Göstergeler ve stratejiler
vika //@version=5
indicator("Auto Anchored Moving Average", "Anchored MA", overlay = true, max_bars_back = 5000, max_lines_count = 500)
// Inputs
auto = input.bool(true, "Enable Auto Anchor")
anchor = input.timeframe('D', 'Anchor Period')
source = input(hlc3, "Source")
showPP = input.bool(true, "Show Prev. Period MA")
highlightAnc = input.bool(true, "Highlight Anchor Change")
wma_show = input.bool(true, "WMA", inline = "WMA", group = "Styles")
sma_show = input.bool(true, "SMA", inline = "SMA", group = "Styles")
vwap_show = input.bool(true, "VWAP", inline = "VWAP", group = "Styles")
wma_style = input.string("──────", "", options = , inline = "WMA", group = "Styles")
sma_style = input.string("──────", "", options = , inline = "SMA", group = "Styles")
vwap_style = input.string("──────", "", options = , inline = "VWAP", group = "Styles")
wma_color = input.color(color.lime, "WMA Color", inline = "WMA", group = "Styles")
sma_color = input.color(color.red, "SMA Color", inline = "SMA", group = "Styles")
vwap_color = input.color(color.blue, "VWAP Color", inline = "VWAP", group = "Styles")
// Determine anchor period automatically
autoAnchor = switch
timeframe.isintraday => timeframe.multiplier <= 15 ? "1D" : "1W"
timeframe.isdaily => "1M"
=> "12M"
// Override auto-anchor if 'auto' is off
if auto
anchor := autoAnchor
// Anchor change detection
isNewAnchor = timeframe.change(anchor)
var length = 1
length := isNewAnchor ? 1 : length + 1
// Calculate MAs
wma = ta.wma(source, length)
sma = ta.sma(source, length)
vwap = nz(ta.vwma(source, length))
// Previous MAs (for prev period plot)
p_wma = ta.valuewhen(isNewAnchor, wma , 0)
p_sma = ta.valuewhen(isNewAnchor, sma , 0)
p_vwap = ta.valuewhen(isNewAnchor, vwap , 0)
// Plot styles
get_style(style) =>
switch style
"──────" => line.style_solid
"─ ─ ─ ─" => line.style_dashed
"· · · · ·" => line.style_dotted
// Draw moving averages
draw(show, ma, _c, style) =>
var line _l = na
var label _lb = na
var label _lbc = na
_l.set_x2(bar_index)
if show and isNewAnchor and showPP
_l := line.new(bar_index - 1, ma , bar_index, ma , color = _c, style = style)
show = not isNewAnchor
plot(show and wma_show ? wma : na, "WMA", wma_color, style=plot.style_linebr)
plot(show and sma_show ? sma : na, "SMA", sma_color, style=plot.style_linebr)
plot(show and vwap_show ? vwap : na, "VWAP", vwap_color, style=plot.style_linebr)
// Highlight anchor change
hlight = isNewAnchor and highlightAnc
plotshape(hlight and wma_show ? wma : na, 'WMA Highlight', shape.circle, location.absolute, color.new(wma_color, 50), -1)
plotshape(hlight and sma_show ? sma : na, 'SMA Highlight', shape.circle, location.absolute, color.new(sma_color, 50), -1)
plotshape(hlight and vwap_show ? vwap : na, 'VWAP Highlight', shape.circle, location.absolute, color.new(vwap_color, 50), -1)
// Draw lines for each moving average
draw(wma_show, wma, wma_color, get_style(wma_style))
draw(sma_show, sma, sma_color, get_style(sma_style))
draw(vwap_show, vwap, vwap_color, get_style(vwap_style))
Super CCI By Baljit AujlaThe indicator you've shared is a custom CCI (Commodity Channel Index) with multiple types of Moving Averages (MA) and Divergence Detection. It is designed to help traders identify trends and reversals by combining the CCI with various MAs and detecting different types of divergences between the price and the CCI.
Key Components of the Indicator:
CCI (Commodity Channel Index):
The CCI is an oscillator that measures the deviation of the price from its average price over a specific period. It helps identify overbought and oversold conditions and the strength of a trend.
