do it is we calculate a Simple Moving Average or SMA and the indicator using a lot of different bars back values. Then if there is an event, characterized by the indicator crossing above 80 or below 20, we subtract the close by the SMA and square it. If there is no event we return a big value, we want the error to be as small as possible. Because we loop over every value for bars back, we get the value with the smallest error. We also do this for the smoothing values.
// Function to calculate SSE for a given combination of N, K, and D
sse_calc(_N, _K, _D) =>
SMA = ta.sma(close, _N)
sf = stoch_func(close, high, low, _N)
k = ta.sma(sf, _K)
d = ta.sma(k, _D)
var float error = na
if ta.crossover(d, 80) or ta.crossunder(d, 20)
error := math.pow(close - SMA, 2)
else
error := 999999999999999999999999999999999999999
error
var int best_N = na
var int best_K = na
var int best_D = na
var float min_SSE = na
// Loop through all combinations of N, K, and D
for N in N_range
for K in K_range
for D in D_range
sse = sse_calc(N, K, D)
if (na(min_SSE) or sse < min_SSE)
min_SSE := sse
best_N := N
best_K := K
best_D := D
int N_opt = na
int K_opt = na
int D_opt = na
if c_lkb_bool == false
N_opt := best_N
K_opt := best_K
D_opt := best_D
This is the section where the best lookback values are calculated.
We provide the option to use this self optimizer or to use your own lookback values.
Here is an example on the daily AMEX:SPY chart. The top Stochastic is the value with the SSE calculation, the bottom is with a fixed 14, 1, 3 input values. We see in the candles with boxes where some potential differences and trades may be.
This is another comparison of the SSE functionality and the fixed lookbacks on the NYSE:PLTR 1 day chart.
Differences may be more apparent on lower time frame charts.
We encourage traders to not follow indicators blindly, none are 100% accurate. SSE does not guarantee that the values generated will be the best for a given moment in time. Please comment on any desired updates, all criticism is welcome!
Theta Shield | Flux Charts💎 GENERAL OVERVIEW
Introducing our new Theta Shield indicator! Theta is the options risk factor concerning how fast there is a decline in the value of an option over time. This indicator aims to help the trader avoid sideways market phases in the current ticker, to minimize the risk of theta decay. For more information, please check the "How Does It Work" section.
Features of the new Theta Shield Indicator :
Foresight Of Accumulation Zones
Decrease Risk Of Theta Decay
Clear "Valid" & "Non-Valid" Signals
Validness Trail
Alerts
📌 HOW DOES IT WORK ?
In options trading, theta is defined as the rate of decline in the value of an option due to the passage of time. Traders want to avoid this kind of decay in the value of an option. One of the best ways to avoid it is not holding an option contract when the market is going sideways. This indicator uses a stochastic oscillator to try to get a foresight of sideways markets, warning the trader to not hold an option contract while the price is in a range.
The indicator starts by calculating the stochastic value using close, high & low prices of the candlesticks. Then a stoch threshold & a theta length are determined depending on the option contract type defined by the user in the settings of the indicator. Each candlestick that falls above or below the stoch threshold value is counted, and a "theta valid strength" is calculated using the counted candlesticks, which has a value between -100 & 100. Here is the formula of the "theta valid strength" value :
f_lin_interpolate(float x0, float x1, float y0, float y1, float x) =>
y0 + (x - x0) * (y1 - y0) / (x1 - x0)
thetaValid = Total Candlesticks That Fall Above & Below The Threshold In Last "Theta Length" bars.
thetaValidStrength = f_lin_interpolate(0, thetaLength, -100, 100, thetaValid)
Then a trail is rendered, and "Valid" & "Non-Valid" signals are given using this freshly calculated strength value. Valid means that the indicator currently thinks that no accumulation will happen in the near future, so the option positions in the current ticker are protected from the theta decay. Non-Valid means that the indicator thinks the ticker has entered the accumulation phase, so holding any option position is not recommended, as they may be affected by the theta decay.
🚩 UNIQUENESS
This indicator offers a unique way to avoid theta decay in options trading. It uses a stochastic oscillator and thresholds to calculate a "theta strength" value, which is used for rendering validness signals and a trail. Traders can follow the valid & non-valid signals when deciding to hold their options position or not. The indicator also has an alerts feature, so you can get notified when a ticker is about to enter a range, or when it's about to get out of it.
⚙️ SETTINGS
1. General Configuration
Contract Type -> You can set the option contract type here. The indicator will adjust itself to get a better foresight depending on the contract length.
2. Style
Fill Validness -> Will render a trail based on "theta strength" value.
Support line based on RSIThis indicator builds a support line using the stock price and RSI.
Inputs:
1. Time window for the RSI:
the time window the RSI is calculated with, usually it's 14 but in here I recommend 30.
