PulseHub
Get started

Predictions

HorizonSigma Pro [CHE]HorizonSigma Pro Disclaimer Not every timeframe will yield good results . Very short charts are dominated by microstructure noise, spreads, and slippage; signals can flip and the tradable edge shrinks after costs. Very high timeframes adapt more slowly, provide fewer samples, and can lag regime shifts. When you change timeframe, you also change the ratios between horizon, lookbacks, and correlation windows—what works on M5 won’t automatically hold on H1 or D1. Liquidity, session effects (overnight gaps, news bursts), and volatility do not scale linearly with time. Always validate per symbol and timeframe, then retune horizon, z-length, correlation window, and either the neutral band or the z-threshold. On fast charts, “components” mode adapts quicker; on slower charts, “super” reduces noise. Keep prior-shift and calibration enabled, monitor Hit Rate with its confidence interval and the Brier score, and execute only on confirmed (closed-bar) values. For example, what do “UP 61%” and “DOWN 21%” mean? “UP 61%” is the model’s estimated probability that the close will be higher after your selected horizon—directional probability, not a price target or profit guarantee. “DOWN 21%” still reports the probability of up; here it’s 21%, which implies 79% for down (a short bias). The label switches to “DOWN” because the probability falls below your short threshold. With a neutral-band policy, for example ±7%, signals are: Long above 57%, Short below 43%, Neutral in between. In z-score mode, fixed z-cutoffs drive the call instead of percentages. The arrow length on the chart is an ATR-scaled projection to visualize reach; treat it as guidance, not a promise. Part 1 — Scientific description Objective. The indicator estimates the probability that price will be higher after a user-defined horizon (a chosen number of bars) and emits long, short, or neutral decisions under explicit thresholds. It combines multi‑feature, z‑normalized inputs, adaptive correlation‑based weighting, a prior‑shifted sigmoid mapping, optional rolling probability calibration, and repaint‑safe confirmation. It also visualizes an ATR‑scaled forward projection and prints a compact statistics panel. Data and labeling. For each bar, the target label is whether price increased over the past chosen horizon. Learning is deliberately backward‑looking to avoid look‑ahead: features are associated with outcomes that are only known after that horizon has elapsed. Feature engineering. The feature set includes momentum, RSI, stochastic %K, MACD histogram slope, a normalized EMA(20/50) trend spread, ATR as a share of price, Bollinger Band width, and volume normalized by its moving average. All features are standardized over rolling windows. A compressed “super‑feature” is available that aggregates core trend and momentum components while penalizing excessive width (volatility). Users can switch between a “components” mode (weighted sum of individual features) and a “super” mode (single compressed driver). Weighting and learning. Weights are the rolling correlations between features (evaluated one horizon ago) and realized directional outcomes, smoothed by an EMA and optionally clamped to a bounded range to stabilize outliers. This produces an adaptive, regime‑aware weighting without explicit machine‑learning libraries. Scoring and probability mapping. The raw score is either the weighted component sum or the weighted super‑feature. The score is standardized again and passed through a sigmoid whose steepness is user‑controlled. A “prior shift” moves the sigmoid’s midpoint to the current base rate of up moves, estimated over the evaluation window, so that probabilities remain well‑calibrated when markets drift bullish or bearish. Probabilities and standardized scores are EMA‑smoothed for stability. Decision policy. Two modes are supported: - Neutral band: go long if the probability is above one half plus a user‑set band; go short if it is below one half minus that band; otherwise stay neutral. - Z‑score thresholds: use symmetric positive/negative cutoffs on the standardized score to trigger long/short. Repaint protection. All values used for decisions can be locked to confirmed (closed) bars. Intrabar updates are available as a preview, but confirmed values drive evaluation and stats. Calibration. An optional rolling linear calibration maps past confirmed probabilities to realized outcomes over the evaluation window. The mapping is clipped to the unit interval and can be injected back into the decision logic if desired. This improves reliability (probabilities that “mean what they say”) without necessarily improving raw separability. Evaluation metrics. The table reports: hit rate on