The CCI is calculated by subtracting a moving average (SMA) from the price and dividing by the average deviation from the SMA. The CCI values fluctuate above and below a zero centerline.
Multiple Moving Averages (MA):
The indicator allows you to choose from a variety of moving averages to smooth the CCI line and identify trend direction or support/resistance levels. The available types of MAs include:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
HMA (Hull Moving Average)
RMA (Running Moving Average)
SMMA (Smoothed Moving Average)
TEMA (Triple Exponential Moving Average)
DEMA (Double Exponential Moving Average)
VWMA (Volume-Weighted Moving Average)
ZLEMA (Zero-Lag Exponential Moving Average)
You can select the type of MA to use with a specified length to help identify the trend direction or smooth out the CCI.
Divergence Detection:
The indicator includes a divergence detection mechanism to identify potential trend reversals. Divergences occur when the price and an oscillator like the CCI move in opposite directions, signaling a potential change in price momentum.
Four types of divergences are detected:
Bullish Divergence: Occurs when the price makes a lower low, but the CCI makes a higher low. This indicates a potential reversal to the upside.
Bearish Divergence: Occurs when the price makes a higher high, but the CCI makes a lower high. This indicates a potential reversal to the downside.
Hidden Bullish Divergence: Occurs when the price makes a higher low, but the CCI makes a lower low. This suggests a continuation of the uptrend.
Hidden Bearish Divergence: Occurs when the price makes a lower high, but the CCI makes a higher high. This suggests a continuation of the downtrend.
Each type of divergence is marked on the chart with arrows and labels to alert traders to potential trading opportunities. The labels include the divergence type (e.g., "Bull Div" for Bullish Divergence) and have customizable text colors.
Visual Representation:
The CCI and its associated moving average are plotted on the indicator panel below the price chart. The CCI is plotted as a line, and its color changes depending on whether it is above or below the moving average:
Green when the CCI is above the MA (indicating bullish momentum).
Red when the CCI is below the MA (indicating bearish momentum).
Horizontal lines are drawn at specific levels to help identify key CCI thresholds:
200 and -200 levels indicate extreme overbought or oversold conditions.
75 and -75 levels represent less extreme levels of overbought or oversold conditions.
The 0 level acts as a neutral or baseline level.
A background color fill between the 75 and -75 levels helps highlight the neutral zone.
Customization Options:
CCI Length: You can customize the length of the CCI, which determines the period over which the CCI is calculated.
MA Length: The length of the moving average applied to the CCI can also be adjusted.
MA Type: Choose from a variety of moving averages (SMA, EMA, WMA, etc.) to smooth the CCI.
Divergence Detection: The indicator automatically detects the four types of divergences (bullish, bearish, hidden bullish, hidden bearish) and visually marks them on the chart.
How to Use the Indicator:
Trend Identification: When the CCI is above the selected moving average, it suggests bullish momentum. When the CCI is below the moving average, it suggests bearish momentum.
Overbought/Oversold Conditions: The CCI values above 100 or below -100 indicate overbought and oversold conditions, respectively.
Divergence Analysis: The detection of bullish or bearish divergences can signal potential trend reversals. Hidden divergences may suggest trend continuation.
Trading Signals: You can use the divergence markers (arrows and labels) as potential buy or sell signals, depending on whether the divergence is bullish or bearish.
Practical Application:
This indicator is useful for traders who want to:
Combine the CCI with different moving averages for trend-following strategies.
Identify overbought and oversold conditions using the CCI.
Use divergence detection to anticipate potential trend reversals or continuations.
Have a highly customizable tool for various trading strategies, including trend trading, reversal trading, and divergence-based trading.
Overall, this is a comprehensive tool that combines multiple technical analysis techniques (CCI, moving averages, and divergence) in a single indicator, providing traders with a robust way to analyze price action and spot potential trading opportunities.
MM20 & MM50Moyenne Mobile 20
Moyenne Mobile 50
Les deux moyennes mobiles réuni en un script
FX:EURUSD FX:USDJPY FX:GBPUSD OANDA:GBPJPY FX:AUDUSD FX:USDCAD FX:EURJPY
MA 10/50/100/200Four Simple averages combined. This function enable users to use the SMA 10, 50, 100, and 200 in one single formula.
