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!
Cari dalam skrip untuk "2021年黄金价格走势"
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.
Financial Astrology Crypto ML Daily TrendThis daily trend indicator is based on financial astrology cycles detected with advanced machine learning techniques for the crypto-currencies research portfolio: ADA, BAT, BNB, BTC, DASH, EOS, ETC, ETH, LINK, LTC, XLM, XMR, XRP, ZEC and ZRX. The daily price trend is forecasted through this planets cycles (angular aspects, speed, declination), fast ones are based on Moon, Mercury, Venus and Sun and Mid term cycles are based on Mars, Vesta and Ceres. The combination of all this cycles produce a daily price trend prediction that is encoded into a PineScript array using binary format "0 or 1" that represent sell and buy signals respectively. The indicator provides signals since 2021-01-01 to 2022-12-31, the past months signals purpose is to support backtesting of the indicator combined with other technical indicator entries like MAs, RSI or Stochastic. For future predictions besides 2022 a machine learning models re-train phase will be required.
The resolution of this indicator is 1D, you can tune a parameter where you can determine how many future bars of daily trend are plotted and adjust an hours shift to anticipate future signals into current bar in order to produce a leading indicator effect to anticipate the trend changes with some hours of anticipation. Combined with technical analysis indicators this daily trend is very powerful because can help to produce approximately 60% of profitable signals based on the backtesting results. You can look at our open source Github repositories to validate accuracy using the backtesting strategies we have implemented in Jesse Crypto Trading Framework as proof of concept of the predictive potential of this indicator. Alternatively, we have implemented a PineScript strategy that use this indicator, just consider that we are pending to do signals update to the period July 2021 to December 2022: This strategy have accumulated more than 110 likes and many traders have validated the predictive power of Financial Astrology.
DISCLAIMER: This indicator is experimental and don’t provide financial or investment advice, the main purpose is to demonstrate the predictive power of financial astrology. Any allocation of funds following the documented machine learning model prediction is a high-risk endeavour and it’s the users responsibility to practice healthy risk management according to your situation.
Zig Lines with Percent & ValueOverview, Features, and Usage:
The Zig Lines with Percent & Value is an indicator that highlights the highest and lowest points of the market from pivot points and zigzag lines based on the ZigZag Period setting. By a default value of 13 for the ZigZag Period this works well on Bitcoin or other alt coins on the 1 hour or higher timeframe charts.
What makes this indicator unique is that it draws a green line to signify an uptrend or a red line to signify a down trend. It will also show the percent difference between the previous point/line, for example: If you see a -negative percentage point with a red line drawn to it, then you are looking at a low pivot point and then as the green line is drawn to a +positive percentage value the percentage you see is the difference between the two points. This is great to see a trend reversal as you can look at previous pivot points and notice about how far the price moves before it changes direction (trend reversal).
There is an invisible EMA line that is used to assist with coloring the negative vs positive values. The value above or below the percentage is the lowest or highest price at that pivot point . The display of the price at the pivot point depends on your ZigZag Period setting and the timeframe of your chart.
Added Bollinger Bands as it fits perfectly with the visuals of the Zig Lines & Pivots.
Usage of Bollinger Bands:
~As the price or candle gets close to the top or bottom of the Bollinger band it can give you a better confirmation that the pivot location is at it's final place, and the trend is more likely to switch directions.
It’s important to know this indicator should not be used for alerts of any type it does repaint as the green or red line is drawing based on live chart data and it can change depending on the direction of the market. This is a great visual tool for trend analysis or to be used with other indicators as a confirmation for a possible good entry or exit position.
Credits ( and consent to use ):
Credits go to user LonesomeTheBlue for creation of this 'Double Zig Zag with HHLL' script.
The addition of the Value above/below the Percentages is from user Noldo and that script is found here:
The Bollinger Bands setup was suggested by user countseven12 and his script that uses the same BB setup is found here:
References:
1. Chen, James. (2021 March 15). Zig Zag Indicator . Received from http: www.investopedia.com
2. Mitchell, Cory. (2021 April 30). Pivot Points . Received from http: www.investopedia.com
All in One Strategy no RSI Label - For higher dollar cryptoThis is the All in One Strategy without the RSI suggestion label that will work well for any of the crypto currencies trading above $500 so the overlay shows up better. I am using ETH as an example on this.
Based on some comments on my previously published script that has been replaced I have added Alert Conditions to this version that can be used in other bots. You can also copy and paste these alert conditions into the other All in One script I published for the lower priced cryptocurrencies.
To use the alert conditions I have in here, you will need to convert this strategy into a study to do so. Delete the entry and exit logic at the end (lines 299 through 351), delete line 18 and paste the following in place of line 18:
study(shorttitle='Ain1 No Label',title='All in One Strategy no RSI Label', overlay=true, scale=scale.left)
Here are the settings to mimic what you see here in the back test strategy I am publishing. Remember that previous results do not guarantee future results.
Chart Time = 30 Minutes (if you didn't read my original All in One post, read it. Shorter isn't better. You lose your money faster in a shorter amount of time and I learned that the hard way)
Start Time = 1 April 2021 00:00
End Time = 31 December 2021 00:00
Trade Type = Long/Short
Stop Loss % = 20.1
Take Profit % = 14.57
RSI Length = 20
Overbought = 44
Oversold = 45
EMA Fast Length = 5
EMA Slow Length = 15
Overbought Lookback Minimum Value = 62
Overbought Lookback Bars = 3
Oversold Minimum Value = 43
Oversold Lookback Bars = 5
Source = Close
Max Lookback Period = 5
Use EMA Only = True (check the box)
K = 9
D = 17
K Mode = SMA
High Source = ohlc4
Low Source = ohlc4
Properties - Starting Amount is $3500, everything else is the same.
Any questions, feel free to ask. I will answer as soon as I can.
Realtime Delta Volume Action [LucF]█ OVERVIEW
This indicator displays on-chart, realtime, delta volume and delta ticks information for each bar. It aims to provide traders who trade price action on small timeframes with volume and tick information gathered as updates come in the chart's feed. It builds its own candles, which are optimized to display volume delta information. It only works in realtime.
█ WARNING
This script is intended for traders who can already profitably trade discretionary on small timeframes. The high cost in fees and the excitement of trading at small timeframes have ruined many newcomers to trading. While trading at small timeframes can work magic for adrenaline junkies in search of thrills rather than profits, I DO NOT recommend it to most traders. Only seasoned discretionary traders able to factor in the relatively high cost of such a trading practice can ever hope to take money out of markets in that type of environment, and I would venture they account for an infinitesimal percentage of traders. If you are a newcomer to trading, AVOID THIS TOOL AT ALL COSTS — unless you are interested in experimenting with the interpretation of volume delta combined with price action. No tool currently available on TradingView provides this type of close monitoring of volume delta information, but if you are not already trading small timeframes profitably, please do not let yourself become convinced that it is the missing piece you needed. Avoid becoming a sucker who only contributes by providing liquidity to markets.
The information calculated by the indicator cannot be saved on charts, nor can it be recalculated from historical bars.
If you refresh the chart or restart the script, the accumulated information will be lost.
█ FEATURES
Key values
The script displays the following key values:
• Above the bar: ticks delta (DT), the total ticks for the bar, the percentage of total ticks that DT represents (DT%)
• Below the bar: volume delta (DV), the total volume for the bar, the percentage of total volume that DV represents (DV%).
Candles
Candles are composed of four components:
1. A top shaped like this: ┴, and a bottom shaped like this: ┬ (picture a normal Japanese candle without a body outline; the values used are the same).
2. The candle bodies are filled with the bull/bear color representing the polarity of DV. The intensity of the body's color is determined by the DV% value.
When DV% is 100, the intensity of the fill is brightest. This plays well in interpreting the body colors, as the smaller, less significant DV% values will produce less vivid colors.
3. The bright-colored borders of the candle bodies occur on "strong bars", i.e., bars meeting the criteria selected in the script's inputs, which you can configure.