signaled bars; a Wilson confidence interval for that hit rate at a chosen confidence level; Brier score as a measure of probability accuracy; counts of long/short trades; average realized return by side; profit factor; net return; and exposure (signal density). All are computed on rolling windows consistent with the learning scheme. Visualization. On the chart, an arrowed projection shows the predicted direction from the current bar to the chosen horizon, with magnitude scaled by ATR (optionally scaled by the square‑root of the horizon). Labels display either the decision probability or the standardized score. Neutral states can display a configurable icon for immediate recognition. Computational properties. The design relies on rolling means, standard deviations, correlations, and EMAs. Per‑bar cost is constant with respect to history length, and memory is constant per tracked series. Graphical objects are updated in place to obey platform limits. Assumptions and limitations. The method is correlation‑based and will adapt after regime changes, not before them. Calibration improves probability reliability but not necessarily ranking power. Intrabar previews are non‑binding and should not be evaluated as historical performance. Part 2 — Trader‑facing description What it does. This tool tells you how likely price is to be higher after your chosen number of bars and converts that into Long / Short / Neutral calls. It learns, in real time, which components—momentum, trend, volatility, breadth, and volume—matter now, adjusts their weights, and shows you a probability line plus a forward arrow scaled by volatility. How to set it up. 1) Choose your horizon. Intraday scalps: 5–10 bars. Swings: 10–30 bars. The default of 14 bars is a balanced starting point. 2) Pick a feature mode. - components: granular and fast to adapt when leadership rotates between signals. - super: cleaner single driver; less noise, slightly slower to react. 3) Decide how signals are triggered. - Neutral band (probability based): intuitive and easy to tune. Widen the band for fewer, higher‑quality trades; tighten to catch more moves. - Z‑score thresholds: consistent numeric cutoffs that ignore base‑rate drift. 4) Keep reliability helpers on. Leave prior shift and calibration enabled to stabilize probabilities across bullish/bearish regimes. 5) Smoothing. A short EMA on the probability or score reduces whipsaws while preserving turns. 6) Overlay. The arrow shows the call and a volatility‑scaled reach for the next horizon. Treat it as guidance, not a promise. Reading the stats table. - Hit Rate with a confidence interval: your recent accuracy with an uncertainty range; trust the range, not only the point. - Brier Score: lower is better; it checks whether a stated “70%” really behaves like 70% over time. - Profit Factor, Net Return, Exposure: quick triage of tradability and signal density. - Average Return by Side: sanity‑check that the long and short calls each pull their weight. Typical adjustments. - Too many trades? Increase the neutral band or raise the z‑threshold. - Missing the move? Tighten the band, or switch to components mode to react faster. - Choppy timeframe? Lengthen the z‑score and correlation windows; keep calibration on. - Volatility regime change? Revisit the ATR multiplier and enable square‑root scaling of horizon. Execution and risk. - Size positions by volatility (ATR‑based sizing works well). - Enter on confirmed values; use intrabar previews only as early signals. - Combine with your market structure (levels, liquidity zones). This model is statistical, not clairvoyant. What it is not. Not a black‑box machine‑learning model. It is transparent, correlation‑weighted technical analysis with strong attention to probability reliability and repaint safety. Suggested defaults (robust starting point). - Horizon 14; components mode; weight EMA 10; correlation window 500; z‑length 200. - Neutral band around seven percentage points, or z‑threshold around one‑third of a standard deviation. - Prior shift ON, Calibration ON, Use calibrated for decisions OFF to start. - ATR multiplier 1.0; square‑root horizon scaling ON; EMA smoothing 3. - Confidence setting equivalent to about 95%. Disclaimer No indicator guarantees profits. HorizonSigma Pro is a decision aid; always combine with solid risk management and your own judgment. Backtest, forward test, and size responsibly. The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context. Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved. By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk. Enhance your trading precision and confidence 🚀 Best regards Chervolino
Indicator
by chervolino