2. offset by percentage:
just adding or subtructing some percentage of the result, some stocks need a bit of offset to work
3. stability:
the higher it is the less the RSI effects the graph. for realy high stability the indicator the the stock price will be realy close.
formula: (close*(100-newRSI)/50)*(100+offset)/100
when:
newRSI = (RSI + (50 * stability1))/(stability+1)
recommended usage:
Usually, if the indicator becomes higher than the price, (the price lowers). the stock will go up again to around the last price where they met.
so, for example, if the stock price was 20 and going down. while the indicator was 18 and going up, then they met at 19 and later the indicator became 20 while the stock fell to 18. most chances are that the stock will come back to 19 where they met and at the same time the indicator will also get to 19.
In stocks that are unstable, like NVDA. this indicator can be used to see the trend and avoid the unstability of the stock.
Risk On/Risk Off Williams %RThe Risk On/Risk Off Williams %R indicator is a technical analysis tool designed to gauge market sentiment by comparing the performance of risk-on and risk-off assets. This indicator combines the Williams %R, a momentum oscillator, with a composite index derived from various financial assets to determine the prevailing market risk sentiment.
Components:
Risk-On Assets: These are typically more volatile and are expected to perform well during bullish market conditions. The indicator uses the following risk-on assets:
SPY (S&P 500 ETF)
QQQ (Nasdaq-100 ETF)
HYG (High-Yield Corporate Bond ETF)
XLF (Financial Select Sector SPDR Fund)
XLK (Technology Select Sector SPDR Fund)
Risk-Off Assets: These are generally considered safer investments and are expected to outperform during bearish market conditions. The indicator includes:
TLT (iShares 20+ Year Treasury Bond ETF)
GLD (SPDR Gold Trust)
DXY (U.S. Dollar Index)
IEF (iShares 7-10 Year Treasury Bond ETF)
XLU (Utilities Select Sector SPDR Fund)
Calculation:
Risk-On Index: The average closing price of the risk-on assets.
Risk-Off Index: The average closing price of the risk-off assets.
The composite index is computed as:
Composite Index=Risk On Index−Risk Off Index
Composite Index=Risk On Index−Risk Off Index
Williams %R: This momentum oscillator measures the current price relative to the high-low range over a specified period. It is calculated as:
\text{Williams %R} = \frac{\text{Highest High} - \text{Composite Index}}{\text{Highest High} - \text{Lowest Low}} \times -100
where "Highest High" and "Lowest Low" are the highest and lowest values of the composite index over the lookback period.
Usage:
Williams %R: A momentum oscillator that ranges from -100 to 0. Values above -50 suggest bullish conditions, while values below -50 indicate bearish conditions.
Background Color: The background color of the chart changes based on the Williams %R relative to a predefined threshold level:
Green background: When Williams %R is above the threshold level, indicating a bullish sentiment.
Red background: When Williams %R is below the threshold level, indicating a bearish sentiment.
Purpose:
The indicator is designed to provide a visual representation of market sentiment by comparing the performance of risk-on versus risk-off assets. It helps traders and investors understand whether the market is leaning towards higher risk (risk-on) or safety (risk-off) based on the relative performance of these asset classes. By incorporating the Williams %R, the indicator adds a momentum-based dimension to this analysis, allowing for better decision-making in response to shifting market conditions.
CNN Fear and Greed Index JD modified from minusminusCNN Fear and Greed Index - www.cnn.com
Modified from minusminus -
See Documentation from CNN's website
CNN's Fear and Greed index is an attempt to quantitatively score the Fear and Greed in the SPX using 7 factors:
Market Momentum- S&P 500 (SPX) and its 125-day moving average
Stock Price Strength -Net new 52-week highs and lows on the NYSE
Stock Price Breadth - McClellan Volume Summation Index
Put and Call options - 5-day average put/call ratio
Market Volatility - VIX and its 50-day moving average
Safe Haven Demand - Difference in 20-day stock and bond returns
Junk Bond Demand - Yield spread: junk bonds vs. investment grade
Each Factor has a weight input for the final calculation initially set to a weight of 1. The final calculation of the index is a weighted average of each factor.
3 Factors have separate functions for calculation : See Code for Clarity
SPX Momentum : difference between the Daily CBOE:SPX index value and it's 125 Day Simple moving average.
Stock Price Strength : Net New 52-week highs and lows on the NYSE.
Function calculates a measure of Net New 52-week highs by:
NYSE 52-week highs (INDEX:MAHN) - all new NYSE Highs (INDEX:HIGH)
measure of Net New 52-week lows by:
NYSE 52-week lows (INDEX:MALN) - all new NYSE Lows (INDEX:LOWN)
Then calculate a ratio of Net New 52-week Highs and Lows over Total Highs and Lows then takes a 5-day moving average of that ratio-See Code
Stock Price Breadth is the McClellan Volume Summation Index :
First Calculate the McClellan Oscillator
Second Calculate the Summation Index
4 Factors are Straight data requests
5 Day Simple Moving Average of the Put-Call Ratio on SPY
50 Day Simple Moving Average of the SPX VIX
Difference between 20 Day Simple Moving Average of SPX Daily Close and 20 Day Simple Moving Average of 10Y Constant Maturity US Treasury Note
Yield Spread between ICE BofA US High Yield Index and ICE BofA US Investment Grade Corporate Yield Index
The Fear and Greed Index is a weighted average of these factors - which is then normalized to scale from 0 to 100 using the past 25 values - length parameter.