4. The POC line is a small horizontal line that appears to the left of the candle. It is the volume-weighted average of all price updates during the bar.
Calculations
This script monitors each realtime update of the chart's feed. It first determines if price has moved up or down since the last update. The polarity of the price change, in turn, determines the polarity of the volume and tick for that specific update. If price does not move between consecutive updates, then the last known polarity is used. Using this method, we can calculate a running volume delta and ticks delta for the bar, which becomes the bar's final delta values when the bar closes (you can inspect values of elapsed realtime bars in the Data Window or the indicator's values). Note that these values will all reset if the script re-executes because of a change in inputs or a chart refresh.
While this method of calculating is not perfect, it is by far the most precise way of calculating volume delta available on TradingView at the moment. Calculating more precise results would require scripts to have access to tick data from any chart timeframe. Charts at seconds timeframes do use exchange/broker ticks when the feeds you are using allow for it, and this indicator will run on them, but tick data is not yet available from higher timeframes. Also, note that the method used in this script is far superior to the intrabar inspection technique used on historical bars in my other "Delta Volume" indicators. This is because volume and ticks delta here are calculated from many more realtime updates than the available intrabars in history. Unfortunately, the calculation method used here cannot be used on historical bars, where intrabar inspection remains, in my opinion, the optimal method.
Inputs
The script's inputs provide many ways to personalize all the components: what is displayed, the colors used to display the information, and the marker conditions. Tooltips provide details for many of the inputs; I leave their exploration to you.
Markers
Markers provide a way for you to identify the points of interest of your choice on the chart. You control the set of conditions that trigger each of the five available markers.
You select conditions by entering, in the field for each marker, the number of each condition you want to include, separated by a comma. The conditions are:
1 — The bar's polarity is up/dn.
2 — `close` rises/falls ("rises" means it is higher than its value on the previous bar).
3 — DV's polarity is +/–.
4 — DV% rises (↕).
5 — POC rises/falls.
6 — The quantity of realtime updates rises (↕).
7 — DV > limit (You specify the limit in the inputs. Since DV can be +/–, DV– must be less than `–limit` for a short marker).
8 — DV% > limit (↕).
9 — DV+ rises for a long marker, DV– falls for a short.
10 — Consecutive DV+/DV– on two bars.
11 — Total volume rises (↕).
12 — DT's polarity is +/–.
13 — DT% rises (↕).
14 — DT+ rises for a long marker, DT– falls for a short.
Conditions showing the (↕) symbol do not have symmetrical states; they act more like filters. If you only include condition 4 in a marker's setup, for example, both long and short markers will trigger on bars where DV% rises. To trigger only long or short markers, you must add a condition providing directional differentiation, such as conditions 1 or 2. Accordingly, you would enter "1,4" or "2,4".
For a marker to trigger, ALL the conditions you specified for it must be met. Long markers appear on the chart as "Mx▲" signs under the values displayed below candles. Short markers display "Mx▼" over the number of updates displayed above candles. The marker's number will replace the "x" in "Mx▲". The script loads with five markers that will not trigger because no conditions are associated with them. To activate markers, you will need to select and enter the set of conditions you require for each one.
Alerts
You can configure alerts on this script. They will trigger whenever one of the configured markers triggers. Alerts do not repaint, so they trigger at the bar's close—which is also when the markers will appear.
█ HOW TO USE IT
As a rule, I do not prescribe expected use of my indicators, as traders have proved to be much more creative than me in using them. Additionally, I tend to think that if you expect detailed recommendations from me to be able to use my indicators, it's a sign you are in a precarious situation and should go back to the drawing board and master the necessary basics that will allow you to explore and decide for yourself if my indicators can be useful to you, and how you will use them. I will make an exception for this thing, as it presents fairly novel information. I will use simple logic to surmise potential uses, as contrary to most of my other indicators, I have NOT used this one to actually trade. Markets have a way of throwing wrenches in our seemingly bullet-proof rationalizing, so drive cautiously and please forgive me if the pointers I share here don't pan out.
The first thing to do is to disable your normal bars. You can do this by clicking on the eye icon that appears when you hover over the symbol's name in the upper-left corner of your chart.
The absolute value and polarity of DV mean little without perspective; that's why I include both total volume for the bar and the percentage that DV represents of that total volume. I interpret a low DV% value as indecision. If you share that opinion, you could, let's say, configure one of the markers on "DV% > 80%", for example (to do so you would enter "8" in the condition field of any marker, and "80" in the limit field for condition 8, below the marker conditions).
I also like to analyze price action on the bar with DV%. Small DV% values should often produce small candle bodies. If a small DV% value occurs on a bar with much movement and high volume, I'm thinking "tough battle with potential explosive power when one side wins". Conversely, large bodies with high DV% mean that large volume is breaching through multiple levels, or that nobody is suddenly willing to take the other side of a normal volume of trades.
I find the POC lines really interesting. First, they tell us the price point where the most significant action (taking into account both price occurrences AND volume) during the bar occurred. Second, they can be useful when compared against past values. Third, their color helps us in figuring out which ones are the most significant. Unsurprisingly, bunches of orange POCs tend to appear in consolidation zones, in pauses, and before reversals. It may be useful to often focus more on POC progression than on `close` values. This is not to say that OHLC values are not useful; looking, as is customary, for higher highs or lower lows, or for repeated tests of precise levels can of course still be useful. I do like how POCs add another dimension to chart readings.
What should you do with the ticks delta above bars? Old-time ticker tape readers paid attention to the sounds coming from it (the "ticker" moniker actually comes from the sound they made). They knew activity was picking up when the frequency of the "ticks" increased. My thinking is that the total number of ticks will help you in the same way, since increasing updates usually mean growing interest—and thus perhaps price movement, as increasing volatility or volume would lead us to surmise. Ticks delta can help you figure out when proportionally large, random orders come in from traders with other perspectives than the short-term price action you are typically working with when you use this tool. Just as volume delta, ticks delta are one more informational component that can help you confirm convergence when building your opinions on price action.
What are strong bars? They are an attempt to identify significance. They are like a default marker, except that instead of displaying "Mx▲/▼" below/above the bar, the candle's body is outlined in bright bull/bear color when one is detected. Strong bars require a respectable amount of conditions to be met (you can see and re-configure them in the inputs). Think of them as pushes rather than indications of an upcoming, strong and multi-bar move. Pushes do, for sure, often occur at the beginning of strong trends. You will often see a few strong bars occur at 2-3 bar intervals at the beginning or middle of trends. But they also tend to occur at tops/bottoms, which makes their interpretation problematic. Another pattern that you will see quite frequently is a final strong bar in the direction of the trend, followed a few bars later by another strong bar in the reverse direction. My summary analyses seemed to indicate these were perhaps good points where one could make a bet on an early, risky reversal entry.
The last piece of information displayed by the indicator is the color of the candle bodies. Three possible colors are used. Bull/bear is determined by the polarity of DV, but only when the bar's polarity matches that of DV. When it doesn't, the color is the divergence color (orange, by default). Whichever color is used for the body, its intensity is determined by the DV% value. Maximum intensity occurs when DV%=100, so the more significant DV% values generate more noticeable colors. Body colors can be useful when looking to confirm the convergence of other components. The visual effect this creates hopefully makes it easier to detect patterns on the chart.
One obvious methodology that comes to mind to trade with this tool would be to use another indicator like Technical Ratings at a higher timeframe to identify the larger context's trend, and then use this tool to identify entries for short-term trades in that direction.
█ NOTES AND RAMBLINGS
Instant Calculations
This indicator uses instant values calculated on the bar only. No moving averages or calculations involving historical periods are used. The only exception to this rule is in some of the marker conditions like "Two consecutive DV+ values", where information from the previous bar is used.