Holt-Winters Forecast BandsDescription: The Holt-Winters Adaptive Bands indicator combines seasonal trend forecasting with adaptive volatility bands. It uses the Holt-Winters triple exponential smoothing model to project future price trends, while Nadaraya-Watson smoothed bands highlight dynamic support and resistance zones. This indicator is ideal for traders seeking to predict future price movements and visualize potential market turning points. By focusing on broader seasonal and trend data, it provides insight into both short- and long-term market directions. It’s particularly effective for swing trading and medium-to-long-term trend analysis on timeframes like daily and 4-hour charts, although it can be adjusted for other timeframes. Key Features: Holt-Winters Forecast Line: The core of this indicator is the Holt-Winters model, which uses three components — level, trend, and seasonality — to project future prices. This model is widely used for time-series forecasting, and in this script, it provides a dynamic forecast line that predicts where price might move based on historical patterns. Adaptive Volatility Bands: The shaded areas around the forecast line are based on Nadaraya-Watson smoothing of historical price data. These bands provide a visual representation of potential support and resistance levels, adapting to recent volatility in the market. The bands' fill colors (red for upper and green for lower) allow traders to identify potential reversal zones without cluttering the chart. Dynamic Confidence Levels: The indicator adapts its forecast based on market volatility, using inputs such as average true range (ATR) and price deviations. This means that in high-volatility conditions, the bands may widen to account for increased price movements, helping traders gauge the current market environment. How to Use: Forecasting: Use the forecast line to gain insight into potential future price direction. This line provides a directional bias, helping traders anticipate whether the price may continue along a trend or reverse. Support and Resistance Zones: The shaded bands act as dynamic support and resistance zones. When price enters the upper (red) band, it may be in an overbought area, while the lower (green) band may indicate oversold conditions. These bands adjust with volatility, so they reflect the current market conditions rather than fixed levels. Timeframe Recommendations: This indicator performs best on daily and 4-hour charts due to its reliance on trend and seasonality. It can be used on lower timeframes, but accuracy may vary due to increased price noise. For traders looking to capture swing trades, the daily and 4-hour timeframes provide a balance of trend stability and signal reliability. Adjustable Settings: Alpha, Beta, and Gamma: These settings control the level, trend, and seasonality components of the forecast. Alpha is generally the most sensitive setting for adjusting responsiveness to recent price movements, while Beta and Gamma help fine-tune the trend and seasonal adjustments. Band Smoothing and Deviation: These settings control the lookback period and width of the volatility bands, allowing users to customize how closely the bands follow price action. Parameters: Prediction Length: Sets the length of the forecast, determining how far into the future the prediction line extends. Season Length: Defines the seasonality cycle. A setting of 14 is typical for bi-weekly cycles, but this can be adjusted based on observed market cycles. Alpha, Beta, Gamma: These parameters adjust the Holt-Winters model's sensitivity to recent prices, trends, and seasonal patterns. Band Smoothing: Determines the smoothing applied to the bands, making them either more reactive or smoother. Ideal Use Cases: Swing Trading and Trend Following: The Holt-Winters model is particularly suited for capturing larger market trends. Use the forecast line to determine trend direction and the bands to gauge support/resistance levels for potential entries or exits. Identifying Reversal Zones: The adaptive bands act as dynamic overbought and oversold zones, giving traders potential reversal areas when price reaches these levels. Important Notes: No Buy/Sell Signals: This indicator does not produce direct buy or sell signals. It’s intended for visual trend analysis and support/resistance identification, leaving trade decisions to the user. Not for High-Frequency Trading: Due to the nature of the Holt-Winters model, this indicator is optimized for higher timeframes like the daily and 4-hour charts. It may not be suitable for high-frequency or scalping strategies on very short timeframes. Adjust for Volatility: If using the indicator on lower timeframes or more volatile assets, consider adjusting the band smoothing and prediction length settings for better responsiveness.