3 Zones are Shaded: Red for Extreme Fear, Grey for normal jitters, Green for Extreme Greed.
Disclaimer: This is not financial advice. These are just my ideas, and I am not an investment advisor or investment professional. This code is for informational purposes only and do your own analysis before making any investment decisions. This is an attempt to replicate in spirt an index CNN publishes on their website and in no way shape or form infringes on their content, calculations or proprietary information.
From CNN: www.cnn.com
FEAR & GREED INDEX FAQs
What is the CNN Business Fear & Greed Index?
The Fear & Greed Index is a way to gauge stock market movements and whether stocks are fairly priced. The theory is based on the logic that excessive fear tends to drive down share prices, and too much greed tends to have the opposite effect.
How is Fear & Greed Calculated?
The Fear & Greed Index is a compilation of seven different indicators that measure some aspect of stock market behavior. They are market momentum, stock price strength, stock price breadth, put and call options, junk bond demand, market volatility, and safe haven demand. The index tracks how much these individual indicators deviate from their averages compared to how much they normally diverge. The index gives each indicator equal weighting in calculating a score from 0 to 100, with 100 representing maximum greediness and 0 signaling maximum fear.
How often is the Fear & Greed Index calculated?
Every component and the Index are calculated as soon as new data becomes available.
How to use Fear & Greed Index?
The Fear & Greed Index is used to gauge the mood of the market. Many investors are emotional and reactionary, and fear and greed sentiment indicators can alert investors to their own emotions and biases that can influence their decisions. When combined with fundamentals and other analytical tools, the Index can be a helpful way to assess market sentiment.
Jurik Price Bands and Range Box [BigBeluga]Jurik Price Bands and Range Box
The Jurik Price Bands and Range Box - BigBeluga indicator is an advanced technical analysis tool that combines Jurik Moving Average (JMA) based price bands with a dynamic range box. This versatile indicator is designed to help traders identify trends, potential reversal points, and price ranges over a specified period.
🔵 KEY FEATURES
● Jurik Price Bands
Utilizes Jurik Moving Average for smoother, more responsive bands
//@function Calculates Jurik Moving Average
//@param src (float) Source series
//@param len (int) Length parameter
//@param ph (int) Phase parameter
//@returns (float) Jurik Moving Average value
jma(src, len, ph) =>
var float jma = na
var float e0 = 0.0
var float e1 = 0.0
var float e2 = 0.0
phaseRatio = ph < -100 ? 0.5 : ph > 100 ? 2.5 : ph / 100 + 1.5
beta = 0.45 * (len - 1) / (0.45 * (len - 1) + 2)
alpha = math.pow(beta, phaseRatio)
e0 := (1 - alpha) * src + alpha * nz(e0 )
e1 := (src - e0) * (1 - beta) + beta * nz(e1 )
e2 := (e0 + phaseRatio * e1 - nz(jma )) * math.pow(1 - alpha, 2) + math.pow(alpha, 2) * nz(e2 )
jma := e2 + nz(jma )
jma
Consists of an upper band, lower band, and a smooth price line
Bands adapt to market volatility using Jurik MA on ATR
Helps identify potential trend reversal points and overextended market conditions
● Dynamic Range Box
Displays a box representing the price range over a specified period
Calculates high, low, and mid-range prices
Option for adaptive mid-range calculation based on average price
Provides visual representation of recent price action and volatility
● Price Position Indicator
Shows current price position relative to the mid-range
Displays percentage difference from mid-range
Color-coded for quick trend identification
● Dashboard
Displays key information including current price, range high, mid, and low
Shows trend direction based on price position relative to mid-range
Provides at-a-glance market context
🔵 HOW TO USE
● Trend Identification
Use the middle of the Range Box as the primary trend reference point
Price above the middle of the Range Box indicates an uptrend
Price below the middle of the Range Box indicates a downtrend
The bar on the right shows the percentage distance of the close from the middle of the box
This percentage indicates both trend direction and strength
Refer to the dashboard for quick trend direction confirmation
● Potential Reversal Points
Upper and lower Jurik Bands can indicate potential trend reversal points
Price reaching or exceeding these bands may suggest overextended conditions
Watch for price reaction at these levels for possible trend shifts or pullbacks
Range Box high and low can serve as additional reference points for price action
● Range Analysis
Use Range Box to gauge recent price volatility and trading range
Mid-range line can act as a pivot point for short-term price movements
Percentage difference from mid-range helps quantify price position strength
🔵 CUSTOMIZATION
The Jurik Price Bands and Range Box indicator offers several customization options:
Adjust Range Box length for different timeframe analysis
Toggle between standard and adaptive mid-range calculation
Standard:
Adaptive:
Modify Jurik MA length and deviation for band calculation
Toggle visibility of Jurik Bands
By fine-tuning these settings, traders can adapt the indicator to various market conditions and personal trading strategies.