Trading Small vs Long Timeframes
I never trade discretionary at the 5sec–5min timeframes this indicator was designed to be used with; I trade discretionary at 1D, 1W and 1M timeframes, and let systems trade at smaller timeframes. The higher the timeframe you trade at, the fewer fees you will pay because you trade less and are not churning trading volume, as is inevitable at smaller timeframes. Trading at higher timeframes is also a good way to gain an instant edge on most of the trading crowd that has its nose to the ground and often tends to forget the big picture. It also makes for a much less demanding trading practice, where you have lots of time to research and build your long-term opinions on potential future outcomes. While the future is always uncertain, I believe trades riding on long-term trends have stronger underlying support from the reality outside markets.
To traders who will ask why I publish an indicator designed for small timeframes, let me say that my main purpose here is to showcase what can be done with Pine. I often see comments by coders who are obviously not aware of what Pine is capable of in 2021. Since its humble beginnings seven years ago, Pine has grown and become a serious programming language. TradingView's growing popularity and its ongoing commitment to keep Pine accessible to newcomers to programming is gradually making Pine more and more of a standard in indicator and strategy programming. The technical barriers to entry for traders interested in owning their trading practice by developing their personal tools to trade have never been so low. I am also publishing this script because I value volume delta information, and I present here what I think is an original way of analyzing it.
Performance
The script puts a heavy load on the Pine runtime and the charting engine. After running the script for a while, you will often notice your chart becoming less responsive, and your chart tab can take longer to activate when you go back to it after using other tabs. That is the reason I encourage you to set the number of historical values displayed on bars to the minimum that meets your needs. When your chart becomes less responsive because the script has been running on it for many hours, refreshing the browser tab will restart everything and bring the chart's speed back up. You will then lose the information displayed on elapsed bars.
Neutral Volume
This script represents a departure from the way I have previously calculated volume delta in my scripts. I used the notion of "neutral volume" when inspecting intrabar timeframes, for bars where price did not move. No longer. While this had little impact when using intrabar inspection because the minimum usable timeframe was 1min (where bars with zero movement are relatively infrequent), a more precise way was required to handle realtime updates, where multiple consecutive prices often have the same value. This will usually happen whenever orders are unable to move across the bid/ask levels, either because of slow action or because a large-volume bid/ask level is taking time to breach. In either case, the proper way to calculate the polarity of volume delta for those updates is to use the last known polarity, which is how I calculate now.
The Order Book
Without access to the order book's levels (the depth of market), we are limited to analyzing transactions that come in the TradingView feed for the chart. That does not mean the volume delta information calculated this way is irrelevant; on the contrary, much of the information calculated here is not available in trading consoles supplied by exchanges/brokers. Yet it's important to realize that without access to the order book, you are forfeiting the valuable information that can be gleaned from it. The order book's levels are always in movement, of course, and some of the information they contain is mere posturing, i.e., attempts to influence the behavior of other players in the market by traders/systems who will often remove their orders when price comes near their order levels. Nonetheless, the order book is an essential tool for serious traders operating at intraday timeframes. It can be used to time entries/exits, to explain the causes of particular price movements, to determine optimal stop levels, to get to know the traders/systems you are betting against (they tend to exhibit behavioral patterns only recognizable through the order book), etc. This tool in no way makes the order book less useful; I encourage all intraday traders to become familiar with it and avoid trading without one.
[blackcat] L1 Vitali Apirine Rate Of Change With BandsLevel: 1
Background
Vitali Apirine introuced this RoC indicator of “Rate Of Change With Bands” on March 2021.
Function
In Vitali Apirine's article “Rate Of Change With Bands” , the author introduces a concept of identifying overbought and oversold levels based on calculating standard deviation bands of the rate of change (ROC) momentum oscillator. The rate of change bands widen and narrow as the ROC deviation increases and decreases. The author proposes using this indicator in conjunction with other technical analysis methods to determine if the instrument is overbought or oversold.
Key Signal
UpperBand --> overbought threshold
oMARoc --> Output RoC Moving Average
LowerBand --> oversold threshold
Labels
L --> Long
S --> Short
XL --> Close Long
XS --> Close Short
Pros and Cons
100% Vitali Apirine definition translation, even variable names are the same. This help readers who would like to use pine to read his article.
Remarks
The 1st script for Blackcat1402 Vitali Apirine series publication.
Readme
In real life, I am a prolific inventor. I have successfully applied for more than 60 international and regional patents in the past 12 years. But in the past two years or so, I have tried to transfer my creativity to the development of trading strategies. Tradingview is the ideal platform for me. I am selecting and contributing some of the hundreds of scripts to publish in Tradingview community. Welcome everyone to interact with me to discuss these interesting pine scripts.
The scripts posted are categorized into 5 levels according to my efforts or manhours put into these works.
Level 1 : interesting script snippets or distinctive improvement from classic indicators or strategy. Level 1 scripts can usually appear in more complex indicators as a function module or element.
Level 2 : composite indicator/strategy. By selecting or combining several independent or dependent functions or sub indicators in proper way, the composite script exhibits a resonance phenomenon which can filter out noise or fake trading signal to enhance trading confidence level.
Level 3 : comprehensive indicator/strategy. They are simple trading systems based on my strategies. They are commonly containing several or all of entry signal, close signal, stop loss, take profit, re-entry, risk management, and position sizing techniques. Even some interesting fundamental and mass psychological aspects are incorporated.
Level 4 : script snippets or functions that do not disclose source code. Interesting element that can reveal market laws and work as raw material for indicators and strategies. If you find Level 1~2 scripts are helpful, Level 4 is a private version that took me far more efforts to develop.
Level 5 : indicator/strategy that do not disclose source code. private version of Level 3 script with my accumulated script processing skills or a large number of custom functions. I had a private function library built in past two years. Level 5 scripts use many of them to achieve private trading strategy.
wEMPlotDescription:
Plots the Weekly Expected Move (wEM) using the following week's Option Chain ATM Call+Put ask price to determine the EM for the following week
The wEM is the options market pricing in the expected future volatility for the following week.
The wEM is the range that the underlying price will be contained during the week 68% of the time.
These levels can be used as targets for options or equity trades for either directional or non-directional trades.
The options market in the major indices, such as SPX, can drive the overall market's order flow and so the EM can provide
useful insight into the hedging levels being used by professionals and market markers.
As Trading View does not currently provide access to option chain data, the option chain expected move for an underlying has to be manually
entered each week, but the script provides an easy to use framework to enter the parameters for the next week.
These parameters are as follows:
eg.
t1_1 = timestamp(2021, 02, 08) <==== timestamp for the start of next week (yyyy,mm,dd)
t1_2 = timestamp(2021, 02, 12) <==== timestamp for the end of next week (yyyy,mm,dd)
plotwem("QQQ", 331.36, 5.86, t1_1, t1_2, 0, 0)
^^^^
plotwem(Symbol, Close-last-week, Expected Move next week, Next week start timestamp, Next week end timestamp, Highlight-Upper-EM, Highlight-Lower-EM)
Parameters are:
Symbol : Underlying chart symbol (aka ticker). Can be a symbol for equity, future or index.
Close-last-week: Closing price at the end of last week.
Expected Move next week: The Expected Move for next week: Calculated from next week's Option Chain ATM Call+Put ask price
Next week start timestamp : Timestamp for the start of next week
Next week end timestamp : Timestamp for the end of next week
Highlight-Upper-EM : highlight upper expected move level. Set to 1 to highlight with red color. Set to 0 is no highlight.
Highlight-Lower-EM : highlight lower expected move level. Set to 1 to highlight with red color. Set to 0 is no highlight.
The highlight parameters can be updated at any point to indicate that the underlying has either touched the EM level or breached the level.
The highlights can be used to visually determine periods of market instability which can provide insight into applicable strategies for the market conditions.
[DS]Entry_Exit_TRADE.V01-StrategyThe proposal of this script is to show the possible trading points of BUY and SELL based on the 15-minute chart of the Nasdaq Future Index. The start point of the strategy was schedule for 2021/01/01 and until the time of this publication (2021/01/31), for 1 index contract the results presented area a Gross Profit of 2.97% with a Net Profit of 1.35%.