Indicator
by Finntech1
5
The Next Pivot (With History) [Mxwll]Introducing "The Next Pivot (With History)"! With permission from the author @KioseffTrading The script "The Next Pivot" has been restructured to show historical projections! Features Find the most similar price sequence per time frame change. Forecast almost any public indicator! Not just price! Forecast any session i.e. 4Hr, 1Hr, 15m, 1D, 1W Forecast ZigZag for any session Spearmen Pearson Absolute Difference Cosine Similarity Mean Squared Error Kendall Forecasted linear regression channel The image above shows/explains some of the indicator's capabilities! Additionally, you can project almost any indicator! Should load times permit it, the script can search all bar history for a correlating sequence. This won't always be possible, contingent on the forecast length, correlation length, and the number of bars on the chart. If a load time error occurs, simple reduce the "Bars Back To Search" parameter! The script can only draw 500 bars into the future. For whatever time frame you are on and the session you wish to project, ensure it will not exceeded a 500-bar forecast! Reasonable Assessment The script uses various similarity measures to find the "most similar" price sequence to what's currently happening. Once found, the subsequent price move (to the most similar sequence) is recorded and projected forward. So, 1: Script finds most similar price sequence 2: Script takes what happened after and projects forward While this may be useful, the projection is simply the reaction to a possible one-off "similarity" to what's currently happening. Random fluctuations are likely and, if occurring, similarities between the current price sequence and the "most similar" sequence are plausibly coincidental. Thanks!
Indicator
by MXWLL-Capital-Trading
2
Golden Level Predictions v1.0Golden Level Predictions (GLP) Trading Indicator This script introduces a custom trading indicator named "GLP" tailored for the PulseWire platform. It offers various price levels derived from Fibonacci calculations and other mathematical models, assisting traders in pinpointing potential overpriced and discounted price levels. Key Features: User Inputs : Users have the flexibility to select their desired timeframe, with options ranging from Weekly, Daily, Monthly, and more. Additionally, they can opt to showcase Fibonacci lines and the associated prices within these levels. Price Level Calculations : - Employs constants such as the Golden Ratio (PHI) and Pi (PI) to extract various multipliers and factors. - Assesses if the current asset is a cryptocurrency and tweaks calculations accordingly. - Determines overpriced and discounted price levels, drawing from the current open price and past data. Fibonacci Levels : - For each overpriced and discounted level, the script computes intermediary Fibonacci levels, including 23.6%, 38.2%, 50%, 61.8%, and 78.6% (the 3rd level is excluded due to plot limitations). - These levels are illustrated on the chart, granting traders a more detailed view of price targets. Visual Elements : - Projects horizontal lines to the subsequent selected indicator interval for every calculated price level. - Exhibits potential percentage gains or losses at each tier, indicating the prospective price alteration upon reaching that level. - Differentiates overpriced (green) and discounted (red) levels using color codes. A neutral price is depicted in yellow. Anticipated Close Calculation : Offers a projected closing price for the current timeframe, based on a myriad of factors. This indicator is particularly effective with cryptocurrencies due to their inherent volatility. It's also compatible with stocks and is most efficient with tickers that provide volume data.
Indicator
by gggoaaat
15
Bitcoin Market Cap wave model weeklyThis Bitcoin Market Cap wave model indicator is rooted in the foundation of my previously developed tool, the : Bitcoin wave model To derive the Total Market Cap from the Bitcoin wave price model, I employed a straightforward estimation for the Total Market Supply (TMS). This estimation relies on the formula: TMS <= (1 - 2^(-h)) for any h.This equation holds true for any value of h, which will be elaborated upon shortly. It is important to note that this inequality becomes the equality at the dates of halvings, diverging only slightly during other periods. Bitcoin wave model is based on the logarithmic regression model and the sinusoidal waves, induced by the halving events. This chart presents the outcome of an in-depth analysis of the complete set of Bitcoin price data available from October 2009 to August 2023. The central concept is that the logarithm of the Bitcoin price closely adheres to the logarithmic regression model. If we plot the logarithm of the price against the logarithm of time, it forms a nearly straight line. The parameters of this model are provided in the script as follows: log(BTCUSD) = 1.48 + 5.44log(h). The secondary concept involves employing the inherent time unit