Relative Momentum StrengthThe Relative Momentum Strength (RMS) indicator is designed to help traders and investors identify tokens with the strongest momentum over two customizable timeframes. It calculates and plots the percentage price change over 30-day and 90-day periods (or user-defined periods) to evaluate a token's relative performance.
30-Day Momentum (Green Line): Short-term price momentum, highlighting recent trends and movements.
90-Day Momentum (Blue Line): Medium-term price momentum, providing insights into broader trends.
This tool is ideal for comparing multiple tokens or assets to identify those showing consistent strength or weakness. Use it to spot outperformers and potential reversals in a competitive universe of assets.
How to Use:
Apply this indicator to your TradingView chart for any token or asset.
Look for tokens with consistently high positive momentum for potential strength.
Use the plotted values to compare relative performance across your watchlist.
Customization:
Adjust the momentum periods to suit your trading strategy.
Overlay it with other indicators like RSI or volume for deeper analysis.
Candlestick Strength and Volatility ReadoutDisplays a readout on the top right corner of the screen displaying a two basic calculations (volatility and strength; i.e. candlestick size and how close to the highs or lows it closed) for more convenient candlestick (price action) analysis.
Due to restrictions with Pine Script (or my knowledge thereof) only the current and previous candlestick data is shown, rather than the one currently hovered over.
The data is derived via two simple calculations; volatility being division between the range of the candlestick's high and low by the ATR; 'strength' (what I like to call it) being the range of the body by the range of the open to high or low, depending on the facing direction (positive or negative candlestick). These are expressed as percentages and will turn green depending on the set threshold.
Using this, one can effectively automate calculations you'd have to do by hand otherwise. I personally use these as entry filters in my trading, so it helps to not have to measure, remeasure, and divide before each potential entry.
Settings are implemented to change certain variables to your liking.
Trend Flow Line (TFL)The Trend Flow Line (TFL) is a versatile moving average indicator that dynamically adjusts to trends using a combination of Hull and Weighted Moving Averages, with optional color coding for bullish and bearish trends.
Introduction
The Trend Flow Line (TFL) is a powerful indicator designed to help traders identify and follow market trends with precision. It combines multiple moving average techniques to create a responsive yet smooth trendline. Whether you're a beginner or an experienced trader, the TFL can enhance your chart analysis by highlighting key price movements and trends.
Detailed Description
The Trend Flow Line (TFL) goes beyond traditional moving averages by leveraging a hybrid approach to calculate trends.
Here's how it works:
.........
Combination of Hull and Weighted Moving Averages
The TFL integrates the Hull Moving Average (HMA), known for its fast responsiveness, and the Double Weighted Moving Average (DWMA), which offers smooth transitions.
The HMA is adjusted dynamically based on the user-defined length, ensuring adaptability to various trading styles and timeframes.
.....
Dynamic Smoothing
The TFL calculates its value by averaging the HMA and DWMA, creating a balanced line that responds to market fluctuations without excessive noise.
This balance makes it ideal for identifying both short-term reversals and long-term trends.
.....
Customizable Features
Timeframe: Analyze the indicator on custom timeframes, independent of the chart's current timeframe.
Color Coding: Optional color settings visually differentiate bullish (uptrend) and bearish (downtrend) phases.
Line Width: Adjust the line thickness to suit your chart preferences.
Color Smoothness: Fine-tune how quickly the color changes to reflect trend shifts, providing a visual cue for potential reversals.
The TFL's algorithm ensures a blend of precision and adaptability, making it suitable for any market or trading strategy.
.........
The Trend Flow Line (TFL) is an essential tool for traders looking to stay ahead of market trends while maintaining a clear and visually intuitive charting experience. It combines HMA and DWMA for trend sensitivity and smoothness.
Daily PlayDaily Play Indicator
The Daily Play Indicator is a clean and versatile tool designed to help traders organize and execute their daily trading plan directly on their charts. This indicator simplifies your workflow by visually displaying key inputs like market trend, directional bias, and key levels, making it easier to focus on your trading strategy.
Features
Dropdown Selection for Trend and Bias:
• Set the overall market trend (Bullish, Bearish, or Neutral) and your directional bias (Long, Short, or Neutral) using intuitive dropdown menus. No more manual typing or guesswork!
