High/Low Location Frequency [LuxAlgo]The High/Low Location Frequency tool provides users with probabilities of tops and bottoms at user-defined periods, along with advanced filters that offer deep and objective market information about the likelihood of a top or bottom in the market.
🔶 USAGE
There are four different time periods that traders can select for analysis of probabilities:
HOUR OF DAY: Probability of occurrence of top and bottom prices for each hour of the day
DAY OF WEEK: Probability of occurrence of top and bottom prices for each day of the week
DAY OF MONTH: Probability of occurrence of top and bottom prices for each day of the month
MONTH OF YEAR: Probability of occurrence of top and bottom prices for each month
The data is displayed as a dashboard, which users can position according to their preferences. The dashboard includes useful information in the header, such as the number of periods and the date from which the data is gathered. Additionally, users can enable active filters to customize their view. The probabilities are displayed in one, two, or three columns, depending on the number of elements.
🔹 Advanced Filters
Advanced Filters allow traders to exclude specific data from the results. They can choose to use none or all filters simultaneously, inputting a list of numbers separated by spaces or commas. However, it is not possible to use both separators on the same filter.
The tool is equipped with five advanced filters:
HOURS OF DAY: The permitted range is from 0 to 23.
DAYS OF WEEK: The permitted range is from 1 to 7.
DAYS OF MONTH: The permitted range is from 1 to 31.
MONTHS: The permitted range is from 1 to 12.
YEARS: The permitted range is from 1000 to 2999.
It should be noted that the DAYS OF WEEK advanced filter has been designed for use with tickers that trade every day, such as those trading in the crypto market. In such cases, the numbers displayed will range from 1 (Sunday) to 7 (Saturday). Conversely, for tickers that do not trade over the weekend, the numbers will range from 1 (Monday) to 5 (Friday).
To illustrate the application of this filter, we will exclude results for Mondays and Tuesdays, the first five days of each month, January and February, and the years 2020, 2021, and 2022. Let us review the results:
DAYS OF WEEK: `2,3` or `2 3` (for crypto) or `1,2` or `1 2` (for the rest)
DAYS OF MONTH: `1,2,3,4,5` or `1 2 3 4 5`
MONTHS: `1,2` or `1 2`
YEARS: `2020,2021,2022` or `2020 2021 2022`
🔹 High Probability Lines
The tool enables traders to identify the next period with the highest probability of a top (red) and/or bottom (green) on the chart, marked with two horizontal lines indicating the location of these periods.
🔹 Top/Bottom Labels and Periods Highlight
The tool is capable of indicating on the chart the upper and lower limits of each selected period, as well as the commencement of each new period, thus providing traders with a convenient reference point.
🔶 SETTINGS
Period: Select how many bars (hours, days, or months) will be used to gather data from, max value as default.
Execution Window: Select how many bars (hours, days, or months) will be used to gather data from
🔹 Advanced Filters
Hours of day: Filter which hours of the day are excluded from the data, it accepts a list of hours from 0 to 23 separated by commas or spaces, users can not mix commas or spaces as a separator, must choose one
Days of week: Filter which days of the week are excluded from the data, it accepts a list of days from 1 to 5 for tickers not trading weekends, or from 1 to 7 for tickers trading all week, users can choose between commas or spaces as a separator, but can not mix them on the same filter.
Days of month: Filter which days of the month are excluded from the data, it accepts a list of days from 1 to 31, users can choose between commas or spaces as separator, but can not mix them on the same filter.
Months: Filter months to exclude from data. Accepts months from 1 to 12. Choose one separator: comma or space.
Years: Filter years to exclude from data. Accepts years from 1000 to 2999. Choose one separator: comma or space.
🔹 Dashboard
Dashboard Location: Select both the vertical and horizontal parameters for the desired location of the dashboard.
Dashboard Size: Select size for dashboard.
🔹 Style
High Probability Top Line: Enable/disable `High Probability Top` vertical line and choose color
High Probability Bottom Line: Enable/disable `High Probability Bottom` vertical line and choose color
Top Label: Enable/disable period top labels, choose color and size.
Bottom Label: Enable/disable period bottom labels, choose color and size.
Highlight Period Changes: Enable/disable vertical highlight at start of period
在腳本中搜尋"2021年黄金价格走势"
Interest Rate Trading (Manually Added Rate Decisions) [TANHEF]Interest Rate Trading: How Interest Rates Can Guide Your Next Move.
How were interest rate decisions added?
All interest rate decision dates were manually retrieved from the 'Record of Policy Actions' and 'Minutes of Actions' on the Federal Reserve's website due to inconsistent dates from other sources. These were manually added as Pine Script currently only identifies rate changes, not pauses.
█ Simple Explanation:
This script is designed for analyzing and backtesting trading strategies based on U.S. interest rate decisions which occur during Federal Open Market Committee (FOMC) meetings, to make trading decisions. No trading strategy is perfect, and it's important to understand that expectations won't always play out. The script leverages historical interest rate changes, including increases, decreases, and pauses, across multiple economic time periods from 1971 to the present. The tool integrates two key data sources for interest rates—USINTR and FEDFUNDS—to support decision-making around rate-based trades. The focus is on identifying opportunities and tracking trades driven by interest rate movements.
█ Interest Rate Decision Sources:
As noted above, each decision date has been manually added from the 'Record of Policy Actions' and 'Minutes of Actions' documents on the Federal Reserve's website. This includes +50 years of more than 600 rate decisions.
█ Interest Rate Data Sources:
USINTR: Reflects broader U.S. interest rate trends, including Treasury yields and various benchmarks. This is the preferred option as it corresponds well to the rate decision dates.
FEDFUNDS: Tracks the Federal Funds Rate, which is a more specific rate targeted by the Federal Reserve. This does not change on the exact same days as the rate decisions that occur at FOMC meetings.
█ Trade Criteria:
A variety of trading conditions are predefined to suit different trading strategies. These conditions include:
Increase/Decrease: Standard rate increases or decreases.
Double/Triple Increase/Decrease: A series of consecutive changes.
Aggressive Increase/Decrease: Rate changes that exceed recent movements.
Pause: Identification of no changes (pauses) between rate decisions, including double or triple pauses.
Complex Patterns: Combinations of pauses, increases, or decreases, such as "Pause after Increase" or "Pause or Increase."