of Bitcoin instead of days: 'h' denotes a slightly adjusted time measurement intrinsic to the Bitcoin blockchain. It can be approximated as (days since the genesis block) * 0.0007. Precisely, 'h' is defined as follows: h = 0 at the genesis block, h = 1 at the first halving block, and so forth. In general, h = block height / 210,000. Adjustments are made to account for variations in block creation time. The third concept revolves around investigating halving waves triggered by supply shock events resulting from the halvings. These halvings occur at regular intervals in Bitcoin's native time 'h'. All halvings transpire when 'h' is an integer. These events induce waves with intervals denoted as h = 1. Consequently, we can model these waves using a sin(2pih - a) function. The parameter determining the time shift is assessed as 'a = 0.4', aligning with earlier expectations for halving events and their subsequent outcomes. The fourth concept introduces the notion that the waves gradually diminish in amplitude over the progression of "time h," diminishing at a rate of 0.7^h. Lastly, we can create bands around the modeled sinusoidal waves. The upper band is derived by multiplying the sine wave by a factor of 3.1*(1-0.16)^h, while the lower band is obtained by dividing the sine wave by the same factor, 3.1*(1-0.16)^h. The current bandwidth is 2.5x. That means that the upper band is 2.5 times the lower band. These bands are forming an exceptionally narrow predictive channel for Bitcoin. Consequently, a highly accurate estimation of the peak of the next cycle can be derived. The prediction indicates that the zenith past the fourth halving, expected around the summer of 2025, could result in Total Bitcoin Market Cap ranging between 4B and 5B USD. The projections to the future works well only for weekly timeframe. Enjoy the mathematical insights!
Indicator
by leoum
2
The Next Pivot [Kioseff Trading]Hello! This script "The Next Pivot" uses various similarity measures to compare historical price sequences to the current price sequence! Features Find the most similar price sequence up to 100 bars from the current bar Forecast price path up to 250 bars Forecast ZigZag up to 250 bars Spearmen Pearson Absolute Difference Cosine Similarity Mean Squared Error Kendall Forecasted linear regression channel The image above shows/explains some of the indicator's capabilities! The image above highlights the projected zig zag (pivots) pattern! Colors are customizable (: Additionally, you can plot a forecasted LinReg channel. Should load times permit it, the script can search all bar history for a correlating sequence. This won't always be possible, contingent on the forecast length, correlation length, and the number of bars on the chart. Reasonable Assessment The script uses various similarity measures to find the "most similar" price sequence to what's currently happening. Once found, the subsequent price move (to the most similar sequence) is recorded and projected forward. So, 1: Script finds most similar price sequence 2: Script takes what happened after and projects forward While this may be useful, the projection is simply the reaction to a possible one-off "similarity" to what's currently happening. Random fluctuations are likely and, if occurring, similarities between the current price sequence and the "most similar" sequence are plausibly coincidental. That said, if you have any ideas on cool features to add please let me know! Thank you (:
Indicator
by KioseffTrading
80
Monte Carlo Price ProbabilitiesMonte Carlo simulations have been a popular tool in the world of finance, risk analysis, and decision making for decades. In this post, I will take you through the history of Monte Carlo simulations and explain how I implemented this powerful technique in Pine Script. This implementation can help traders and investors in various time frames to better understand the potential future price movements of financial instruments based on historical data. History of Monte Carlo Simulations The Monte Carlo method was named after the famous Monte Carlo Casino in Monaco, as the technique involves using random sampling to approximate solutions to mathematical problems. The method was first introduced by Stanislaw Ulam, a mathematician working on the Manhattan Project in the 1940s. Ulam realized that using random sampling could provide approximate solutions to complex problems that were otherwise difficult or impossible to solve analytically. Over the years, Monte Carlo simulations have found applications in various fields, including physics, engineering, and finance. In the world of finance, the method has been used to model stock price movements, option pricing, portfolio optimization, and risk management. Implementation in Pine In my implementation of Monte Carlo simulations in Pine, I created a script that allows users to input several parameters such as the arbitrary price, number of simulations, number of steps into the future, and the start bar index. The start bar index is a