The Jurik Price Bands and Range Box indicator provides a multi-faceted approach to market analysis, combining trend identification, potential reversal point detection, and range analysis in one comprehensive tool. The use of Jurik Moving Average offers a smoother, more responsive alternative to traditional moving averages, potentially providing more accurate signals.
This indicator can be particularly useful for traders looking to understand market context quickly, identify potential reversal points, and assess current market volatility. The combination of dynamic bands, range analysis, and the informative dashboard provides traders with a rich set of data points to inform their trading decisions.
As with all technical indicators, it's recommended to use the Jurik Price Bands and Range Box in conjunction with other forms of analysis and within the context of a well-defined trading strategy. While this indicator provides valuable insights, it should be considered alongside other factors such as overall market conditions, volume, and fundamental analysis when making trading decisions.
Multi-Step Vegas SuperTrend - strategy [presentTrading]Long time no see! I am back : ) Please allow me to gain some warm-up.
█ Introduction and How it is Different
The "Vegas SuperTrend Strategy" is an enhanced trading strategy that leverages both the Vegas Channel and SuperTrend indicators to generate buy and sell signals.
What sets this strategy apart from others is its dynamic adjustment to market volatility and its multi-step take profit mechanism. Unlike traditional single-step profit-taking approaches, this strategy allows traders to systematically scale out of positions at predefined profit levels, thereby optimizing their risk-reward ratio and maximizing potential gains.
BTCUSD 6hr performance
█ Strategy, How it Works: Detailed Explanation
The Vegas SuperTrend Strategy combines the strengths of the Vegas Channel and SuperTrend indicators to identify market trends and generate trade signals. The following subsections delve into the details of how each component works and how they are integrated.
🔶 Vegas Channel Calculation
The Vegas Channel is based on a simple moving average (SMA) and the standard deviation (STD) of the closing prices over a specified period. The channel is defined by upper and lower bounds that are dynamically adjusted based on market volatility.
Simple Moving Average (SMA):
SMA_vegas = (1/N) * Σ(Close_i) for i = 0 to N-1
where N is the length of the Vegas Window.
Standard Deviation (STD):
STD_vegas = sqrt((1/N) * Σ(Close_i - SMA_vegas)^2) for i = 0 to N-1
Vegas Channel Upper and Lower Bounds:
VegasChannelUpper = SMA_vegas + STD_vegas
VegasChannelLower = SMA_vegas - STD_vegas
The details are here:
🔶 Trend Detection and Trade Signals
The strategy determines the current market trend based on the closing price relative to the SuperTrend bounds:
Market Trend:
MarketTrend = 1 if Close > SuperTrendPrevLower
-1 if Close < SuperTrendPrevUpper
Previous Trend otherwise
Trade signals are generated when there is a shift in the market trend:
Bullish Signal: When the market trend shifts from -1 to 1.
Bearish Signal: When the market trend shifts from 1 to -1.
🔶 Multi-Step Take Profit Mechanism
The strategy incorporates a multi-step take profit mechanism that allows for partial exits at predefined profit levels. This helps in locking in profits gradually and reducing exposure to market reversals.
Take Profit Levels:
The take profit levels are calculated as percentages of the entry price:
TakeProfitLevel_i = EntryPrice * (1 + TakeProfitPercent_i/100) for long positions
TakeProfitLevel_i = EntryPrice * (1 - TakeProfitPercent_i/100) for short positions
Multi-steps take profit local picture:
█ Trade Direction
The trade direction can be customized based on the user's preference:
Long: The strategy only takes long positions.
Short: The strategy only takes short positions.
Both: The strategy can take both long and short positions based on the market trend.
█ Usage
To use the Vegas SuperTrend Strategy, follow these steps:
Configure Input Settings:
- Set the ATR period, Vegas Window length, SuperTrend Multiplier, and Volatility Adjustment Factor.
- Choose the desired trade direction (Long, Short, Both).
- Enable or disable the take profit mechanism and set the take profit percentages and amounts for each step.
█ Default Settings
The default settings of the strategy are designed to provide a balanced approach to trading. Below is an explanation of each setting and its effect on the strategy's performance:
ATR Period (10): This setting determines the length of the ATR used in the SuperTrend calculation. A longer period smoothens the ATR, making the SuperTrend less sensitive to short-term volatility. A shorter period makes the SuperTrend more responsive to recent price movements.
Vegas Window Length (100): This setting defines the period for the Vegas Channel's moving average. A longer window provides a broader view of the market trend, while a shorter window makes the channel more responsive to recent price changes.