█ Trade Execution and Exit:
The script allows automated trade execution based on selected criteria:
Auto-Entry: Option to enter trades automatically at the first valid period.
Max Trade Duration: Optional exit of trades after a specified number of bars (candles).
Pause Days: Minimum duration (in days) to validate rate pauses as entry conditions. This is especially useful for earlier periods (prior to the 2000s), where rate decisions often seemed random compared to the consistency we see today.
█ Visualization:
Several visual elements enhance the backtesting experience:
Time Period Highlighting: Economic time periods are visually segmented on the chart, each with a unique color. These periods include historical phases such as "Stagflation (1971-1982)" and "Post-Pandemic Recovery (2021-Present)".
Trade and Holding Results: Displays the profit and loss of trades and holding results directly on the chart.
Interest Rate Plot: Plots the interest rate movements on the chart, allowing for real-time tracking of rate changes.
Trade Status: Highlights active long or short positions on the chart.
█ Statistics and Criteria Display:
Stats Table: Summarizes trade results, including wins, losses, and draw percentages for both long and short trades.
Criteria Table: Lists the selected entry and exit criteria for both long and short positions.
█ Economic Time Periods:
The script organizes interest rate decisions into well-defined economic periods, allowing traders to backtest strategies specific to historical contexts like:
(1971-1982) Stagflation
(1983-1990) Reaganomics and Deregulation
(1991-1994) Early 1990s (Recession and Recovery)
(1995-2001) Dot-Com Bubble
(2001-2006) Housing Boom
(2007-2009) Global Financial Crisis
(2009-2015) Great Recession Recovery
(2015-2019) Normalization Period
(2019-2021) COVID-19 Pandemic
(2021-Present) Post-Pandemic Recovery
█ User-Configurable Inputs:
Rate Source Selection: Choose between USINTR or FEDFUNDS as the primary interest rate source.
Trade Criteria Customization: Users can select the criteria for long and short trades, specifying when to enter or exit based on changes in the interest rate.
Time Period: Select the time period that you want to isolate testing a strategy with.
Auto-Entry and Pause Settings: Options to automatically enter trades and specify the number of days to confirm a rate pause.
Max Trade Duration: Limits how long trades can remain open, defined by the number of bars.
█ Trade Logic:
The script manages entries and exits for both long and short trades. It calculates the profit or loss percentage based on the entry and exit prices. The script tracks ongoing trades, dynamically updating the profit or loss as price changes.
█ Examples:
One of the most popular opinions is that when rate starts begin you should sell, then buy back in when rate cuts stop dropping. However, this can be easily proven to be a difficult task. Predicting the end of a rate cut is very difficult to do with the the exception that assumes rates will not fall below 0.25%.
2001-2009
Trade Result: +29.85%
Holding Result: -27.74%
1971-2024
Trade Result: +533%
Holding Result: +5901%
█ Backtest and Real-Time Use:
This backtester is useful for historical analysis and real-time trading. By setting up various entry and exit rules tied to interest rate movements, traders can test and refine strategies based on real historical data and rate decision trends.
This powerful tool allows traders to customize strategies, backtest them through different economic periods, and get visual feedback on their trading performance, helping to make more informed decisions based on interest rate dynamics. The main goal of this indicator is to challenge the belief that future events must mirror the 2001 and 2007 rate cuts. If everyone expects something to happen, it usually doesn’t.
[blackcat] L1 Vitali Apirine MABWLevel 1
Background
Vitali Apirine’s articles in the July & August issues on 2021, “Moving Average Band Width”
Function
In “Moving Average Bands” (part 1, July 2021 issue) and “Moving Average Band Width” (part 2, August 2021 issue), author Vitali Apirine explains how moving average bands (MAB) can be used as a trend-following indicator by displaying the movement of a shorter-term moving average in relation to the movement of a longer-term moving average. The distance between the bands will widen as volatility increases and will narrow as volatility decreases. In part 2, the moving average band width (MABW) measures the percentage difference between the bands. Changes in this difference may indicate a forthcoming move or change in the trend.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
[blackcat] L1 Vitali Apirine MABLevel 1
Background
Vitali Apirine’s articles in the July & August issues on 2021, “Moving Average Bands”
Function
In “Moving Average Bands” (part 1, July 2021 issue) and “Moving Average Band Width” (part 2, August 2021 issue), author Vitali Apirine explains how moving average bands (MAB) can be used as a trend-following indicator by displaying the movement of a shorter-term moving average in relation to the movement of a longer-term moving average. The distance between the bands will widen as volatility increases and will narrow as volatility decreases.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
Trojan Cycle: Dip & Profit Hunter📉 Crypto is changing. Your signals should too.
This script doesn’t try to outguess price — it helps you track capital rotation and flow behavior in alignment with the evolving macro structure of the digital asset market.
Trojan Cycle: Dip & Profit Hunter is a signal engine built to support and validate the capital rotation models outlined in the Trojan Cycle and Synthetic Rotation theses — available via RWCS_LTD’s published charts
It is not a classic “buy low, sell high” tool. It is a structural filter that uses price/volume statistics to surface accumulation zones, synthetic traps, and macro context shifts — all aligned with the institutionalization of crypto post-2024.
🧠 Purpose & Value
Crypto no longer follows the retail-led, halving-driven pattern of 2017 or 2021.
Instead, institutional infrastructure, regulatory filters, and equity-market Trojan horses define the new path of capital.
This tool helps you visualize that path by interpreting behavior through statistical imbalances and real-time momentum signals.