crucial setting for running the script on lower time frames, as it helps to ensure that the script runs smoothly for a given symbol. The script then calculates the log return of each bar and categorizes them into green (positive) or red (negative) moves. It uses these historical price movements to calculate the probabilities of future price movements for each step in the simulation. The core of the Monte Carlo simulation lies in the `monte()` function, which generates random numbers to determine if the next price movement will be green or red, and then selects a move size based on its probability. The `sim()` function runs multiple simulations using the `monte()` function and stores the results in an array. Finally, the script calculates the probability of the arbitrary price being reached in the future based on the results of the simulations. It also plots the probability on the chart, allowing users to visually assess the potential future price movements of the financial instrument. Using the Monte Carlo Simulation To use the Monte Carlo simulation in Pine, you need to input the desired parameters such as the arbitrary price, number of simulations, number of steps into the future, and the start bar index. For some symbols, you may need to set the start bar index to around 10k to ensure that the script runs smoothly. Once you have input the parameters and run the script, you will see the probability of reaching the arbitrary price plotted on the chart. This can provide a valuable insight into the potential future price movements of the financial instrument based on historical data, helping you make more informed trading and investment decisions. Conclusion Monte Carlo simulations have a rich history and have proven to be a valuable tool in various fields, including finance. My implementation of Monte Carlo simulations in Pine allows traders and investors to better understand the potential future price movements of financial instruments in various time frames. By evaluating the probabilities of reaching specific price levels, users can make more informed decisions and better manage their risk.
Indicator
by The_Peaceful_Lizard
4
Linear Regress on Price And VolumeLinear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables. It assumes a linear relationship between the dependent variable and the independent variable(s) and attempts to fit a straight line that best describes the relationship. In the context of predicting the price of a stock based on the volume, we can use linear regression to build a model that relates the price of the stock (dependent variable) to the volume (independent variable). The idea is to use lookback period to predict future prices based on the volume. To build this indicator, we start by collecting data on the price of the stock and the volume over a selected of time or by default 21 days. We then plot the data on a scatter plot with the volume on the x-axis and the price on the y-axis. If there is a clear pattern in the data, we can fit a straight line to the data using a method called least squares regression. The line represents the best linear approximation of the relationship between the price and the volume. Once we have the line, we can use it to make predictions. For example, if we observe a certain volume, we can use the line to estimate the corresponding price. It's worth noting that linear regression assumes a linear relationship between the variables. In reality, the relationship between the price and the volume may be more complex, and other factors may also influence the price of the stock. Therefore, while linear regression can be a useful tool, it should be used in conjunction with other methods and should be interpreted with caution.
Indicator
by stocktechbot
Leavitt Convolution [CC]The Leavitt Convolution indicator was created by Jay Leavitt (Stocks and Commodities Oct 2019, page 11), who is most well known for creating the Volume-Weighted Average Price indicator. This indicator is very similar to my Leavitt Projection script and I forgot to mention that both of these indicators are actually predictive moving averages. The Leavitt Convolution indicator doubles down on this idea by creating a prediction of the Leavitt Projection which is another prediction for the next bar. Obviously this means that it isn't always correct in its predictions but it does a very good job at predicting big trend changes before they happen. The recommended strategy for how to trade with these indicators is to plot a fast version and a slow version and go long when the fast version crosses over the slow version or to go short when the fast version crosses under the slow version. I have color coded the lines to turn light green for a normal buy signal or dark green for a strong buy signal and light red for a normal sell signal, and dark red for a strong sell signal. This is another indicator in a series that I'm publishing to fulfill a special request from @ashok1961 so let me know if you ever have any special requests for me.