Key Levels:
Quickly input and display the Previous Day High and Previous Day Low. These levels are essential for many trading strategies, such as breakouts.
Real-Time News Notes:
Add a quick note about impactful news or market events (e.g., “Fed meeting today” or “Earnings season”) to keep contextual awareness while trading.
Simple On-Chart Display:
The indicator creates a “table-like” structure on the chart, aligning your inputs in an easy-to-read format. The data is positioned dynamically so it doesn’t obstruct the price action.
Customisable Visual Style:
Simple labels with clear text to ensure that your chart remains neat and tidy.
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Use Case
The Daily Play Indicator is ideal for:
• Day traders and scalpers who rely on precise planning and real-time execution.
• Swing traders looking to mark critical levels and develop a trade plan before the session begins.
• Anyone who needs a structured way to stay focused and disciplined during volatile market conditions.
By integrating this tool into your workflow, you can easily align your daily preparation with live market action.
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How to Use
Open the indicator settings to configure your inputs:
• Trend: Use the dropdown to choose between Bullish, Bearish, or Neutral.
• Bias: Select Long, Short, or Neutral to align your personal bias with the market.
• Previous Day Levels: Enter the High and Low of the previous trading session for key reference points.
• News: Add a short description of any relevant market-moving events.
Mean Price
^^ Plotting switched to Line.
This method of financial time series (aka bars) downsampling is literally, naturally, and thankfully the best you can do in terms of maximizing info gain. You can finally chill and feed it to your studies & eyes, and probably use nothing else anymore.
(HL2 and occ3 also have use cases, but other aggregation methods? Not really, even if they do, the use cases are ‘very’ specific). Tho in order to understand why, you gotta read the following wall, or just believe me telling you, ‘I put it on my momma’.
The true story about trading volumes and why this is all a big misdirection
Actually, you don’t need to be a quant to get there. All you gotta do is stop blindly following other people’s contextual (at best) solutions, eg OC2 aggregation xD, and start using your own brain to figure things out.
Every individual trade (basically an imprint on 1D price space that emerges when market orders hit the order book) has several features like: price, time, volume, AND direction (Up if a market buy order hits the asks, Down if a market sell order hits the bids). Now, the last two features—volume and direction—can be effectively combined into one (by multiplying volume by 1 or -1), and this is probably how every order matching engine should output data. If we’re not considering size/direction, we’re leaving data behind. Moreover, trades aren’t just one-price dots all the time. One trade can consume liquidity on several levels of the order book, so a single trade can be several ticks big on the price axis.
You may think now that there are no zero-volume ticks. Well, yes and no. It depends on how you design an exchange and whether you allow intra-spread trades/mid-spread trades (now try to Google it). Intra-spread trades could happen if implemented when a matching engine receives both buy and sell orders at the same microsecond period. This way, you can match the orders with each other at a better price for both parties without even hitting the book and consuming liquidity. Also, if orders have different sizes, the remaining part of the bigger order can be sent to the order book. Basically, this type of trade can be treated as an OTC trade, having zero volume because we never actually hit the book—there’s no imprint. Another reason why it makes sense is when we think about volume as an impact or imbalance act, and how the medium (order book in our case) responds to it, providing information. OTC and mid-spread trades are not aggressive sells or buys; they’re neutral ticks, so to say. However huge they are, sometimes many blocks on NYSE, they don’t move the price because there’s no impact on the medium (again, which is the order book)—they’re not providing information.
... Now, we need to aggregate these trades into, let’s say, 1-hour bars (remember that a trade can have either positive or negative volume). We either don’t want to do it, or we don’t have this kind of information. What we can do is take already aggregated OHLC bars and extract all the info from them. Given the market is fractal, bars & trades gotta have the same set of features:
- Highest & lowest ticks (high & low) <- by price;
- First & last ticks (open & close) <- by time;
- Biggest and smallest ticks <- by volume.*
*e.g., in the array ,
2323: biggest trade,
-1212: smallest trade.
Now, in our world, somehow nobody started to care about the biggest and smallest trades and their inclusion in OHLC data, while this is actually natural. It’s the same way as it’s done with high & low and open & close: we choose the minimum and maximum value of a given feature/axis within the aggregation period.