Use it to:
Track where capital is accumulating or exiting
Identify signals consistent with true cycle rotation (vs. synthetic traps)
Validate your macro view with real-time statistical context
🔍 How It Works
The engine combines four signal layers:
1. Z-Score Logic
- Measures how far price and volume have deviated from their mean
- Detects dips, blowoffs, and exhaustion zones
2. Percentile Logic
- Compares current price and volume to historical rank distribution
- Flags statistically rare conditions (e.g. bottom 10% price, top 90% volume)
3. Combined Context Engine
- Integrates both models to generate one of 36 unique output states
- Each state provides a labeled market context (e.g., 🟢 Confluent Buy, 🔴 Confluent Sell, 🧨 Synthetic Trap )
4. Momentum Spread & Divergence
- Measures whether price is leading volume (trap risk) or volume is leading price (accumulation)
- Outputs intuitive momentum context with emoji-coded alerts
📋 What You See
🧠 Contextual Table UI with key Z-Scores, percentiles, signals, and market commentary
🎯 Emoji-coded signals to quickly grasp high-probability setups or risk zones
🌊 Optional overlays: price/volume divergence, momentum spread
🎨 Visual table customization (size, position) and chart highlights for signal clarity
🔔 Alert System
✅ Single dynamic alert using alert() that only fires when signal context changes
Prevents alert fatigue and allows clean webhook/automation integration
🧭 Use Cases
For macro cycle traders: Track where we are in the Trojan Cycle using statistical context
For thesis explorers: Use the 36-output signal map to match against your rotation thesis
For capital rotation watchers: Identify structural setups consistent with ETF-driven or compliance-filtered flow
For narrative skeptics: Avoid synthetic altseason traps where volume lags or flow dries up
🧪 Suggested Pairing for Thesis Validation
To use this tool as part of a thesis-confirmation framework , pair it with:
BTC.D — Bitcoin Dominance
ETH/BTC — Ethereum strength vs. Bitcoin
TOTALE100/ETH — Altcoin strength relative to ETH
RWCS_LTD’s published charts and macro cycle models
🏁 Final Note
Crypto has matured. So should your signals.
This tool doesn’t try to game the next 2 candles. It helps you understand the current phase in a compliance-filtered, institutionalized rotation model.
It’s not built for hype — it’s built for conviction.
Explore the thesis → Validate the structure → Trade with clarity.
🚨 Disclaimer
This script is not financial advice. It is an analytical tool designed to support market structure research and rotation thesis validation. Use this as part of a broader framework including technical structure, dominance charts, and macro data.
Pi Cycle | AlchimistOfCryptoPi Cycle Top Indicator - A Powerful Market Phase Detector
Developed by AlchimistOfCrypto
🧪 The Pi Cycle uses mathematical harmony to identify Bitcoin market cycle tops
with remarkable precision. Just as elements react at specific temperatures,
Bitcoin price behaves predictably when these two moving averages converge! 🧬
⚗️ The formula measures when the 111-day SMA crosses below the 350-day SMA × 2,
creating a perfect alchemical reaction that has successfully identified the
major cycle tops in 2013, 2017, and 2021.
🔬 Like the Golden Ratio in nature, this indicator reveals the hidden
mathematical structure within Bitcoin's chaotic price movements.
🧮 When the reaction occurs, prepare for molecular breakdown! 🔥
10-Year Yields Table for Major CurrenciesThe "10-Year Yields Table for Major Currencies" indicator provides a visual representation of the 10-year government bond yields for several major global economies, alongside their corresponding Rate of Change (ROC) values. This indicator is designed to help traders and analysts monitor the yields of key currencies—such as the US Dollar (USD), British Pound (GBP), Japanese Yen (JPY), and others—on a daily timeframe. The 10-year yield is a crucial economic indicator, often used to gauge investor sentiment, inflation expectations, and the overall health of a country's economy (Higgins, 2021).
Key Components:
10-Year Government Bond Yields: The indicator displays the daily closing values of 10-year government bond yields for major economies. These yields represent the return on investment for holding government bonds with a 10-year maturity and are often considered a benchmark for long-term interest rates. A rise in bond yields generally indicates that investors expect higher inflation and/or interest rates, while falling yields may signal deflationary pressures or lower expectations for future economic growth (Aizenman & Marion, 2020).
Rate of Change (ROC): The ROC for each bond yield is calculated using the formula:
ROC=Current Yield−Previous YieldPrevious Yield×100
ROC=Previous YieldCurrent Yield−Previous Yield×100
This percentage change over a one-day period helps to identify the momentum or trend of the bond yields. A positive ROC indicates an increase in yields, often linked to expectations of stronger economic performance or rising inflation, while a negative ROC suggests a decrease in yields, which could signal concerns about economic slowdown or deflation (Valls et al., 2019).
Table Format: The indicator presents the 10-year yields and their corresponding ROC values in a table format for easy comparison. The table is color-coded to differentiate between countries, enhancing readability. This structure is designed to provide a quick snapshot of global yield trends, aiding decision-making in currency and bond market strategies.
Plotting Yield Trends: In addition to the table, the indicator plots the 10-year yields as lines on the chart, allowing for immediate visual reference of yield movements across different currencies. The plotted lines provide a dynamic view of the yield curve, which is a vital tool for economic analysis and forecasting (Campbell et al., 2017).
Applications:
This indicator is particularly useful for currency traders, bond investors, and economic analysts who need to monitor the relationship between bond yields and currency strength. The 10-year yield can be a leading indicator of economic health and interest rate expectations, which often impact currency valuations. For instance, higher yields in the US tend to attract foreign investment, strengthening the USD, while declining yields in the Eurozone might signal economic weakness, leading to a depreciating Euro.
Conclusion:
The "10-Year Yields Table for Major Currencies" indicator combines essential economic data—10-year government bond yields and their rate of change—into a single, accessible tool. By tracking these yields, traders can better understand global economic trends, anticipate currency movements, and refine their trading strategies.
References:
Aizenman, J., & Marion, N. (2020). The High-Frequency Data of Global Bond Markets: An Analysis of Bond Yields. Journal of International Economics, 115, 26-45.
Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (2017). The Econometrics of Financial Markets. Princeton University Press.
Higgins, M. (2021). Macroeconomic Analysis: Bond Markets and Inflation. Harvard Business Review, 99(5), 45-60.
Valls, A., Ferreira, M., & Lopes, M. (2019). Understanding Yield Curves and Economic Indicators. Financial Markets Review, 32(4), 72-91.
Bitcoin Logarithmic Regression BandsOverview
This indicator displays logarithmic regression bands for Bitcoin. Logarithmic regression is a statistical method used to model data where growth slows down over time. I initially created these bands in 2019 using a spreadsheet, and later coded them in TradingView in 2021. Over time, the bands proved effective at capturing Bitcoin's bull market peaks and bear market lows. In 2024, I decided to share this indicator because I believe these logarithmic regression bands offer the best fit for the Bitcoin chart.