█ FEATURES
The indicator shows on the graph the position of the MACD and TSI indicators that are the places of strength among Buyers and Sellers.
It's possible to observe a sharp fall or rise in the price of these positions.
On the current candle, a label is displayed containing the value of the William %R Mod indicator, which will display the OverBought position (dark red) and OverSold position (dark green). The other colors like light red and green are the regions where the price makes the decision of which direction to go.
There are also other indicators:
a) The positions of the BUY (light green) and SELL areas (light red);
b) The label with the position of BUY (dark green) and SELL (dark red) with the line that connects these points;
c) DEMA 72 (orange);
d) EmaOchl4 in the color green for BULL and red for BEAR market;
e) Pivots high and low
f) Maximum (purple light) and minimum areas (blue light)
█ FUNCTIONS AND SETTINGS
The indicator uses the following functions:
(1) DEMA - Double Exponential Moving Average (08,17,34, 72)
(2) ema () - Exponential Moving Averge (72, ohlc4)
(3) plot()
(4) barcolor()
(5) cross()
(6) pivots ()
(7) William R% Md (OverBought = -7, OverSold=-93)
(8) Maximum and Minimum Value
(9) fill()
(10) macd () - Moving Average Convergence Divergence (Fast Lengt=12, Slow Length=26, Source=close, Signal Smoothing=9)
(11) tsi() - Trading Strenght Indicator==> Índice de Força Real ( IFR ) (Long Length=72, Short Length=17, Signal Length=17)
(12) Buy and Sell TRADE Points
█ PERFORMANCE AND ERRORS
The positions of BUY and SELL points are defined through the crossing of the Dema 34 candles with the Ema Ohcl4. As it is an indicator, it can present different positions from de market direction. Thus there is a need to observe the direction of the market in order to verify whether the indicate decision is really acceptable. The decision to BUY or SELL an asset must be well studied to avoid financial losses. The indicator will only help you in this decision, is your responsibility the decision of entering or leaving an asset.
█ THANKS TO
PineCoders for all they do, all the tools and help they provide, and their involvement in making a better community. All the PineCoders, Pine Pros, and Pine Wizards, people who share their work and knowledge for the sake of it and helping others, I'm very happy and grate full indeed.
█ NOTE
If you have any suggestions for improving the script or need help using it, please send a message in the comments
Weekly/Daily/Hourly/Minutes Colored Background IntervalsThis is my "Weekly/Daily/Hourly/Minutes Colored Background Intervals" assistant. I wouldn't describe it as an indicator, it just exhibits coloration of referenced periods of time with bgcolor() in Pine. With the arrival of 2021, I pondered the necessity of needing a visualization pre-2021 to visually recognize periodicity of market movements by the week, day, hour, or an adjustable period of minutes. While this script is simply generic, I hope you may find useful in your endeavors as a member on TradingView.
Explaining the script's usage, the "Minutes" input can be adjusted from anywhere between 5-55 minutes for only intraday. This can be modified to accommodate 90 minutes (1.5hrs) or any other minutes period desirable by tweaking certain numbers up to 1440. Minutes and Hourly backgrounds are disabled by default for most daily traders. Changing the input() code to `true` will provide them on by default when the script loads, if you choose that route. Each time periods background color is enable/disable capable. All of the colors are easily adjustable to any combination you can ponder for your visual acuity with the color swatch provided by input(type=input.color). The coloring can be "swapped" by input() depending on how you wish to start and end the day visually. I thought this would come in handy. The weekly background can have different starting points, whether it be Sunday, Monday, or any other day such as Friday for example.
The entire script's contents isn't intended for complete re-use as is for publicly published scripts. It's more along the lines of code that could be used to personally modify indicators you have, depending on the time frames you may actually be trading on. The code is basically modular, so you can use bits and pieces of it in your personally modified Pine Editor scripts that you wish to customize for yourself. I will say that the isXxx() functions are completely reusable in any script without any need for author permission inquiries from me, as easy as copy and paste. Those may come in handy for many folks. If you find them useful in certain circumstances, use isXxx() functions as you please. Day of the week detection by functions will have applications beyond my current intended use for them.
Of notable mention, this is a miniature lesson by example of how the new input(type=input.color) may be used. I'm also using `var` inside functions to aid in computational efficiency of the script runtime. The colors are permanently stored at the very beginning of the scripts operation inside the function and just reused from that point onward. Its a rare use case, but well suited for this scripts intention. Once again I have demonstrated the "Power of Pine" for developers of any experience level to learn from via code elegance.
When available time provides itself, I will consider your inquiries, thoughts, and concepts presented below in the comments section, should you have any questions or comments regarding this indicator. When my indicators achieve more prevalent use by TV members , I may implement more ideas when they present themselves as worthy additions. Have a profitable future everyone!
Bull Call Spread Entry StrategyThis strategy script uses the "Spread Entry Strength" overlay indicator script I designed to show entry timing optimized for an Option Bull
Call Spread.
As for this strategy...
The defaults for the strategy itself are as follows:
Period for strategy: 1/1/18 to 12/1/2021. This can be changed to a different period using the settings.
Condition for entry:
Bull Spread Entry Strength >= "Overlay Signal Strength Level"
Limit entry is used, price must be <= close when signaled
Entry occurs by next day or the order is cancelled
Condition for exit (uses a timed exit):
Bars passed since order entry >= 30 (6 weeks..~42 calendar days)
Thursday (day before "option" expiration date... assuming weekly options exist)
All of the user settings from the overlay are pulled into this for customization purposes. Details of the actual Spread Entry Strength overlay are as follows (copied from my shared indicator):
2 background shadings will occur:
The background will shade blue if the ticker is prime for a Bullish Call spread.
The background will shade purple if the the ticker is prime for a Bearish Put spread.
In theory, if the SE Strength is at one of the extremes of the Bear or Bull side, then a spread is prime for entry.
To calculate this, 8 conditions receive a 1 or zero dependent on whether the condition is true (1) or false (0), and then all of those are summed. The primary gist of the strength comes from Nishant's book, or my interpretation thereof, with some additives that limits what I need to review (such as condition 8 below.)
The 8 Bull Conditions are:
1) Bollinger Bands are outside of the Keltner Channels
2) ADX is trending up
3) RSI is trending up
4) -DI is trending down
5) RSI is under 30
6) Price is below the lower Keltner Channel
7) Price is between the lower Bollinger Band and the Bollinger basis.
8) Price at one point within the last 5 bars was below the lower Bollinger Band
The 8 Bear Conditions are the inverse conditions (except the first):
1) Bollinger Bands are outside of the Keltner Channels
2) ADX is trending down
3) RSI is trending down
4) +DI is trending up
5) RSI is over 70
6) Price is above the upper Keltner Channel
7) Price is between the upper Bollinger Band and the Bollinger basis.
8) Price at one point within the last 5 bars was above the upper Bollinger Band
There is a "market noise" filter that will filter out shading when another market move is considered, i.e. if you don't want to see the potential trade when QQQ moves more than 1% then do the following in the settings:
Check "Market Filter"
Enter QQQ in the "Market Ticker To Use"
Enter 1 in the "Market Too Hot Level"
Press Ok
Obviously, the same holds true for the "Market Too Cool Filter."
Second release notes:
Overlay Signal Strength Level - You can set your own "level" for the overlay in the settings, instead of having to change the script code itself. I have the default set to 6. A lower number shows more overlays, a higher number shows fewer (i.e. more conditions have been met.).
Provide Narrative (Troubleshooting) - Narrative label created with several outputs that will show after the last bar. This narrative needs to be turned on in the settings, as the default is "off" ... unchecked.
Remove Strength Indicator When Squeezed - when checked no overlays will be produced regardless of "scoring." Default is off.
Show Squeezes (Will Override Indicator When Concurrent) - overlays an orange background when the ticker is in a squeeze. I am still working on the accuracy here, but it's usable. This will override the strength indicator as well. This needs to be turned on, if you want it.