So, we don’t have these 2 values: biggest and smallest ticks. The best we can do is infer them, and given the fact the biggest and smallest ticks can be located with the same probability everywhere, all we can do is predict them in the middle of the bar, both in time and price axes. That’s why you can see two HL2’s in each of the 3 formulas in the code.
So, summed up absolute volumes that you see in almost every trading platform are actually just a derivative metric, something that I call Type 2 time series in my own (proprietary ‘for now’) methods. It doesn’t have much to do with market orders hitting the non-uniform medium (aka order book); it’s more like a statistic. Still wanna use VWAP? Ok, but you gotta understand you’re weighting Type 1 (natural) time series by Type 2 (synthetic) ones.
How to combine all the data in the right way (khmm khhm ‘order’)
Now, since we have 6 values for each bar, let’s see what information we have about them, what we don’t have, and what we can do about it:
- Open and close: we got both when and where (time (order) and price);
- High and low: we got where, but we don’t know when;
- Biggest & smallest trades: we know shit, we infer it the way it was described before.'
By using the location of the close & open prices relative to the high & low prices, we can make educated guesses about whether high or low was made first in a given bar. It’s not perfect, but it’s ultimately all we can do—this is the very last bit of info we can extract from the data we have.
There are 2 methods for inferring volume delta (which I call simply volume) that are presented everywhere, even here on TradingView. Funny thing is, this is actually 2 parts of the 1 method. I wonder how many folks see through it xD. The same method can be used for both inferring volume delta AND making educated guesses whether high or low was made first.
Imagine and/or find the cases on your charts to understand faster:
* Close > open means we have an up bar and probably the volume is positive, and probably high was made later than low.
* Close < open means we have a down bar and probably the volume is negative, and probably low was made later than high.
Now that’s the point when you see that these 2 mentioned methods are actually parts of the 1 method:
If close = open, we still have another clue: distance from open/close pair to high (HC), and distance from open/close pair to low (LC):
* HC < LC, probably high was made later.
* HC > LC, probably low was made later.
And only if close = open and HC = LC, only in this case we have no clue whether high or low was made earlier within a bar. We simply don’t have any more information to even guess. This bar is called a neutral bar.
At this point, we have both time (order) and price info for each of our 6 values. Now, we have to solve another weighted average problem, and that’s it. We’ll weight prices according to the order we’ve guessed. In the neutral bar case, open has a weight of 1, close has a weight of 3, and both high and low have weights of 2 since we can’t infer which one was made first. In all cases, biggest and smallest ticks are modeled with HL2 and weighted like they’re located in the middle of the bar in a time sense.
P.S.: I’ve also included a "robust" method where all the bars are treated like neutral ones. I’ve used it before; obviously, it has lesser info gain -> works a bit worse.
Bostian Intraday Intensity Index (BII)The Bostian Intraday Intensity Index (BII) is a metric used to analyze the trading volume and price movements of a specific stock or asset, measuring the strength and pressure of the market. BII captures buy and sell signals by examining the relationship between trading volume and price fluctuations. Below is an explanation of the key components and calculation method for BII:
○ BII Formula:
sum(V*((C-L)^2-(H-C)^2))/(H-L)))
V (Volume): Trading volume
C (Close): Closing price
L (Low): Lowest price
H (High): Highest price
○ Meaning of the Indicator:
Positive Values: When BII is positive, it indicates strong buying pressure. The closer the closing price is to the high, the stronger the buying pressure.
Negative Values: When BII is negative, it indicates strong selling pressure. The closer the closing price is to the low, the stronger the selling pressure.
○ How to Use:
Buy Signal: When the BII value is positive and trending upwards, it may be considered a buying opportunity.
Sell Signal: When the BII value is negative and trending downwards, it may be considered a selling opportunity.
The BII indicator is useful for analyzing the strength and pressure of the market through the correlation of price movements and trading volume. It helps investors capture buy and sell signals to make better investment decisions.
Stock vs Sector Comparison with HighlightsThis graph is meant as a support to select a stock that is expected to perform better than the sector.
The graph is based on weekly chart. So this is a medium / long term strategy.