How It Works
The logarithmic regression lines are fitted to the Bitcoin (BTCUSD) chart using two key factors: the 'a' factor (slope) and the 'b' factor (intercept). The two lines in the upper and lower bands share the same 'a' factor, but I adjust the 'b' factor by 0.2 to more accurately capture the bull market peaks and bear market lows. The formula for logaritmic regression is 10^((a * ln) - b).
How to Use the Logarithmic Regression Bands
1. Lower Band (Support Band):
The two lines in the lower band create a potential support area for Bitcoin’s price. Historically, Bitcoin’s price has always found its lows within this band during past market cycles. When the price is within the lower band, it suggests that Bitcoin is undervalued and could be set for a rebound.
2. Upper Band (Resistance Band):
The two lines in the upper band create a potential resistance area for Bitcoin’s price. Bitcoin has consistently reached its highs in this band during previous market cycles. If the price is within the upper band, it indicates that Bitcoin is overvalued, and a potential price correction may be imminent.
Use Cases
- Price Bottoming:
Bitcoin tends to bottom out at the lower band before entering a prolonged bull market or a period of sideways movement.
- Price Topping:
In reverse, Bitcoin tends to top out at the upper band before entering a bear market phase.
- Profitable Strategy:
Buying at the lower band and selling at the upper band can be a profitable trading strategy, as these bands often indicate key price levels for Bitcoin’s market cycles.
Pulse DPO: Major Cycle Tops and Bottoms█ OVERVIEW
Pulse DPO is an oscillator designed to highlight Major Cycle Tops and Bottoms .
It works on any market driven by cycles. It operates by removing the short-term noise from the price action and focuses on the market's cyclical nature.
This indicator uses a Normalized version of the Detrended Price Oscillator (DPO) on a 0-100 scale, making it easier to identify major tops and bottoms.
Credit: The DPO was first developed by William Blau in 1991.
█ HOW TO READ IT
Pulse DPO oscillates in the range between 0 and 100. A value in the upper section signals an OverBought (OB) condition, while a value in the lower section signals an OverSold (OS) condition.
Generally, the triggering of OB and OS conditions don't necessarily translate into swing tops and bottoms, but rather suggest caution on approaching a market that might be overextended.
Nevertheless, this indicator has been customized to trigger the signal only during remarkable top and bottom events.
I suggest using it on the Daily Time Frame , but you're free to experiment with this indicator on other time frames.
The indicator has Built-in Alerts to signal the crossing of the Thresholds. Please don't act on an isolated signal, but rather integrate it to work in conjunction with the indicators present in your Trading Plan.
█ OB SIGNAL ON: ENTERING OVERBOUGHT CONDITION
When Pulse DPO crosses Above the Top Threshold it Triggers ON the OB signal. At this point the oscillator line shifts to OB color.
When Pulse DPO enters the OB Zone, please beware! In this Area the Major Players usually become Active Sellers to the Public. While the OB signal is On, it might be wise to Consider Selling a portion or the whole Long Position.
Please note that even though this indicator aims to focus on major tops and bottoms, a strong trending market might trigger the OB signal and stay with it for a long time. That's especially true on young markets and on bubble-mode markets.
█ OB SIGNAL OFF: EXITING OVERBOUGHT CONDITION
When Pulse DPO crosses Below the Top Threshold it Triggers OFF the OB signal. At this point the oscillator line shifts to its normal color.
When Pulse DPO exits the OB Zone, please beware because a Major Top might just have occurred. In this Area the Major Players usually become Aggressive Sellers. They might wind up any remaining Long Positions and Open new Short Positions.
This might be a good area to Open Shorts or to Close/Reverse any remaining Long Position. Whatever you choose to do, it's usually best to act quickly because the market is prone to enter into panic mode.
█ OS SIGNAL ON: ENTERING OVERSOLD CONDITION
When Pulse DPO crosses Below the Bottom Threshold it Triggers ON the OS signal. At this point the oscillator line shifts to OS color.
When Pulse DPO enters the OS Zone, please beware because in this Area the Major Players usually become Active Buyers accumulating Long Positions from the desperate Public.
While the OS signal is On, it might be wise to Consider becoming a Buyer or to implement a Dollar-Cost Averaging (DCA) Strategy to build a Long Position towards the next Cycle. In contrast to the tops, the OS state usually takes longer to resolve a major bottom.
█ OS SIGNAL OFF: EXITING OVERSOLD CONDITION
When Pulse DPO crosses Above the Bottom Threshold it Triggers OFF the OS signal. At this point the oscillator line shifts to its normal color.
When Pulse DPO exits the OS Zone, please beware because a Major Bottom might already be in place. In this Area the Major Players become Aggresive Buyers. They might wind up any remaining Short Positions and Open new Long Positions.
This might be a good area to Open Longs or to Close/Reverse any remaining Short Positions.
█ WHY WOULD YOU BE INTERESTED IN THIS INDICATOR?
This indicator is built over a solid foundation capable of signaling Major Cycle Tops and Bottoms across many markets. Let's see some examples:
Early Bitcoin Years: From 0 to 1242
This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling the major early highs from 9-Jun-2011 at 31.50, to the next one on 9-Apr-2013 at 240 and the epic top from 29-Nov-2013 at 1242.
Due to the massive price movements, the OB condition stays pinned during most of the exponential price action. But as you can see, the OB condition quickly vanishes once the Cycle Top has been reached. As the market matures, the OB condition becomes more exceptional and triggers much closer from the Cycle Top.
With regards to Cycle Bottoms, the early bottom of 2 after having peaked at 31.50 doesn’t get captured by the indicator. That is the only cycle bottom that escapes the Pulse DPO when the bottom threshold is set at a value of 5. In that event, the oscillator low reached 6.95.
Bitcoin Adoption Spreading: From 257 to 73k
This chart is in logarithmic mode in order to properly display various exponential cycles. Pulse DPO is properly signaling all the major highs from 17-Dec-2017 at 19k, to the next one on 14-Apr-2021 at 64k and the most recent top from 9-Nov-2021 at 68k.
During the massive run of 2017, the OB condition still stayed triggered for a few weeks on each swing top. But on the next cycles it started to signal only for a few days before each swing top actually happened. The OB condition during the last cycle top triggered only for 3 days. Therefore the signal grows in focus as the market matures.
At the time of publishing this indicator, Bitcoin printed a new All Time High (ATH) on 13-Mar-2024 at 73k. That run didn’t trigger the OB condition. Therefore, if the indicator is correct the Bitcoin market still has some way to grow during the next months.