SuperTrend Multiplier (5): This base multiplier adjusts the sensitivity of the SuperTrend to the ATR. A higher multiplier makes the SuperTrend less sensitive, reducing the frequency of trade signals. A lower multiplier increases sensitivity, generating more signals.
Volatility Adjustment Factor (5): This factor dynamically adjusts the SuperTrend multiplier based on the width of the Vegas Channel. A higher factor increases the sensitivity of the SuperTrend to changes in market volatility, while a lower factor reduces it.
Take Profit Percentages (3.0%, 6.0%, 12.0%, 21.0%): These settings define the profit levels at which portions of the trade are exited. They help in locking in profits progressively as the trade moves in favor.
Take Profit Amounts (25%, 20%, 10%, 15%): These settings determine the percentage of the position to exit at each take profit level. They are distributed to ensure that significant portions of the trade are closed as the price reaches the set levels, reducing exposure to reversals.
Adjusting these settings can significantly impact the strategy's performance. For instance, increasing the ATR period or the SuperTrend multiplier can reduce the number of trades, potentially improving the win rate but also missing out on some profitable opportunities. Conversely, lowering these values can increase trade frequency, capturing more short-term movements but also increasing the risk of false signals.
Prometheus IQR bandsThis indicator is a tool that uses market data to plot bands along with a price chart.
This tool uses interquartile range (IQR) instead of Standard Deviation (STD) because market returns are not normally distributed. There is also no way to tell if the pocket of the market you are looking at is normally distributed. So using methods that work better with non-normal data minimizes risk more than using a different process.
Calculation
Code for helper functions:
// Function to calculate the percentile value
percentile(arr, p) =>
index = math.floor(p * (array.size(arr) - 1) + 0.5)
array.get(arr, index)
manual_iqr(data, lower_percentile, upper_percentile)=>
// Sort the data
data_arr = array.new()
for i = 0 to lkb_
data_arr.push(close )
array.sort(data_arr)
sorted_data = data_arr.copy()
n = array.size(data_arr)
// Calculate the specified percentiles
Q1 = percentile(sorted_data, lower_percentile)
Q3 = percentile(sorted_data, upper_percentile)
// Calculate IQR
IQR = Q3 - Q1
// Return the IQR
IQR
IQRB(lkb_, sens)=>
sens_l = sens/100
sens_h = (100-sens)/100
val = manual_iqr(close, sens_l, sens_h)
sma = ta.sma(close, int(lkb_))
upper = sma + val
lower = sma - val
Percentile Calculation (percentile function):
Calculates the percentile value of an array (arr) at a given percentile (p).
Uses linear interpolation to find the exact percentile value in a sorted array.
Manual IQR Calculation (manual_iqr function):
Converts the input data into an array (data_arr) and sorts it.
Computes the lower and upper quartiles (Q1 and Q3) using the specified percentiles (lower_percentile and upper_percentile).
Computes the Interquartile Range (IQR) as IQR = Q3 - Q1.
Returns the computed IQR.
IQRB Function Calculation (IQRB function):
Converts the sensitivity percentage (sens) into decimal values (sens_l for lower percentile and sens_h for upper percentile).
Calls manual_iqr with the closing prices (close) and the lower and upper percentiles.
Calculates the Simple Moving Average (SMA) of the closing prices (close) over a specified period (lkb_).
Computes the upper and lower bands of the IQR using the SMA and the calculated IQR (val).
Returns an array containing the upper band, lower band, and SMA values.
After the IQR is calculated at the specified sensitivity it is added to and subtracted from a SMA of the specified period.
This provides us with bands of the IQR sensitivity we want.
Trade Examples
Step 1: Price quickly and strongly breaks below the bottom band and continues there for some bars.
Step 2: Price re-enters the bottom band and has a strong reversal.
Step 1: Price strongly breaks above the top band and continues higher.
Step 2: Price breaks below the top band and reverses to the downside.
Step 3: Price breaks below the bottom band after our previous reversal.
Step 4: Price regains that bottom band and reverses to the upside.
Step 5: Price continues moving higher and does not break above the top band or reverse.
Step 1: Price strongly breaks above the top band and continues higher.
Step 2: Price breaks below the top band and reverses to the downside.
Step 3: Price breaks below the bottom band after our previous reversal.
Step 4: Price regains that bottom band and reverses to the upside.
Step 5: Price strongly breaks above the top band after the previous reversal.
Step 6: Price breaks below the top band and reverses down.
Step 7: Price strongly breaks above the top band and continues moving higher.
Step 8: Price breaks below the top band and reverses down.
Step 9: Price strongly breaks above the top band and continues moving higher.
Step 10: Price breaks below the top band and reverses down.
Step 1: Price breaks above the top band.
Step 2: Price drops below the top band and chops slightly, without a large reversal from that break.