How is expected to be used: when the stock has under performed the sector for some time, there is a natural tendence that it will catch up with the sector again. So, for example, if the color change from green to red, you should consider find another stock in the sector. If the stock looses the green color, but is not red yet, you should wait. And vice versa if you start with red. However, life is not that simple, as you can get fake signal. To mitigate this problem, you can adjust the threshold in the input setting, so just go for the signal after x weeks over/underperforming. You also need remember to select the sector in the settings, as the sector is not give automatically when you select the stock.
Below the sectors used:
Sector Name Ticker
S&P 500 (Market Index) SPY
Technology XLK
Financials XLF
Consumer Discretionary XLY
Industrials XLI
Health Care XLV
Consumer Staples XLP
Energy XLE
Utilities XLU
Communication Services XLC
Real Estate XLRE
Materials XLB
Raccoon SupremeStrategy that shows signal for long and short when the RSI goes above a certain level
BTC Pi Cycle Top - Diminishing Version (As seen on ITC)This is an indicator shown on Into The Cryptoverse. Video posted on Nov 25 2024.
The video goes to explain that the Pi Cycle top indicator has successfully called 3 market tops (1 via backtesting, and 2 subsequent in a row). The video goes on to say that there is a chance, due to diminishing returns, that this indicator may not detect the 2024/2025 market top due to this.
This indicator, which is a modified version of the Pi Cycle top, is made using the formula 111 DAY SMA / (350 Day SMA * 2)
The indicator shows that there have been diminishing peaks , and to take this into consideration, as a descending trend can be seen by drawing a trendline across these peaks.
www.youtube.com
Tabela Customizada com EMAs, RSI e RegressãoEste indicador avançado combina análise técnica com um painel visual detalhado, projetado para ajudar traders a tomarem decisões informadas no mercado. Ele inclui:
RSI com Níveis Personalizados:
O indicador calcula o Índice de Força Relativa (RSI) em múltiplos intervalos de tempo (1min, 5min, 15min, 1h, 4h, diário, semanal e mensal).
Exibe os níveis de suporte e resistência do RSI (70 e 30) diretamente no gráfico, além de uma linha média para facilitar a interpretação.
Identifica a tendência com base no RSI, indicando se o mercado está em tendência de alta ou baixa.
Tabela Dinâmica:
Um painel com três colunas que exibe o intervalo de tempo, o valor do RSI e a tendência atual.
Atualiza automaticamente as informações conforme o mercado evolui.
Médias Móveis Exponenciais (EMAs):
Exibe EMAs importantes (9, 12, 21, 50 e 200 períodos), que podem ser ocultadas ou exibidas conforme necessário.
Ajuda a identificar tendências e potenciais pontos de reversão.
Regressão Linear com Desvios:
Uma funcionalidade opcional que calcula e exibe a linha de regressão linear, com limites superiores e inferiores baseados no desvio padrão.
Indica áreas de sobrecompra e sobrevenda, auxiliando na identificação de possíveis reversões.
Customização Visual:
Configurações de cores e transparência para facilitar a leitura e personalização do gráfico.
Este indicador é ideal para traders que buscam uma visão abrangente do mercado, combinando análise de momentum (RSI), tendências (EMAs) e zonas de reversão (Regressão Linear).
Recomendações:
Use-o em conjunto com outros indicadores ou estratégias para validar sinais.
Ative a regressão linear apenas em momentos-chave para evitar poluição visual no gráfico.
Aproveite este indicador como uma ferramenta poderosa para sua análise técnica no TradingView!
Direitos Autorais © 2024 Diego Junior Haefliger
RSI & Bollinger Band Reversal AlertAt the bottom of RSI and Bollinger Bands reversals tend to happen and vice versa. This Indicator is to help identify times for reversals.
Dual EMA with Distance IndicationDual EMA with Distance
This indicator plots two Exponential Moving Averages (EMAs) of your choice directly on the chart and calculates the absolute distance between them. Whether you’re tracking trends or identifying crossover opportunities, this tool gives you added insight into the relationship between the selected EMAs.
Features :
Customizable EMAs : Adjust the length and line width for both EMAs to fit your strategy.
Color & Style Options : Personalize the colors and transparency of each EMA line to match your chart theme.
Distance Display : Enable an optional distance box that dynamically shows the absolute difference between the two EMAs. You can position this box in any corner of the chart and choose your preferred text size, color, and precision (decimal places).