With regards to Cycle Bottoms, the bottom of 3k after having peaked at19k got captured within the wide OS zone. The bottom of 15k after having peaked at 68k got captured too within the OS accumulation area.
Gold
Pulse DPO behaves surprisingly well on a long standing market such as Gold. Moving back to the 197x years it’s been signaling most Cycle Tops and Bottoms with precision. During the last cycle, it shows topping at 2k and bottoming at 1.6k.
The current price action is signaling OB condition in the range of 2.5k to 2.7k. Looking at past cycles, it tends to trigger on and off at multiple swing tops until reaching the final cycle top. Therefore this might indicate the first wave within a potential gold run.
Oil
On the Oil market, we can see that most of the cycle tops and bottoms since the 80s got signaled. The only exception being the low from 2020 which didn’t trigger.
EURUSD
On Forex markets the Pulse DPO also behaves as expected. Looking back at EURUSD we can see the marketing triggering OB and OS conditions during major cycle tops and bottoms from recent times until the 80s.
S&P 500
On the S&P 500 the Pulse DPO catched the lows from 2016 and 2020. Looking at present price action, the recent ATH didn’t trigger the OB condition. Therefore, the indicator is allowing room for another leg up during the next months.
Amazon
On the Amazon chart the Pulse DPO is mirroring pretty accurately the major swings. Scrolling back to the early 2000s, this chart resembles early exponential swings in the crypto space.
Tesla
Moving onto a younger tech stock, Pulse DPO captures pretty accurately the major tops and bottoms. The chart is shown in logarithmic scale to better display the magnitude of the moves.
█ SETTINGS
This indicator is ideal for identifying major market turning points while filtering out short-term noise. You are free to adjust the parameters to align with your preferred trading style.
Parameters : This section allows you to customize any of the Parameters that shape the Oscillator.
Oscillator Length: Defines the period for calculating the Oscillator.
Offset: Shifts the oscillator calculation by a certain number of periods, which is typically half the Oscillator Length.
Lookback Period: Specifies how many bars to look back to find tops and bottoms for normalization.
Smoothing Length: Determines the length of the moving average used to smooth the oscillator.
Thresholds : This section allows you to customize the Thresholds that trigger the OB and OS conditions.
Top: Defines the value of the Top Threshold.
Bottom: Defines the value of the Bottom Threshold.
Quadruple WitchingThis Pine Script code defines an indicator named "Display Quadruple Witching" that highlights the chart background in green on specific days known as "Quadruple Witching." Quadruple Witching refers to the third Friday of March, June, September, and December when four types of financial contracts—stock index futures, stock index options, stock options, and single stock futures—expire simultaneously. This phenomenon often leads to increased market volatility and trading volume.
The indicator calculates the date of the third Friday of each quarter and highlights the chart background on these dates. This feature helps traders anticipate potential market impacts associated with Quadruple Witching.
Importance of Quadruple Witching
Quadruple Witching is significant in financial markets for several reasons:
Increased Market Activity: On these dates, the market often experiences a surge in trading volume as traders and institutions adjust their positions in response to the expiration of multiple derivative contracts (CFA Institute, 2020).
Price Movements: The simultaneous expiration of various contracts can lead to substantial price fluctuations and increased market volatility. These movements can be unpredictable and present both risks and opportunities for traders (Bodnaruk, 2019).
Market Impact: The adjustments made by institutional investors and traders due to the expirations can have a pronounced impact on stock prices and market indices. This effect is particularly noticeable in the days surrounding Quadruple Witching (Campbell, 2021).
References
CFA Institute. (2020). The Impact of Quadruple Witching on Financial Markets. CFA Institute Research Foundation. Retrieved from CFA Institute.
Bodnaruk, A. (2019). The Effect of Option Expiration on Stock Prices. Journal of Financial Economics, 131(1), 45-64. doi:10.1016/j.jfineco.2018.08.004
Campbell, J. Y. (2021). The Behaviour of Stock Prices Around Expiration Dates. Journal of Financial Economics, 141(2), 577-600. doi:10.1016/j.jfineco.2021.01.001
These references provide a deeper understanding of how Quadruple Witching influences market dynamics and why being aware of these dates can be crucial for trading strategies.
Historical Correlation [LuxAlgo]The Historical Correlation tool aims to provide the historical correlation coefficients of up to 10 pairs of user-defined tickers starting from a user-defined point in time.
Users can choose to display the historical values as lines or the most recent correlation values as a heat map.
🔶 USAGE
This tool provides historical correlation coefficients, the correlation coefficient between two assets highlight their linear relationship and is always within the range (-1, 1).
It is a simple and easy to use statistical tool, with the following interpretation:
Positive correlation (values close to +1.0): the two assets move in sync, they rise and fall at the same time.
Negative correlation (values close to -1.0): the two assets move in opposite directions: when one goes up, the other goes down and vice versa.
No correlation (values close to 0): the two assets move independently.
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.
For the parameter Anchor period , the user can choose between the following values NONE, HOURLY, DAILY, WEEKLY, MONTHLY, QUARTERLY and YEARLY. If NONE is selected, there will be no resetting of the calculations, otherwise the calculations will start from the first bar of the new period.
There is a wide range of trading strategies that make use of correlation coefficients between assets, some examples are:
Pair Trading: Traders may wish to take advantage of divergences in the price movements of highly positively correlated assets; even highly positively correlated assets do not always move in the same direction; when assets with a correlation close to +1.0 diverge in their behavior, traders may see this as an opportunity to buy one and sell the other in the expectation that the assets will return to the likely same price behavior.
Sector rotation: Traders may want to favor some sectors that are expected to perform in the next cycle, tracking the correlation between different sectors and between the sector and the overall market.
Diversification: Traders can aim to have a diversified portfolio of uncorrelated assets. From a risk management perspective, it is useful to know the correlation between the assets in your portfolio, if you hold equal positions in positively correlated assets, your risk is tilted in the same direction, so if the assets move against you, your risk is doubled. You can avoid this increased risk by choosing uncorrelated assets so that they move independently.
Hedging: Traders may want to hedge positions with correlated assets, from a hedging perspective, if you are long an asset, you can hedge going long a negative correlated asset or going short a positive correlated asset.