Short SMA Period - period used to calculate the short SMA, used in the narrative only, at this point in time.
Medium SMA Period - period used to calculate the medium SMA, used in the narrative only, at this point in time.
Long SMA Period - period used to calculate the medium SMA, used in the narrative only, at this point in time.
Outside of the settings... a few calculation adjustments here and there have occurred and some color shading adjustments to allow for the adjustable level setting.
[AS] MACD-v & Hist [Alex Spiroglou | S.M.A.R.T. TRADER SYSTEMS] MACD-v & MACD-v Histogram
=======================================
Volatility Normalised Momentum 📈
Twice Awarded Indicator 🏆
=======================================
=======================================
✅ 1. INTRODUCTION TO THE MACD-v ✅
=======================================
I created the MACD-v in 2015,
as a way to deal with the limitations
of well known indicators like the Stochastic, RSI, MACD.
I decided to publicly share a very small part of my research
in the form of a research paper I wrote in 2022,
titled "MACD-v: Volatility Normalised Momentum".
That paper was awarded twice:
1. The "Charles H. Dow" Award (2022),
for outstanding research in Technical Analysis,
by the Chartered Market Technicians Association (CMTA)
2. The "Founders" Award (2022),
for advances in Active Investment Management,
by the National Association of Active Investment Managers (NAAIM)
=======================================
===================================================
❌ 2. WHY CREATE THE MACD-v ?
THE LIMITATIONS OF CONVENTIONAL MOMENTUM INDICATORS
====================================================
Technical Analysis indicators focused on momentum,
come in two general categories,
each with its own set of limitations:
(i) Range Bound Oscillators (RSI, Stochastics, etc)
These usually have a scaling of 0-100,
and thus have the advantage of having normalised readings,
that are comparable across time and securities.
However they have the following limitations (among others):
1. Skewing effect of steep trends
2. Indicator values do not adjust with and reflect true momentum
(indicator values are capped to 100)
(ii) Unbound Oscillators (MACD, RoC, etc)
These are boundless indicators,
and can expand with the market,
without being limited by a 0-100 scaling,
and thus have the advantage of really measuring momentum.
They have the main following limitations (among others):
1. Subjectivity of overbought / oversold levels
2. Not comparable across time
3. Not comparable across securities
=======================================
=======================================
💡 3. THE SOLUTION TO SOLVE THESE LIMITATIONS
=======================================
In order to deal with these limitations,
I decided to create an indicator,
that would be the "Best of two worlds".
A unique & hybrid indicator,
that would have objective normalised readings
(similar to Range Bound Oscillators - RSI)
but would also be able to have no upper/lower boundaries
(similar to Unbound Oscillators - MACD).
This would be achieved by "normalising" a boundless oscillator (MACD)
=======================================
==================================================
⛔ 4. DEEP DIVE INTO THE 5 LIMITATIONS OF THE MACD
==================================================
A Bloomberg study found that the MACD
is the most popular indicator after the RSI,
but the MACD has 5 BIG limitations.
Limitation 1: MACD values are not comparable across Time
The raw MACD values shift
as the underlying security's absolute value changes across time,
making historical comparisons obsolete
e.g S&P 500 maximum MACD was 1.56 in 1957-1971,
but reached 86.31 in 2019-2021 - not indicating 55x stronger momentum,
but simply different price levels.
Limitation 2: MACD values are not comparable across Assets
Traditional MACD cannot compare momentum between different assets.
S&P 500 MACD of 65 versus EUR/USD MACD of -0.5
reflects absolute price differences, not momentum differences
Limitation 3: MACD values cannot be Systematically Classified
Due to limitations #1 & #2, it is not possible to create
a momentum level classification scale
where one can define "fast", "slow", "overbought", "oversold" momentum
making systematic analysis impossible
Limitation 4: MACD Signal Line gives false crossovers in low-momentum ranges
In range-bound, low momentum environments,
most of the MACD signal line crossovers are false (noise)
Since there is no objective momentum classification system (limitation #3),
it is not possible to filter these signals out,
by avoiding them when momentum is low
Limitation 5: MACD Signal Line gives late crossovers in high momentum regimes.
Signal lag in strong trends not good at timing the turning point
— In high-momentum moves, MACD crossovers may come late.
Since there is no objective momentum classification system (limitation #3),
it is not possible to filter these signals out,
by avoiding them when momentum is high
===================================================================
===================================================================
🏆 5. MACD-v : THE SOLUTION TO THE LIMITATIONS OF THE MACD , RSI, etc
====================================================================
MACD-v is a volatility normalised momentum indicator.
It remedies these 5 limitations of the classic MACD,
while creating a tool with unique properties.
Formula: × 100
MACD-V enhances the classic MACD by normalizing for volatility,
transforming price-dependent readings into standardized momentum values.
This resolves key limitations of traditional MACD and adds significant analytical power.
Core Advantages of MACD-V
Advantage 1: Time-Based Stability
MACD-V values are consistent and comparable over time.
A reading of 100 has the same meaning today as it did in the past
(unlike traditional MACD which is influenced by changes in price and volatility over time)
Advantage 2: Cross-Market Comparability
MACD-V provides universal scaling.
Readings (e.g., ±50) apply consistently across all asset classes—stocks,
bonds, commodities, or currencies,
allowing traders to compare momentum across markets reliably.
Advantage 3: Objective Momentum Classification
MACD-V includes a defined 5-range momentum lifecycle
with standardized thresholds (e.g., -150 to +150).
This offers an objective framework for analyzing market conditions
and supports integration with broader models.
Advantage 4: False Signal Reduction in Low-Momentum Regimes
MACD-V introduces a "neutral zone" (typically -50 to +50)
to filter out these low-probability signals.
Advantage 5: Improved Signal Timing in High-Momentum Regimes
MACD-V identifies extremely strong trends,
allowing for more precise entry and exit points.
Advantage 6: Trend-Adaptive Scaling
Unlike bounded oscillators like RSI or Stochastic,
MACD-V dynamically expands with trend strength,
providing clearer momentum insights without artificial limits.
Advantage 7: Enhanced Divergence Detection
MACD-V offers more reliable divergence signals
by avoiding distortion at extreme levels,
a common flaw in bounded indicators (RSI, etc)
====================================================================
=======================================
⚒️ 5. HOW TO USE THE MACD-v: 7 CORE PATTERNS
HOW TO USE THE MACD-v Histogram: 2 CORE PATTERNS
=======================================
>>>>>> BASIC USE (RANGE RULES) <<<<<<
The MACD-v has 7 Core Patterns (Ranges) :
1. Risk Range (Overbought)
Condition: MACD-V > Signal Line and MACD-V > +150
Interpretation: Extremely strong bullish momentum—potential exhaustion or reversal zone.
2. Retracing
Condition: MACD-V < Signal Line and MACD-V > -50
Interpretation: Mild pullback within a bullish trend.
3. Rundown
Condition: MACD-V < Signal Line and -50 > MACD-V > -150
Interpretation: Momentum is weakening—bearish pressure building.
4. Risk Range (Oversold)
Condition: MACD-V < Signal Line and MACD-V < -150
Interpretation: Extreme bearish momentum—potential for reversal or capitulation.
5. Rebounding
Condition: MACD-V > Signal Line and MACD-V > -150
Interpretation: Bullish recovery from oversold or weak conditions.
6. Rallying
Condition: MACD-V > Signal Line and MACD-V > +50
Interpretation: Strengthening bullish trend—momentum accelerating.
7. Ranging (Neutral Zone)
Condition: MACD-V remains between -50 and +50 for 20+ bars
Interpretation: Sideways market—low conviction and momentum.
The MACD-v Histogram has 2 Core Patterns (Ranges) :
1. Risk (Overbought)
Condition: Histogram > +40
Interpretation: Short-term bullish momentum is stretched—possible overextension or reversal risk.