Step 3: Price breaks below the bottom band.
Step 4: Price re-enters the bottom band and just chops, no large reversal.
Step 5: Price breaks below the bottom band.
Step 6: Price retakes the bottom band and strongly reverses.
This tool can be uses to spot reversals and see when trends may continue as the stay inside the bands. No indicator is 100% accurate, we encourage traders to not follow them blindly and use them as tools.
High & Low Of Custom Session - Breakout True Open [cognyto]This indicator is based on the High & Low Of Custom Session - OpeningRange Breakout (Expo) created by Zeiierman.
It adds new functionality and enhances existing settings, targeting ES, NQ, and YM:
Manages session defaults to 12:00 to 13:00
New true opening fully customizable (default 13:00)
Manages timeframe visualization (default 15m and below)
Manages session draw length until the end of the current session (default NY)
Manages previous sessions, allowing the to be hidden
Improves timezone selection (default NY)
Following the strategy called Paradox detailed by DayTradingRauf, it works with indices like ES, NQ, and YM.
The rules consider three possible profiles:
First
AM session as consolidation (08:00-12:00)
Lunch hour range as consolidation (less than 100 points)
PM session breaking either side of the session range
Second
AM session trending lower (08:00-12:00)
Lunch hour range as consolidation (less than 100 points)
PM session trending higher
Third
AM session trending higher (08:00-12:00)
Lunch hour range as consolidation (less than 100 points)
PM session trending lower
After the session ends, the opening price at 13:00 is automatically drawn as it is a key point for the entry strategy.
The strategy can be monitored using a 5-minute or 15-minute timeframe as follows:
- Wait for a liquidity hunt (either the high or low of the lunch session range or AM is taken).
- If liquidity is taken, switch to the 1-minute timeframe and wait for a CISD (change in the state of delivery), where the price closes below an OB, or consider a breaker block or iFVG to enter the trade.
- Bullish entries should happen below the opening price at 13:00, and bearish entries should happen above.
- Consider a 1:2 reward ratio. However, runners can target the opposite side of the range that was not yet taken.
This indicator is for informational purposes only and you should not rely on any information it provides as legal, tax, investment, financial or other advice. Nothing provided by this indicator constitutes a solicitation, recommendation, endorsement or offer by cognyto or any third party service provider to buy or sell any securities or other financial instruments in this or any other jurisdiction in which such solicitation or offer would be unlawful under the securities laws of such jurisdiction.
[SGM Volatility Lvl]Choppiness Index (CI)
The Choppiness Index is a technical analysis tool used to determine whether a market is trending or consolidating. CI values range between 0 and 100:
- Higher values (close to 100) indicate a choppy market (i.e., the market is consolidating and not trending strongly).
- Lower values (close to 0) signify a trending market (either up or down).
In this script:
- CI values above 62 are considered to represent high volatility.
- CI values below 28 are viewed as representing lower volatility or consolidation.
How the Indicator Works
Choppiness Index Calculation
The CI is calculated using the average true range (ATR) and the high-low range over the specified length:
ci = 100 * math.log10(math.sum(ta.atr(1), length_line) / (ta.highest(length_line) - ta.lowest(length_line))) / math.log10(length_line)
Volatility Determination
The script determines the market's volatility state based on CI:
if ci >= 62
ischarge := 2
if ci <= 28
ischarge := 0
- ischarge = 2 indicates high volatility.
- ischarge = 0 indicates consolidation.
Line Setup
Lines are set on the chart based on the market's volatility:
- If CI increases and indicates high volatility, a line (colored with `volcolor`) is drawn at the close price of the bar.
- If CI decreases and indicates consolidation, a line (colored with `conColor`) is drawn at the close price of the bar.
Line Extension
The lines are automatically extended to the next indicator update or bar:
for i = 0 to array.size(ray) - 1
if i < array.size(ray) - 1
current_line = array.get(ray, i)
next_line = array.get(ray, i + 1)
if not na(current_line) and not na(next_line)
line.set_x2(current_line, line.get_x1(next_line))
else
line.set_x2(current_line, bar_index)
Relevance
Identifying Key Levels
The indicator helps traders identify key levels as follows:
- High Volatility : Lines indicating high volatility suggest strong trending movements. These levels can signify breakout points or areas where the price has made significant moves.
- Consolidation : Lines indicating consolidation suggest the market is ranging. These levels can be used to identify sideways movements, areas of accumulation or distribution, and potential breakout zones.
Potential Future Points of Interest
- High Volatility Lines: Can serve as resistance or support levels if the market revisits these areas.
- Consolidation Lines: Highlight potential zones for price breakouts or reversals when the market transitions from consolidation to a trending phase.
In summary, this indicator can be particularly useful for traders looking to identify periods of high volatility and consolidation. By marking such periods on the chart, traders can better understand market behavior and spot potential trading opportunities.