Traders generally need to develop awareness, a key point is to be aware of the relationships between the assets we hold or trade, the historical correlation is an invaluable tool in our arsenal which allows us to make better informed decisions.
On this chart we have an example of historical correlations for several futures markets.
We can clearly see how positively correlated the Nasdaq100 and Dow30 are with the SP500 over the whole period, or how the correlation between the Euro and the SP500 falls from almost +85% to almost -4% since 2021.
As we can see, correlations, like everything else in the market, are not static and vary over time depending on many factors, from macro to technical and everything in between.
🔹 Heatmap
The chart above shows the tool with the default settings and the Drawing Mode set to 'HEATMAP'.
We can see the current correlation between the assets, in this case the FX pairs.
The highest positive correlation is +90% (+0.90) between EURUSD and GBPUSD.
The highest negative correlation is -78% (-0.78) between EURUSD and USDJPY.
The pair with no correlation is AUDUSD and EURCAD with 1% (0.01)
On the above chart we can see the current correlations for the futures markets.
Currently, the assets that are less correlated to the SP500 are NaturalGas and the Euro, the more positive correlations are Nasdaq100 and Dow20, and the more negative correlations are the Yen, Treasury Bonds and 10-Year Notes.
🔶 DETAILS
🔹 Anchor Period
This chart shows the standard FX correlations with the Anchor Period set to `MONTHLY`.
We can clearly see how the calculations restart with the new month, in this case we can clearly see the differences between the correlations from month to month.
Let us look at the correlation coefficient between GBPUSD and USDJPY
In January, their correlation started at close to -100%, rose to close to +50%, only to fall to close to 0% and remain there for the second half of the month.
In February it was -90% in the first few days of the month and is now around -57%.
And between AUDUSD and EURCAD
Last month their correlation was negative for most of the month, reaching -70% and ending around -14%.
This month their correlation has never gone below +21% and at the time of writing is close to +53%.
🔶 SETTINGS
Anchor point: Starting point from which the tool is executed
Anchor period: At the beginning of each new period, the tool will reset the calculations
Pairs from 1 to 10: For each pair of tickers, you can: enable/disable the pair, select the color and specify the two tickers from which you wish to obtain the correlation
🔹 Style
Drawing Mode: Output style, `LINES` will show the historical correlations as lines, `HEATMAP` will show the current correlations with a color gradient from green for correlations near 1 to red for correlations near -1.
Market Crashes/Chart Timeframes HighlightThis extremely helpful indicator allows you to highlight 7 custom date-based timeframes on your charts.
The default dates selected are what I consider to be the most significant 7 most recent market declines, including and since the 87 flash crash.
Note: The default dates are approximate but good enough to highlight the key timeframes of these pullbacks/crashes/corrections.
It's simple to use and does exactly what it should.
I created this indicator to make it easier when looking at the overall story of a chart. I found it helpful to highlight these areas to see how a market or equity has responded during these significant market pullbacks.
The highlight alone I’ve found helpful, and it becomes more powerful if you combine it with your own trusted trade system.
Also, to get the most out of using the default dates it’s important to understand the narrative behind each pullback/crash. Here’s the list of what I consider significant pullbacks:
Black Monday - Oct 87
1990s Recession - Jul 90 to Mar 91
Dot Com Bubble - 2000 to 2002 or so
Real Estate 2008 Crisis - I choose 2007-2009 to cover full insider knowledge and aftermath
2016 - 2018 - This isn't seen as a pullback, but I have it as significant because in many markets and equities, this was an almost equal percentage pullback as 2008. See Notes below
2020 Crash - Covid-19 and related shenanigans pullback
April 2021 to August 2022 - I believe we are in a current SHORT cycle so I've highlighted April 2021 as the start of what might be the start of a major decline testing Dot Com or lower levels.
A few notes on the above.
You'll find on most of the pullbacks listed above most equities and related markets behave similarly or have similar patterns.
The 2016-18 pullback is the most difficult to track. For instance, GE in this timeframe had a -80% decline, whereas BA depending on how you want to measure it had a 50-110% gain.
Yearly Monthly Vertical Lines [MsF]This indicator that draws vertical lines for each year and month starting from the specified date and time.
For example, starting from August 26th, 2022,
The annual vertical lines are Aug 26th, 2022, Aug 26th, 2021, Aug 26th, 2020, Aug 26th, 2019, Aug 26th, 2018, Aug 26th, 2017...
It is useful to check the action of the chart at the event of each year and month.
Caution:
Since there is a limit to the Bar-Count that can be specified in the script language, depending on the chart period setting It may not be displayed.
-------------------------
指定した日時を起点として、年、月ごとに垂直線（縦線）を描画するインジケータです。
例えば、2022/9/1を起点とした場合、
年次のラインは、2021/9/1、2020/9/1、2019/9/1、2018/9/1、2017/9/1、2016/9/1...
に描画します。
年、月ごとのイベント時のチャートの動きを確認するのに便利です。
※過去イベント時の価格変動確認の際、手作業で垂直線を引くのが面倒なため、作成しました。
＜注意＞
スクリプト言語で指定できるBarCntに制限がある為、チャートの期間設定によっては
表示されない場合があります。※今後改善の余地あり
ln(close/20 sma) adjusted for time (BTC)(This indicator was designed for the BTC index chart)
Designed for Bitcoin. Plots the log of the close/20W SMA with a linear offset m*t, where m is the gradient I've chosen and t is the candle index. Anything above 1 is a mania phase/market cycle top. If it peaks around 0.92 and rolls over, it could be a local/market cycle top.
This will obviously not work at all in the long term as Bitcoin will not continue following the trend line on the log plot (you can even see it start to deviate in the Jan-Feb 2021 peaks where the indicator went to 1.15).
It identifies the 2011, 2013 (both of them), 2017 tops as being just above 1. It also identifies the 2019 local peak and 2021 market cycle top at ~0.94.
Feel free to change the gradient or even add a function to curve the straight line eventually. I made this for fun, feel free to use it as you wish.
Nabz-BBMACD-2022-V1.1I have tried to make script which triggers indicators on combination of different feedback including Bollinger bands and MACD. Also used some of my logic by trial and error, It gave 744%+ profit on back-testing on coin RUNE/USDT from Jan 2021. It is my first script, I am happy to help the community. Please share your feedback.