2. Risk (Oversold)
Condition: Histogram < -40
Interpretation: Short-term bearish momentum is stretched—potential for rebound or reversal.
=======================================
=======================================
📈 6. ADVANCED PATTERNS WITH MACD-v
=======================================
Thanks to its volatility normalization,
the MACD-V framework enables the development
of a wide range of advanced pattern recognition setups,
trading signals, and strategic models.
These patterns go beyond basic crossovers,
offering deeper insight into momentum structure,
regime shifts, and high-probability trade setups.
These are not part of this script
=======================================
===========================================================
⚙️ 7. FUNCTIONALITY - HOW TO ADD THE INDICATORS TO YOUR CHART
===========================================================
The script allows you to see :
1. MACD-v
The indicator with the ranges (150,50,0,-50,-150)
and colour coded according to its 7 basic patterns
2. MACD-v Histogram
The indicator The indicator with the ranges (40,0,-40)
and colour coded according to its 2 basic ranges / patterns
3. MACD-v Heatmap
You can see the MACD-v in a Multiple Timeframe basis,
using a colour-coded Heatmap
Note that lowest timeframe in the heatmap must be the one on the chart
i.e. if you see the daily chart, then the Heatmap will be Daily, Weekly, Monthly
4. MACD-v Dashboard
You can see the MACD-v for 7 markets,
in a multiple timeframe basis
=======================================
=======================================
🤝 CONTRIBUTIONS 🤝
=======================================
I would like to thank the following people:
1. Mike Christensen for coding the indicator
@TradersPostInc, @Mik3Christ3ns3n,
2. @Indicator-Jones For allowing me to use his Scanner
3. @Daveatt For allowing me to use his heatmap
=======================================
=======================================
⚠️ LEGAL - Usage and Attribution Notice ⚠️
=======================================
Use of this Script is permitted
for personal or non-commercial purposes,
including implementation by coders and TradingView users.
However, any form of paid redistribution,
resale, or commercial exploitation is strictly prohibited.
Proper attribution to the original author is expected and appreciated,
in order to acknowledge the source
and maintain the integrity of the original work.
Failure to comply with these terms,
or to take corrective action within 48 hours of notification,
will result in a formal report to TradingView’s moderation team,
and will actively pursue account suspension and removal of the infringing script(s).
Continued violations may result in further legal action, as deemed necessary.
=======================================
=======================================
⚠️ DISCLAIMER ⚠️
=======================================
This indicator is For Educational Purposes Only (F.E.P.O.).
I am just Teaching by Example (T.B.E.)
It does not constitute investment advice.
There are no guarantees in trading - except one.
You will have losses in trading.
I can guarantee you that with 100% certainty.
The author is not responsible for any financial losses
or trading decisions made based on this indicator. 🙏
Always perform your own analysis and use proper risk management. 🛡️
=======================================
BTC Flow Dashboard : Spot Premium + OI + Funding + Cycle SignalsSpot Premium vs Perpetual Basket (%):
Tracks how aggressively perps are trading relative to spot, a leading indicator of speculative activity and leverage buildup.
Aggregated Open Interest Z-Score:
A normalized view of OI expansion/contraction across major exchanges (Binance, BitMEX, Bybit, Kraken, etc.), highlighting when leverage enters overheated zones.
Composite Funding Rate Analysis:
Calculates a TWAP-smoothed funding composite across major venues, with optional APR scaling, showing where perpetual markets are paying for long or short exposure.
Confluence Signal Engine:
Dynamically flags bullish or bearish market conditions based on premium behavior and leverage environment — including over-leverage warnings that often precede volatility spikes.
Extreme Cycle Tops & Bottoms (Experimental):
Optional signal module that highlights historically significant extremes (e.g., 2020 bottom or 2021 top) based on statistical Z-score thresholds across the three core metrics.
Notes & Tips
Works best on weekly or monthly timeframes for macro cycle analysis.
Daily and 3D views provide short-term leverage context but may produce more frequent signals.
The Extreme Signal Engine is experimental — not a trading signal on its own, but a contextual tool to support macro decision-making.
Bitcoin Cycle History Visualization [SwissAlgo]BTC 4-Year Cycle Tops & Bottoms
Historical visualization of Bitcoin's market cycles from 2010 to present, with projections based on weighted averages of past performance.
-----------------------------------------------------------------
CALCULATION METHODOLOGY
Why Bottom-to-Bottom Cycle Measurement?
This indicator defines cycles as bottom-to-bottom periods. This is one of several valid approaches to Bitcoin cycle analysis:
- Focuses on market behavior (price bottoms) rather than supply schedule events (halving-to-halving)
- Bottoms may offer good reference points for some analytical purposes
- Tops tend to be extended periods that are harder to define precisely
- Aligns with how some traditional asset cycles are measured and the timing observed in the broader "risk-on" assets category
- Halving events are shown separately (yellow backgrounds) for reference
- Neither halving-based nor bottom-based measurement is inherently superior
Different analysts prefer different cycle definitions based on their analytical goals. This approach prioritizes observable market turning points.
Cycle Date Definitions
- Approximate monthly ranges used for each event (e.g., Nov 2022 bottom = Nov 1-30, 2022)
- Cycle 1: Jul 2010 bottom → Jun 2011 top → Nov 2011 bottom
- Cycle 2: Nov 2011 bottom → Dec 2013 top → Jan 2015 bottom
- Cycle 3: Jan 2015 bottom → Dec 2017 top → Dec 2018 bottom
- Cycle 4: Dec 2018 bottom → Nov 2021 top → Nov 2022 bottom
- Future cycles will be added as new top/bottom dates become firm
Duration Calculations
- Days = timestamp difference converted to days (milliseconds ÷ 86,400,000)
- Bottom → Top: days from cycle bottom to peak
- Top → Bottom: days from peak to next cycle bottom
- Bottom → Bottom: full cycle duration (sum of above)
Price Change Calculations
- % Change = ((New Price - Old Price) / Old Price) × 100
- Example: $200 → $19,700 = ((19,700 - 200) / 200) × 100 = 9,750% gain
- Approximate historical prices used (rounded to significant figures)
Weighted Average Formula
Recent cycles weighted more heavily to reflect the evolved market structure:
- Cycle 1 (2010-2011): EXCLUDED (too early-stage, tiny market cap)
- Cycle 2 (2011-2015): Weight = 1x
- Cycle 3 (2015-2018): Weight = 3x
- Cycle 4 (2018-2022): Weight = 5x
Formula: Weighted Avg = (C2×1 + C3×3 + C4×5) / (1+3+5)
Example for Bottom→Top days: (761×1 + 1065×3 + 1066×5) / 9 = 1,032 days
Projection Method
- Projected Top Date = Nov 2022 bottom + weighted avg Bottom→Top days
- Projected Bottom Date = Nov 2022 bottom + weighted avg Bottom→Bottom days
- Current days elapsed compared to weighted averages
- Warning symbol (⚠) shown when the current cycle exceeds the historical average
Technical Implementation
- Historical cycle dates are hardcoded (not algorithmically detected)
- Dates represent approximate monthly ranges for each event
- The indicator will be updated as the Cycle 5 top and bottom dates become confirmed
- Updates require manual code maintenance - not automatic
- Users should verify they're using the latest version for current cycle data
-----------------------------------------------------------------
FEATURES
- Background highlights for historical tops (red), bottoms (green), and halving events (yellow)
- Data table showing cycle durations and price changes
- Visual cycle boundary boxes with subtle coloring
- Projected timeframes displayed as dashed vertical lines
- Toggle on/off for each visual element
- Customizable background colors
-----------------------------------------------------------------
DISPLAY SETTINGS
- Show/hide cycle tops, bottoms, halvings, data table, and cycle boxes
- Customizable background colors for each event type
- Clean, institutional-grade visual design suitable for analysis
UPDATES & MAINTENANCE
This indicator is maintained as new cycle events occur. When Cycle 5's top and bottom are confirmed with sufficient time elapsed, the code and projections will be updated accordingly. Check for the latest version periodically.