Slow Volume Strength Index (SVSI)The Slow Volume Strength Index (SVSI), introduced by Vitali Apirine in Stocks & Commodities (Volume 33, Chapter 6, Page 28-31), is a momentum oscillator inspired by the Relative Strength Index (RSI). It gauges buying and selling pressure by analyzing the disparity between average volume on up days and down days, relative to the underlying price trend. Positive volume signifies closes above the exponential moving average (EMA), while negative volume indicates closes below. Flat closes register zero volume. The SVSI then applies a smoothing technique to this data and transforms it into an oscillator with values ranging from 0 to 100.
Traders can leverage the SVSI in several ways:
1. Overbought/Oversold Levels: Standard thresholds of 80 and 20 define overbought and oversold zones, respectively.
2. Centerline Crossovers and Divergences: Signals can be generated by the indicator line crossing a midline or by divergences from price movements.
3. Confirmation for Slow RSI: The SVSI can be used to confirm signals generated by the Slow Relative Strength Index (SRSI), another oscillator developed by Apirine.
🔹 Algorithm
In the original article, the SVSI is calculated using the following formula:
SVSI = 100 - (100 / (1 + SVS))
where:
SVS = Average Positive Volume / Average Negative Volume
* Volume is considered positive when the closing price is higher than the six-day EMA.
* Volume is considered negative when the closing price is lower than the six-day EMA.
* Negative volume values are expressed as absolute values (positive).
* If the closing price equals the six-day EMA, volume is considered zero (no change).
* When calculating the average volume, the indicator utilizes Wilder's smoothing technique, as described in his book "New Concepts In Technical Trading Systems."
Note that this indicator, the formula has been simplified to be
SVSI = 100 * Average Positive Volume / (Average Positive Volume + Average Negative Volume)
This formula achieves the same result as the original article's proposal, but in a more concise way and without the need for special handling of division by zero
🔹 Parameters
The SVSI calculation offers configurable parameters that can be adjusted to suit individual trading styles and goals. While the default lookback periods are 6 for the EMA and 14 for volume smoothing, alternative values can be explored. Additionally, the standard overbought and oversold thresholds of 80 and 20 can be adapted to better align with the specific security being analyzed.
RSI, STOCHASTIC RSI AND MFI COMBOCombining the Relative Strength Index (RSI), Stochastic RSI (StochRSI), and Money Flow Index (MFI) can provide traders with a comprehensive approach to analyze market momentum, overbought/oversold conditions, and money flow. Each indicator offers unique insights, and their combination can help confirm trading signals and filter out false signals. Let's delve into each indicator and then discuss how they can be used together:
Relative Strength Index (RSI) 14: DA BLUE LINE
The RSI is a momentum oscillator that measures the speed and change of price movements. It oscillates between 0 and 100 and is typically used to identify overbought (>70) and oversold (<30) conditions. A reading above 70 may indicate that an asset is overbought and could be due for a pullback, while a reading below 30 may suggest that an asset is oversold and could be due for a bounce.
Stochastic RSI (StochRSI) 14: DA RED LINE
The StochRSI is an oscillator that combines the features of both the Stochastic Oscillator and RSI. It measures the relative position of the RSI within its range over a specific period (e.g., 14 periods). Like the RSI, the StochRSI oscillates between 0 and 100 and is used to identify overbought and oversold conditions. Typically:
A StochRSI above 0.8 may suggest overbought conditions.
A StochRSI below 0.2 may indicate oversold conditions.
Money Flow Index (MFI) 14: DA PURPLE LINE
The MFI is a momentum oscillator that measures the inflow and outflow of money into an asset over a specific period (e.g., 14 periods). It oscillates between 0 and 100 and is used to identify overbought and oversold conditions based on both price and volume. Generally:
An MFI above 80 may indicate overbought conditions.
An MFI below 20 may suggest oversold conditions.
Combining RSI, StochRSI, and MFI:
When combining RSI, StochRSI, and MFI, traders can use the following approach to analyze the market:
Identify Overbought/Oversold Conditions:
Look for confluence between RSI, StochRSI, and MFI readings to identify overbought and oversold conditions.
For example, if RSI > 70, StochRSI > 0.8, and MFI > 80, it may suggest a strong overbought condition, potentially indicating a reversal or pullback.
Confirm Trend Strength:
Use the RSI, StochRSI, and MFI to confirm the strength of a trend.
A rising trend with RSI, StochRSI, and MFI above 50 may suggest strong bullish momentum, while a falling trend with readings below 50 may indicate strong bearish momentum.
Divergence Analysis:
Look for divergences between price and RSI, StochRSI, or MFI to identify potential trend reversals.
For example, if the price makes a higher high, but RSI, StochRSI, or MFI makes a lower high (bearish divergence), it may suggest weakening bullish momentum and potential downside.
Combining RSI, StochRSI, and MFI can offer traders a more holistic view of market momentum, overbought/oversold conditions, and money flow. Backtest it let me know your success.