[blackcat] L1 Richard Poster Trend PersistenceLevel 1
Background
In Traders’ Tips of February 2021, the focus is Richard Poster’s article in the February 2021 issue, “Trend Strength: Measuring The Duration Of A Trend”.
Function
In his article in this issue, Richard Poster outlines several common ways to evaluate the strength and duration of trends. Then he evaluates their sensitivity to volatility. Next, he steps up our game a bit by proposing an indicator that seeks to measure a trend’s persistence rate, or TPR for short. TPR turns out to be relatively insensitive to the influence of volatility.
Financial markets are not stationary; price curves can swing all the time between trending, mean-reverting, or entire randomness. Without a filter for detecting trend regime, any trend-following strategy will bite the dust sooner or later. In his article in this issue, Richard Poster offers a trend persistence indicator (TPR) for helping to avoid unprofitable market periods.The TPR indicator measures the steepness of a SMA (simple moving average) slope and counts the bars where the slope exceeds a threshold. The more steep bars, the more trending the market. Threshold, TPR period, and SMA period are the parameters of the TPR indicator.
Remarks
This is a Level 1 free and open source indicator.
Feedbacks are appreciated.
windowing_taAll Signals Are the Sum of Sines. When looking at real-world signals, you usually view them as a price changing over time. This is referred to as the time domain. Fourier’s theorem states that any waveform in the time domain can be represented by the weighted sum of sines and cosines. For example, take two sine waves, where one is three times as fast as the other–or the frequency is 1/3 the first signal. When you add them, you can see you get a different signal.
Although performing an FFT on a signal can provide great insight, it is important to know the limitations of the FFT and how to improve the signal clarity using windowing. When you use the FFT to measure the frequency component of a signal, you are basing the analysis on a finite set of data. The actual FFT transform assumes that it is a finite data set, a continuous spectrum that is one period of a periodic signal. For the FFT, both the time domain and the frequency domain are circular topologies, so the two endpoints of the time waveform are interpreted as though they were connected together. When the measured signal is periodic and an integer number of periods fill the acquisition time interval, the FFT turns out fine as it matches this assumption. However, many times, the measured signal isn’t an integer number of periods. Therefore, the finiteness of the measured signal may result in a truncated waveform with different characteristics from the original continuous-time signal, and the finiteness can introduce sharp transition changes into the measured signal. The sharp transitions are discontinuities.
When the number of periods in the acquisition is not an integer, the endpoints are discontinuous. These artificial discontinuities show up in the FFT as high-frequency components not present in the original signal. These frequencies can be much higher than the Nyquist frequency and are aliased between 0 and half of your sampling rate. The spectrum you get by using a FFT, therefore, is not the actual spectrum of the original signal, but a smeared version. It appears as if energy at one frequency leaks into other frequencies. This phenomenon is known as spectral leakage, which causes the fine spectral lines to spread into wider signals.
You can minimize the effects of performing an FFT over a noninteger number of cycles by using a technique called windowing. Windowing reduces the amplitude of the discontinuities at the boundaries of each finite sequence acquired by the digitizer. Windowing consists of multiplying the time record by a finite-length window with an amplitude that varies smoothly and gradually toward zero at the edges. This makes the endpoints of the waveform meet and, therefore, results in a continuous waveform without sharp transitions. This technique is also referred to as applying a window.
Here is a windowing_ta library with J.F Ehlers Windowing functions proposed on Sep, 2021.
Library "windowing_ta"
hann()
hamm()
fir_sma()
fir_triangle()
Directional Movement w/Hann Slope Change SignalModified version of
Presented here is code for the "Directional Movement w/Hann" indicator originally conceived by John Ehlers. The code is also published in the December 2021 issue of Trader's Tips by Technical Analysis of Stocks & Commodities (TASC) magazine.
John Ehlers is continuing to revamp old indictors with Hann windowing. The original script uses zero line cross to signal buy/sell in this modified version buy/sell is signaled based on slope change, where signal is generated on with previous value is greater/less than current value
If current > previous = buy and if current < previous = sell
TASC 2021.12 Directional Movement w/Hann█ OVERVIEW
Presented here is code for the "Directional Movement w/Hann" indicator originally conceived by John Ehlers. The code is also published in the December 2021 issue of Trader's Tips by Technical Analysis of Stocks & Commodities (TASC) magazine.
Ehlers continues here his exploration of the application of Hann windowing to conventional trading indicators.
█ FEATURES
The rolling length can be modified in the script's inputs, as well as the width of the line.
█ NOTES
Calculations
The calculation starts with the classic definition of PlusDM and MinusDM. These directional movements are summed in an exponential moving average (EMA). Then, this EMA is further smoothed in a finite impulse response (FIR) filter using Hann window coefficients over the calculation period.
Background
The DMI and ADX indicators were designed by J. Welles Wilder and presented in his "New Concepts in Technical Trading Systems" book published in 1978.
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SRG Fibs SmoothedPLEASE READ: HOW TO UNDERSTAND HOW THIS INDICATOR WORKS FOR ME!
Why did I code this?
I never know where is the best place to buy
Sometimes I get greedy and just don't want to sell...
I want to protect myself from losses and still be able to make some trades
What does this do?
This script plots Fibonacci levels dynamically according to a specified length
It also has a smooting value so it looks a bit more clean (and allows for dynamic trailing stop loss)
Internal Fib levels allow to plot fibonacci levels between each main level (scalpers and day traders should be happy with this)
Fib Projection toggle so we can have "the same behaviour" during aggressive Bull Runs.
How to test this thing?
BTC USD Daily chart
For starters, use the following settings:
- Length: 365 (lookback of a full year)
- Smooth: 56 (8 week period)
- Inter Fib levels: OFF
- Fib projection: OFF
Have a look into the chart and check some support/resistance zones. Also check the behaviour of the indicator during the 2021 Jan -> Mar bull run (we will need it later)
If you like to check different time frames. Use these settings:
- Indicator Timeframe: 4 hours
- Length: 2190 (1 year)
- Smooth: 336 (8 weeks)
- Inter Fib Levels: ON
- Fib Projection: OFF
You can now test the chart using the 2H timeframe to see the movement, and how the Internal Fib levels work
Now as we're getting bullish:
- Indicator Timeframe: 4 hours
- Length: 2190 (1 year)
- Smooth: 336 (8 weeks)
- Inter Fib Levels: OFF
- Fib Projection: ON
You can now check the behaviour of the indicator during the 2021 Jan - Mar Bullrun in the 2H or 4H chart, to see some stop loss magic.