Clean Visualization : The indicator ensures clarity with adjustable transparency and a sleek box design for the distance.
Use this to quickly gauge the strength of a trend or identify potential consolidation when the EMAs are close together. It’s versatile, easy to customize, and a handy addition to any trader’s toolkit.
Have ideas for improving this indicator or creating new ones?
Feel free to DM me @DerLucas432 with suggestions or custom indicator requests!
Swing Trading Strategy with DMI by ~NeikSet sell markers by profit %. This script does not use stop losses, please define them yourself. ideal for taking a small safe piece from any bull trend. Enjoy
RSI Revolucionário Inteligente com Filtro de VolatilidadeTítulo do Indicador
RSI Dinâmico com Filtro Avançado de Volatilidade
Descrição Completa
O RSI Dinâmico com Filtro Avançado de Volatilidade é um indicador inovador projetado para traders que buscam precisão nas decisões de compra e venda, utilizando uma abordagem avançada baseada no Índice de Força Relativa (RSI), volatilidade e análise de volume. Este indicador foi desenvolvido para maximizar a assertividade, evitando sinais em momentos de baixa movimentação e validando as operações em condições de mercado mais confiáveis.
Recursos Principais
Zonas Dinâmicas do RSI:
As zonas de sobrecompra e sobrevenda do RSI são ajustadas automaticamente de acordo com a volatilidade, oferecendo maior adaptabilidade aos movimentos de mercado.
Isso permite detectar reversões mais confiáveis, reduzindo falsos positivos.
Filtro de Volatilidade (ATR):
Um filtro avançado de volatilidade baseado no Average True Range (ATR) garante que os sinais sejam emitidos apenas quando o mercado apresenta movimentação significativa, evitando períodos laterais e incertezas.
Validação de Volume:
O indicador considera o volume em relação à média histórica, permitindo maior confiabilidade nos sinais emitidos em períodos de alta liquidez e interesse dos participantes do mercado.
Direção da Tendência com RSI e Média Móvel:
Combinação de RSI e médias móveis (EMA rápida e lenta) para emitir sinais apenas na direção da tendência predominante.
Sinais de Compra e Venda:
CALL (Compra): Quando o RSI atinge níveis de sobrevenda ajustados pela volatilidade, em tendência de alta com volume elevado.
PUT (Venda): Quando o RSI atinge níveis de sobrecompra ajustados pela volatilidade, em tendência de baixa com volume elevado.
Como Utilizar o Indicador
Tendência e Confirmação:
Use as médias móveis para identificar a tendência geral do mercado.
Realize operações apenas na direção da tendência (CALL em alta e PUT em baixa).
Sinais de Compra e Venda:
As setas verdes indicam sinais de compra (CALL).
As setas vermelhas indicam sinais de venda (PUT).
Parâmetros Ajustáveis:
Período RSI: Controle a sensibilidade do RSI às movimentações de mercado.
Período ATR e Filtro de Volatilidade: Ajuste para mercados mais ou menos voláteis.
Multiplicador de Volume: Controle o nível de validação do volume para sinais.
Configuração Ideal
Timeframe sugerido: 1 minuto (M1).
Ativo recomendado: BTC/USD ou outros ativos voláteis.
Teste e ajuste os parâmetros para melhor performance em diferentes mercados e ativos.
Aviso Importante
Este indicador é uma ferramenta poderosa, mas nenhuma estratégia ou ferramenta garante 100% de assertividade. É essencial realizar backtests, testes em conta demo e utilizar gerenciamento de risco adequado para maximizar os resultados.
Criação e Desenvolvimento
Este indicador foi projetado para oferecer precisão e eficiência ao trader moderno, aproveitando o poder da inteligência artificial e técnicas avançadas de análise de mercado. Experimente e otimize seus resultados no mercado! 🚀
Quick scan for signal🙏🏻 Hey TV, this is QSFS, following:
^^ Quick scan for drift (QSFD)
^^ Quick scan for cycles (QSFC)
As mentioned before, ML trading is all about spotting any kind of non-randomness, and this metric (along with 2 previously posted) gonna help ya'll do it fast. This one will show you whether your time series possibly exhibits mean-reverting / consistent / noisy behavior, that can be later confirmed or denied by more sophisticated tools. This metric is O(n) in windowed mode and O(1) if calculated incrementally on each data update, so you can scan Ks of datasets w/o worrying about melting da ice.