Yeong RRGThe code outlines a trading strategy that leverages Relative Strength (RS) and Rate of Change (RoC) to make trading decisions. Here's a detailed breakdown of the tactic described by the code:
Ticker and Period Selection: The strategy begins by selecting a stock ticker symbol and defining a period (len) for the calculations, which defaults to 14 but can be adjusted by the user.
Stock and Index Data Retrieval: It fetches the closing price (stock_close) of the chosen stock and calculates its 25-period exponential moving average (stock_ema). Additionally, it retrieves the closing price of the S&P 500 Index (index_close), used as a benchmark for calculating Relative Strength.
Relative Strength Calculation: The Relative Strength (rs) is computed by dividing the stock's closing price by the index's closing price, then multiplying by 100 to scale the result. This metric is used to assess the stock's performance relative to the broader market.
Moving RS Ratio and Rate of Change: The strategy calculates a Simple Moving Average (sma) of the RS over the specified period to get the RS Ratio (rs_ratio). It then computes the Rate of Change (roc) of this RS Ratio over the same period to get the RM Ratio (rm_ratio).
Normalization: The RS Ratio and RM Ratio are normalized using a formula that adjusts their values based on the mean and standard deviation of their respective series over the specified window. This normalization process helps in standardizing the indicators, making them easier to interpret and compare.
Indicator Plotting: The normalized RS Ratio (jdk_rs_ratio) and RM Ratio (jdk_rm_ratio) are plotted on the chart with different colors for visual analysis. A horizontal line (hline) at 100 serves as a reference point, indicating a neutral level for the indicators.
State Color Logic: The script includes a logic to determine the state color (statecolor) based on the previous state color and the current values of jdk_rs_ratio and jdk_rm_ratio. This color coding is intended to visually represent different market states: green for bullish, red for bearish, yellow for hold, and blue for watch conditions.
Signal Generation: The strategy generates buy, sell, hold, and watch signals based on the state color and the indicators' values relative to 100. For example, a buy signal is generated when both jdk_rs_ratio and jdk_rm_ratio are above 100, and the background color is set to green to reflect this bullish condition.
Trade Execution: Finally, the strategy executes trades based on the generated signals. A "BUY" trade is entered when a buy signal is present, and it is closed when a sell signal occurs.
Overall, the strategy uses a combination of RS and RoC indicators, normalized for better comparison, to identify potential buy and sell opportunities based on the stock's performance relative to the market and its momentum.
Historical Price Projection [LuxAlgo]The Historical Price Projection tool aims to project future price behavior based on historical price behavior plus a user defined growth factor.
The main feature of this tool is to plot a future price forecast with a surrounding area that exactly matches the price behavior of the selected period, with or without added drift.
Other features of the tool include:
User-selected period up to 500 bars anywhere on the chart within 5000 bars
User selected growth factor from 0 (no growth) to 100, this is the percentage of drift to be used in the forecast.
User selected area wide
Show/hide forecast area
🔶 USAGE
This tool generates a price projection with exactly the same price behavior over the period selected by the user, plus a growth factor .
The user must confirm the selection of the anchor point in order for the tool to be executed; this can be done directly on the chart by clicking on any bar, or via the date field in the settings panel.
As we can see on this chart, the four phases of the market cycle are clearly defined and marked, so we choose the distribution phase as our anchor point because in our analysis, we want to see how the market would behave if we were currently at the same point in the cycle.
In the image above, the growth factor parameter is set to 0 so that the projection matches the selection. The tool will use up to 500 bars after the selection point.
The growth factor is defined as the percentage of drift that the tool will use.
Drift is defined as follows:
For periods with a positive return: average negative return within the period
For negative return periods: average positive return within the period
On the chart above, we have selected the same period but added a growth factor of 10, so that the tool uses a 10% drift in its calculations of future prices.
As the return in the selected period is negative, the added drift will make the projection more bearish than the prices from the selection.
On this chart we have changed the selected period, we have chosen the accumulation phase of the last cycle as the anchor point, again with a growth factor of 10%.
As we can see, prices explode higher, making the projection very bullish, as the added effect of both the bullish selected period and the 10% drift is taken into account.
This last chart is a long-term chart, a quarterly chart of the Dow, and it will serve as a review exercise.
What if... everything goes south and the crash of '29 is repeated?
The answer is in the chart, and it is not for the faint of heart
In this case we have chosen a growth factor of 0 to see exactly the same price behaviour projected into the future.
🔶 SETTINGS
🔹 Data Gathering
Anchor point: Starting point for data collection, up to 500 bars will be used.
🔹 Data Transformation
Growth Factor: Values from 0 to 100, is the amount of drift used to calculate the next price in the series.
Area Width: Values from 0 to 100, controls the width of the area around the forecast as an increment/decrement of the growth factor.
🔹 Style
Price line width: Size of the price line.
Bullish color
Bearish color
Show Area: Show forecast area.
Area color