OPEN SOURCE
Code available for review, modification, and improvement. Educational transparency is prioritized.
-----------------------------------------------------------------
IMPORTANT LIMITATIONS
⚠ EXTREMELY SMALL SAMPLE SIZE
Based on only 4 complete cycles (2011-2022). In statistical analysis, this is insufficient for reliable predictions.
⚠ CHANGED MARKET STRUCTURE
Bitcoin's market has fundamentally evolved since early cycles:
- 2010-2015: Tiny market cap, retail-only, unregulated
- 2024-2025: Institutional adoption, spot ETFs, regulatory frameworks, macro correlation
The environment that created past patterns no longer exists in the same form.
⚠ NO PREDICTIVE GUARANTEE
Historical patterns can and do break. Market cycles are not laws of physics. Past performance does not guarantee future results. The next cycle may not follow historical averages.
⚠ LENGTHENING CYCLE THEORY
Some analysts believe cycles are extending over time (diminishing returns, maturing market). If true, simple averaging underestimates future cycle lengths.
⚠ SELF-FULFILLING PROPHECY RISK
The halving narrative may be partially circular - it works because people believe it works. Sufficient changes in market structure or participant behavior can invalidate the pattern.
⚠ APPROXIMATE DATA
Historical prices rounded to significant figures. Exact bottom/top dates vary by exchange. Month-long ranges are used for simplicity.
EDUCATIONAL USE ONLY
This indicator is designed for historical analysis and understanding Bitcoin's past behavior. It is NOT:
- Trading advice or financial recommendations
- A guarantee or prediction of future price movements
- Suitable as a sole basis for investment decisions
- A replacement for fundamental or technical analysis
The projections show "what if the pattern continues exactly" - not "what will happen."
Always conduct independent research, understand the risks, and consult qualified financial advisors before making investment decisions. Only invest what you can afford to lose.
Futures Forward Price [NeoButane]In futures markets, the theoretical value of a futures contract can be derived from its underlying price and cost of carry. By baking in the costs and potential yields, the theoretical forward price then be used in basis against futures prices in place of the underlying spot price.
Usage
The script creates plots on the main chart and a separate window pane. Both are meant to be used to visualize dislocations in the market.
By using a futures vs. forward basis instead of futures vs. spot basis, discounts in the market are clearer.
Last month, the gold futures market GCZ2025 traded >1% above forward price when tariffs were announced and fell back in line once the tariffs were verbally retracted.
View roll spreads over a back-adjusted continuous chart. I guess. I don't think spread traders only look at one chart. This is as educational for me as it is you.
Configuration
The underlying reference needs to be changed to match the futures contract you are using.
The Risk-Free Rate defaults to FRED:SOFR. I found the contract month matched 3-Month SOFR Futures to be the closest for forward price.
Risk-Free Rate: The interest rate source for forward price.
Constant Risk-Free Rate: a static interest rate that can be used in advance of future changes in risk-free rate.
Underlying Reference: spot or index price. Some examples include TVC:SPX, TVC:GOLD, CRYPTO:BTCUSD, TVC:USOIL.
Forward Price Compounding: determines which formula to use. They're similar and become closer as the contract matures.
Alternative Contract: enable and select a futures contract to use it on a chart different than the main.
Storage Cost and Yield: for use with commodities. I haven't found a proper use for them yet but enabling is simple if you are able to.
The following are meant to be used with the continuous formula as they are compounded. However the rate sources don't differ much for the purpose of futures prices.
3-Month CME SOFR Futures
3-Month ICEEUR SONIA Futures
3-Month Osaka TONA Futures
The other rate sources are either meant for futures contracts shorter than quarterly such as monthly crypto futures or were meant to help myself understand how different rates would align with futures prices, like inflation.
What this script does
It uses the cost of carry formula to output the forward price (red line). The underlying reference (green line) is plotted alongside and a futures-derived reference (blue line) can be displayed to see how it looks next to the real reference price.
The data pane displays either the nominal difference or percentage difference between the real futures price and the calculated forward price.
Further reading
www.investopedia.com
www.cmegroup.com
www.oxfordenergy.org
www-2.rotman.utoronto.ca
www.cmegroup.com
3-month rate futures
www.cmegroup.com
www.ice.com
www.bankofengland.co.uk
www.jpx.co.jp
DNSE VN301!, ADX Momentum StrategyDiscover the tailored Pine Script for trading VN30F1M Futures Contracts intraday.
This strategy applies the Statistical Method (IQR) to break down the components of the ADX, calculating the threshold of "normal" momentum fluctuations in price to identify potential breakouts for entry and exit signals. The script automatically closes all positions by 14:30 to avoid overnight holdings.
www.tradingview.com
Settings & Backtest Results:
- Chart: 30-minute timeframe
- Initial capital: VND 100 million
- Position size: 4 contracts per trade (includes trading fees, excludes tax)
- Backtest period: Sep-2021 to Sep-2025
- Return: over 270% (with 5 ticks slippage)
- Trades executed: 1,000+
- Win rate: ~40%
- Profit factor: 1.2
Default Script Settings:
Calculates the acceleration of changes in the +DI and -DI components of the ADX, using IQR to define "normal" momentum fluctuations (adjustable via Lookback period).
Calculates the difference between each bar’s Open and Close prices, using IQR to define "normal" gaps (adjustable via Lookback period).
Entry & Exit Conditions:
Entry Long: Change in +DI or -DI > Avg IQR Value AND Close Price > Previous Close
Exit Long: (all 4 conditions must be met)
- Change in +DI or -DI > Avg IQR Value
- RSI < Previous RSI
- Close–Open Gap > Avg IQR Gap
- Close Price < Previous Close
Entry Short: Change in +DI or -DI > Avg IQR Value AND Close Price < Previous Close
Exit Short: (all 4 conditions must be met)
- Change in +DI or -DI > Avg IQR Value
- RSI > Previous RSI
- Close–Open Gap > Avg IQR Gap
- Close Price > Previous Close
Disclaimers:
Trading futures contracts carries a high degree of risk, and price movements can be highly volatile. This script is intended as a reference tool only. It should be used by individuals who fully understand futures trading, have assessed their own risk tolerance, and are knowledgeable about the strategy’s logic.
All investment decisions are the sole responsibility of the user. DNSE bears no liability for any potential losses incurred from applying this strategy in real trading. Past performance does not guarantee future results. Please contact us directly if you have specific questions about this script.
IU Trade ManagementDESCRIPTION
IU Trade Management is a powerful utility tool designed to help traders manage their trades with precision and clarity. It provides automated Stop Loss, Take Profit, and Break Even calculations using multiple customizable methods. Along with clear SL/TP plotting on the chart, it also displays a detailed trade status table that tracks every important detail including entry price, SL/TP levels, break-even, PNL, and trade duration. This tool is perfect for traders who want to manage risk and rewards visually and systematically.
USER INPUTS :
-Entry Candle Time: Default 20 Jul 2021 00:00 +0300 (select the candle from which the trade begins)
- Entry Price: Default 2333 (define the price at which the trade is executed)
- Trade Direction: Default Long (choose between Long or Short)
- SL/TP Method: Default ATR (options: ATR, Points/Pips, Percentage %, Standard Deviation, Highest/Lowest, Previous High/Low)
- Risk to Reward: Default 3 (set custom risk-to-reward ratio)
- Use Break Even: Default false (option to enable break-even)
- Plot Break Even Line: Default false (option to display BE line)
- RTR of Break Even Point: Default 2 (factor used for BE calculation)
SL/TP Method Specific Inputs:
- ATR Length: Default 14
- ATR Factor: Default 2
- Points/Pips: Default 100
- Percentage: Default 1%
- Standard Deviation Length: Default 20
- Standard Deviation Factor: Default 2
- Highest/Lowest Length: Default 10
Trade Status Table Settings:
- Show Trade Status: Default true
- Table Size: Default small (options: normal, tiny, small, large)
- Table Position: Default top right
- Frame Width: Default 2
- Table Color: Default black
- Frame Color: Default gray
- Border Width: Default 2
- Border Color: Default gray
- Text Color: Default purple (RGB 212, 0, 255)
HOW TO USE THE INDICATOR:
1. Set the entry candle time and entry price manually.
2. Select whether the trade is Long or Short.
3. Choose the preferred SL/TP calculation method (ATR, Percentage, Points, STD, High/Low, Previous High/Low).
4. Define your risk-to-reward ratio and enable break-even if required.
5. The indicator will automatically plot your Entry, Stop Loss, Take Profit, and Break Even levels on the chart.
6. A detailed trade management table will appear, showing trade direction, SL, TP, PNL (points and %), SL/TP method, and total trade time.