Indicator
by cheatcountry
8
Niteya Multi Ticker Dollar-Based Pricing Ver 1.3The main purpose of the indicator is to make a future price estimation based on the highest dollar-based price of the stock in the past, especially for stocks that exceed their past prices in chart currency terms. There should be no expectation that this prediction will necessarily come true. A table with six columns and 19 rows (excluding the header) is created on the graph, positioned bottom and left. The first column contains the ticker code, the second column contains the highest historical price of the stock in currency, the third column contains the past high price of the stock in USD, the fourth column contains the closing price, the fifth column contains the value obtained by multiplying the past highest USD price of the stock by the daily dollar price, and the sixth column is includes the rate of increase. Using the indicator interface, you can select the ticker value in the first row of the table from among 22 different values via a selection box, and for the 18 rows below, you can directly type the ticker name. * The currency of the chart must be compatible with the dollar conversion currency. For example, if the conversion currency is "USDTRY", the currency of the chart should be "TRY". All stocks in the indicator are randomly selected. Investment information, stock selections, comments and recommendations herein are not within the scope of investment consultancy. Investment consultancy service is provided within the framework of investment consultancy agreement to be signed between brokerage houses, portfolio management companies, non-deposit banks and the customer. Türkçe açıklama Göstergenin temel amacı, özellikle grafik para birimi (TRY) bazında geçmiş fiyatlarının üzerine çıkmış hisselerde, hissenin geçmişteki en yüksek dolar bazlı fiyatını esas alarak, geleceğe yönelik bir fiyat tahmininde bulunmaktır. Bu tahminin mutlaka gerçekleşeceği beklentisi olmamalıdır. Grafik üzerinde, üste ve ortalanmış olarak, altı sütun ve başlık kısmı hariç 19 satırlık bir tablo oluşturulmaktadır. İlk sütun hisse kodunu, ikinci sütun hissenin geçmiş en yüksek fiyatını TRY olarak, üçüncü sütun hissenin geçmiş en yüksek fiyatını USD olarak, dördüncü sütun kapanış fiyatını, beşinci sütun hissenin geçmiş en yüksek USD fiyatının günlük dolar kuru ile çarpılarak elde edilen değeri, altıncı sütun ise artış oranını içerir. Gösterge arayüzünü kullanarak, tablonun ilk satırındaki ticker (hisse) değerini 22 farklı değer arasından (BIST 100 ve 21 şirket) bir seçim kutusu yoluyla, altta yer alan 18 satır için ise, doğrudan hisse adını yazabilirsiniz. * Grafiğin para birimi dolar çevrim kuru ile uyumlu olmalıdır. Örneğin, çevrim kuru "USDTRY" ise, grafiğin para birimi "TRY" olmalıdır. Gösterge içinde yer alan tüm hisseler rastgele seçilmiştir. Buradaki yatırım bilgileri, hisse seçimleri, yorum ve tavsiyeleri yatırım danışmanlığı kapsamında değildir. Yatırım danışmanlığı hizmeti, aracı kurumlar, portföy yönetim şirketleri, mevduat kabul etmeyen bankalar ile müşteri arasında imzalanacak yatırım danışmanlığı sözleşmesi çerçevesinde sunulmaktadır.
Indicator
by Niteya
4
RK's 07 ∴ Moving Average Ribbon with Momentum Adjusted by DGTHello folks! In my search for new ways to get faster and better market responses, I found this brilliant Indicator here on Trading View. I rewrite all the code with my own functions and styles. So... This is my adaptation to excellent script "Momentum adjusted Moving Average by DGT" from the user dgtrd In dgtrd's words: "A brand new Moving Average, calculated using Momentum, Acceleration and Probability (Psychological Effect). Momentum adjusted Moving Average( MaMA ) is an indicator that measures Price Action by taking into consideration not only Price movements but also its Momentum, Acceleration and Probability. MaMA , provides faster responses comparing to the regular Moving Average" The original post is here: 👇 T∴F∴A∴ Rodrigo Kazuma
Indicator
by RodrigoKazuma
10
Magic 8-Ball [QuantNomad]Sometimes get tired and what to create something fun and useless ) Here I developed a magic 8-ball. You can apply it to the chart, and it randomly will show you a prediction unique for your symbol/candle time. Please don't take this prediction seriously; there is 0 rationale behind it. However, I believe it can outperform some traders here on TradginView =) So it will show you one of the following messages: Buy Strong Buy HODL Sell Strong Sell Ask again later Better not tell you now Neutral Cannot predict now Very doubtful
Indicator
by QuantNomad
24
Real-time market data and financial news In-depth global business and economic analysis Live stock quotes and portfolio tools
Link Terkait
Live stock quotes and portfolio tools
Comprehensive trading education and market insights