^^ windowed mode
Now the post will be divided into several sections, and a couple of things I guess you’ve never seen or thought about in your life:
1) About Efficiency Ratios posted there on TV;
Some of you might say this is the Efficiency Ratio you’ve seen in Perry's book. Firstly, I can assure you that neither me nor Perry, just as X amount of quants all over the world and who knows who else, would say smth like, "I invented it," lol. This is just a thing you R&D when you need it. Secondly, I invite you (and mods & admin as well) to take a lil glimpse at the following screenshot:
^^ not cool...
So basically, all the Efficiency Ratios that were copypasted to our platform suffer the same bug: dudes don’t know how indexing works in Pine Script. I mean, it’s ok, I been doing the same mistakes as well, but loxx, cmon bro, you... If you guys ever read it, the lines 20 and 22 in da code are dedicated to you xD
2) About the metric;
This supports both moving window mode when Length > 0 and all-data expanding window mode when Length < 1, calculating incrementally from the very first data point in the series: O(n) on history, O(1) on live updates.
Now, why do I SQRT transform the result? This is a natural action since the metric (being a ratio in essence) is bounded between 0 and 1, so it can be modeled with a beta distribution. When you SQRT transform it, it still stays beta (think what happens when you apply a square root to 0.01 or 0.99), but it becomes symmetric around its typical value and starts to follow a bell-shaped curve. This can be easily checked with a normality test or by applying a set of percentiles and seeing the distances between them are almost equal.
Then I noticed that on different moving window sizes, the typical value of the metric seems to slide: higher window sizes lead to lower typical values across the moving windows. Turned out this can be modeled the same way confidence intervals are made. Lines 34 and 35 explain it all, I guess. You can see smth alike on an autocorrelogram. These two match the mean & mean + 1 stdev applied to the metric. This way, we’ve just magically received data to estimate alpha and beta parameters of the beta distribution using the method of moments. Having alpha and beta, we can now estimate everything further. Btw, there’s an alternative parameterization for beta distributions based on data length.
Now what you’ll see next is... u guys actually have no idea how deep and unrealistically minimalistic the underlying math principles are here.
I’m sure I’m not the only one in the universe who figured it out, but the thing is, it’s nowhere online or offline. By calculating higher-order moments & combining them, you can find natural adaptive thresholds that can later be used for anomaly detection/control applications for any data. No hardcoded thresholds, purely data-driven. Imma come back to this in one of the next drops, but the truest ones can already see it in this code. This way we get dem thresholds.
Your main thresholds are: basis, upper, and lower deviations. You can follow the common logic I’ve described in my previous scripts on how to use them. You just register an event when the metric goes higher/lower than a certain threshold based on what you’re looking for. Then you take the time series and confirm a certain behavior you were looking for by using an appropriate stat test. Or just run a certain strategy.
To avoid numerous triggers when the metric jitters around a threshold, you can follow this logic: forget about one threshold if touched, until another threshold is touched.
In general, when the metric gets higher than certain thresholds, like upper deviation, it means the signal is stronger than noise. You confirm it with a more sophisticated tool & run momentum strategies if drift is in place, or volatility strategies if there’s no drift in place. Otherwise, you confirm & run ~ mean-reverting strategies, regardless of whether there’s drift or not. Just don’t operate against the trend—hedge otherwise.
3) Flex;
Extension and limit thresholds based on distribution moments gonna be discussed properly later, but now you can see this:
^^ magic
Look at the thresholds—adaptive and dynamic. Do you see any optimizations? No ML, no DL, closed-form solution, but how? Just a formula based on a couple of variables? Maybe it’s just how the Universe works, but how can you know if you don’t understand how fundamentally numbers 3 and 15 are related to the normal distribution? Hm, why do they always say 3 sigmas but can’t say why? Maybe you can be different and say why?
This is the primordial power of statistical modeling.
4) Thanks;
I really wanna dedicate this to Charlotte de Witte & Marion Di Napoli, and their new track "Sanctum." It really gets you connected to the Source—I had it in my soul when I was doing all this ∞