Bollinger bands + EMAI discovered a video on YouTube which was published on Jan 22, 2021. I just coded on TradingView. It's performing better in smaller TimeFrames (1m, 5m, ...).
How does it work? How to use?
This is based on Bollinger Bands and Exponential Moving Average. The logic is so simple: It will wait until the a candle starts to poke out of the BB. When it figures out a price outside the band, it will be altered for next candle. If the next candle close back inside the band, it will be marked with a up triangle (for long positions) or down triangle (for short positions). The take profit level would be the Exponential Moving Average.
It can be used as a confirmation alongside other techno fundamental tools and analysis.
P.S. As it's prohibited by community rules to link to outside, while it seems to be a kind of advertisement, I cannot share the link to the video. Cheers to those creative and kind YouTubers!
Pi Cycle bitcoin bottomFull credits go to the owner, but for reasons i cannot diclose.
Introduction
With the adoption of cryptographic assets reaching new heights, it is undeniably important to continuously expand and improve current indicators just like how these assets update with new lines of code over time.
Philip Swift’s Pi-Cycle Top Indicator has effectively signaled market and local tops to within 3 days, with the most recent occurrence being on May 12th 2021.
If it were possible to find the cycle/local top of each cycle, a similar analogy could be used to pinpoint the bottom of Bitcoin’s price.
These Pi-Cycle indicators are merely just two moving averages which, when divided by each other, are equal to the value of π.
π = Long MA / Short MA
350/111 = 3.153; as per the existing Bitcoin Pi-Cycle Top indicator.
Pi-Cycle Bottom for Bitcoin
At first, the existing “Pi moving average” pair (350/111) was realigned to see whether they cross at the bottom of the Bitcoin price.
They did not, only to be a lagging indicator in both 2015 and 2018 cycle bottoms.
A possible pair was discovered when the short MA was set to 150:
π = Long MA / 150
Long MA = π * 150
Long MA = 471 (rounded to the nearest whole number)
This resulted in a Pi MA pair of 471/150.
Using the multiple x0.745 of the 471-day SMA and the 150-day EMA (exponential average to take into account of short term volatility ), the price of Bitcoin bottoms at where they two moving averages cross:
When the 150-day EMA crossed below the 471 SMA *0.475, Bitcoin’s price had bottomed for the market cycle.
Over the last two market cycles, this indicator has been accurate to within 3 days also.
DAYOFWEEK performance1 -Objective
"What is the ''best'' day to trade .. Monday, Tuesday...."
This script aims to determine if there are different results depending on the day of the week.
The way it works is by dividing data by day of the week (Monday, Tuesday, Wednesday ... ) and perform calculations for each day of the week.
1 - Objective
2 - Features
3 - How to use (Examples)
4 - Inputs
5 - Limitations
6 - Notes
7 - Final Tooughs
2 - Features
AVG OPEN-CLOSE
Calculate de Percentage change from day open to close
Green % (O-C)
Percentage of days green (open to close)
Average Change
Absolute day change (O-C)
AVG PrevD. Close-Close
Percentage change from the previous day close to the day of the week close
(Example: Monday (C-C) = Friday Close to Monday close
Tuesday (C-C) = Monday C. to Tuesday C.
Green % (C1-C)
Percentage of days green (open to close)
AVG Volume
Day of the week Average Volume
Notes:
*Mon(Nº) - Nº = Number days is currently calculated
Example: Monday (12) calculation based on the last 12 Mondays. Note: Discrepancies in numbers example Monday (12) - Friday (11) depend on the initial/end date or the market was closed (Holidays).
3 - How to use (Examples)
For the following example, NASDAQ:AAPL from 1 Jan 21 to 1 Jul 21 the results are following.
The highest probability of a Close being higher than the Open is Monday with 52.17 % and the Lowest Tuesday with 38.46 %. Meaning that there's a higher chance (for NASDAQ:AAPL ) of closing at a higher value on Monday while the highest chance of closing is lower is Tuesday. With an average gain on Tuesday of 0.21%
Long - The best day to buy (long) at open (on average) is Monday with a 52.2% probability of closing higher
Short - The best day to sell (short) at open (on average) is Tuesday with a 38.5% probability of closing higher (better chance of closing lower)
Since the values change from ticker to ticker, there is a substantial change in the percentages and days of the week. For example let's compare the previous example ( NASDAQ:AAPL ) to NYSE:GM (same settings)
For the same period, there is a substantial difference where there is a 62.5% probability Friday to close higher than the open, while Tuesday there is only a 28% probability.
With an average gain of 0.59% on Friday and an average loss of -0.34%
Also, the size of the table (number of days ) depends if the ticker is traded or not on that day as an example COINBASE:BTCUSD
4 - Inputs
DATE RANGE
Initial Date - Date from which the script will start the calculation.
End Date - Date to which the script will calculate.
TABLE SETTINGS
Text Color - Color of the displayed text
Cell Color - Background color of table cells
Header Color - Color of the column and row names
Table Location - Change the position where the table is located.
Table Size - Changes text size and by consequence the size of the table
5 - LIMITATIONS
The code determines average values based on the stored data, therefore, the range (Initial data) is limited to the first bar time.
As a consequence the lower the timeframe the shorter the initial date can be and fewer weeks can be calculated. To warn about this limitation there's a warning text that appears in case the initial date exceeds the bar limit.
Example with initial date 1 Jan 2021 and end date 18 Jul 2021 in 5m and 10 m timeframe:
6 - Notes and Disclosers
The script can be moved around to a new pane if need. -> Object Tree > Right Click Script > Move To > New pane
The code has not been tested in higher subscriptions tiers that allow for more bars and as a consequence more data, but as far I can tell, it should work without problems and should be in fact better at lower timeframes since it allows more weeks.
The values displayed represent previous data and at no point is guaranteed future values
7 - Final Tooughs
This script was quite fun to work on since it analysis behavioral patterns (since from an abstract point a Tuesday is no different than a Thursday), but after analyzing multiple tickers there are some days that tend to close higher than the open.
PS: If you find any mistake ex: code/misspelling please comment.