WHY IT IS UNIQUE:
- Offers multiple methods to calculate SL and TP (ATR, Percentage, Points, Standard Deviation, High/Low, Previous High/Low)
- Built-in Break Even functionality for risk-free trade management
- Real-time PNL tracking in both points and percentage
- Trade status table for complete transparency on all trade details
- Visual plotting of SL, TP, and Entry with color-coded zones for clarity
HOW USER CAN BENEFIT FROM IT :
- Helps traders manage risk and reward with discipline
- Eliminates guesswork by automating SL and TP levels
- Provides clear visual guidance on trade exits and risk management
- Enhances decision-making with live trade tracking and performance statistics
- Suitable for manual traders as a trade manager and for strategy developers as a risk management reference
Bitcoin Power Law with Cycle BandsBitcoin Power Law with Cycle Bands DescriptionUnlock the power of Bitcoin’s long-term trends with the Bitcoin Power Law with Cycle Bands script, exclusively available through Bitcoin Wealth Edge! This custom TradingView indicator, built for Pine Script v6, models Bitcoin’s price behavior using a 96% R² power law trendline, derived from days since its genesis (January 3, 2009). Designed to predict cycle tops and bottoms, it features:Power Law Trendline: A cyan line representing fair value (e.g., ~$111,000 as of September 2025), based on a logarithmic regression with adjustable coefficients (a = -17.02, b = 5.83).
Cycle Bands: Adjustable red (upper) and green (lower) bands, defaulting to 3.5x and -3.5x multipliers, aligning with historical peaks (e.g., $69K in 2021) and troughs (e.g., $16K in 2022).
Dynamic Labels: Real-time labels displaying fair value, upper limit ($180K), and lower limit ($40K), updated on the last bar for quick insights.
Follow @HodlerRanch
for updates!
FNGAdataDates_Part2FNGAdataDates_Part2 provides the second part of historical trading dates for a financial instrument (e.g., FNGA index or related asset), covering approximately mid-2021 to January 22, 2018, with 896 trading days. The dates are organized into 18 chunks (dates_19 to dates_36), with 50 dates per chunk for 19–35 and 46 dates for chunk 36 (excluding weekends and possibly holidays). This library complements FNGAdataDates_Part1 to complete the 1,846-date dataset and is designed to align with the FNGAopenPrices and FNGAclosePrices libraries for backtesting, analysis, or visualization in Pine Script.
FNGAdataDates_Part1FNGAdataDates_Part1 provides historical trading dates for a financial instrument (e.g., FNGA index or related asset) from May 23, 2025, to approximately mid-2021, covering 950 trading days. The dates are organized into 19 chunks (dates_0 to dates_18), each containing 50 timestamps representing trading days (excluding weekends and possibly holidays). This library is part one of a two-part set due to Pine Script token limits and must be used with FNGAdataDates_Part2 for the complete dataset (1,846 dates). It is designed to align with the FNGAopenPrices and FNGAclosePrices libraries for backtesting, technical analysis, or visualization in Pine Script.
XMR Divergences vs KrakenSUMMARY
This script finds the percentage difference between Kraken, and multiple other exchanges, for the price of XMRUSD, and then runs a variable length moving average of those differences. Optionally, you can multiply by the reported volume of the exchange in question. Skip to "USAGE" at the bottom for a quick view of the settings. But I recommend reading DETAILED DESCRIPTION as well.
PURPOSE
The purpose of this script is to get a look into the relative funds flows of XMR between Kraken and the other exchanges. So long as an exchange withdraws are open: 1) Negative divergences indicate XMR outflows from the exchange under consideration, 2) Postive divergences indicate XMR inflows from Kraken to the exchange.
This appears to be moderately correlated with price movements in Monero (but not always). There is also the theory that positive accumulation is a leading indication of a growing probability of postive price action in the general crypto market, and negative accumulation is a leading indicator of an upcoming peak. In other words, exchanges like to accumulate Monero quietly during calm downtimes, and they like to manage its price from gaining too much attention (pump) during broad market positivity.
BACKGROUND
It's well known among XMR traders that most exchanges are operating on a heavy fractional reserve basis as regards Monero. The past 2 years have seen regular and repeated withdraw freezes, sometimes for weeks/months at a time. Occasionally, liquidity stress tests have been performed, with predictable results - none of these exchanges are able to continue supporting withdraws.
Kraken is the only exchange of meaningful volume that has never frozen withdraws for more than an hour or so. Thus, we theorize that Kraken is operating with all, or most of the XMR they claim to have.
Furthermore, we have seen in the past, large price negative price divergences of these fractional reserve exchanges relative to Kraken. As the social outcry grew stronger for this malfeasance, these exchanges have gone to greater lengths to hide their price divergences.
On minute-by-minute ; hour-by-hour basis, typically, a look with the naked eye would show oscillation around the zero point. But when you average it out, especially on lower timeframes (like the 1 and 5 min candles), you can very clearly see that when withdraws are shut down, these exchanges simultaneously diverge their prices downwards as well.
DETAILED DESCRIPTION
The ideal view of price divergence would compare second-by-second prices, and then run something like a rolling 4-hr or 1-day SMA to average out the overall divergences. However, due to limitations of TradingView, this is impractical/impossible for actual usage/viewing. As a result, a balance must be struck, when selecting the combination of the candle period, and the SMA lookback length.
I find that 5min candles, with a 48-period lookback (that equates to a rolling 4-hour SMA), offers the best view of recent and historical price divergence activity. This of course means that we're only sampling price divergences once every 5 minutes, but it still provides a decent look at what's happening. If this script gets popular, I wouldn't be surprised if these exchanges start timing their candle closes to mask their misdeeds, but that's of course speculative on my part.
The other important factor here, *IS TO MULTIPLY BY VOLUME*. Some of these no-volume exchanges have large price divergences. But if they're not doing any real volume, then it doesn't really have any real market impact. Thus, I recommend keeping the "Make volume adjustment" option on.
If that ends up happening, we'll have to infer that by comparing the difference in close prices, vs the difference in the highest or lowest intra-candle prices (wicks). Typically a divergence should have all 3 showing similar results.
Notes regarding "Sum_of_All": This only makes sense when multiplying by volume. So only check this if you also made the volume adjustment. Generally I believe that *Binance* sets the tone. However, we have seen numerous occasions where Binance diverges down, and the others diverge up. I believe this is a social influence tactic, since most people look at Binance price. Meanwhile, they're trying to accumulate some small amount on the other exchanges to minimize their overall loss. This of course assumes collusion by these exchanges, which is a high likely hood, seeing as how in May 2021, they all diverged together simultaneously (among other evidence).
USAGE
I recommend using your browser zoom, to see data beyond 1 month in the past.
Lookback - The number of candles over which to conduct a moving average. On 5-min candles for example, here's how the math works out:
12 - Equates to a 1 hr MA
24 - 2 hrs
48 - 4 hrs (default)
288 - 1 day
2880 - 10 days
Make Volume Adjustment - Recommend that you usually keep this on.
Line Widths - Set to preference
Show_Close_Price? - You can compute the difference at candle close. Or you can check the other boxes to compare the highest/lowest prices for intra candle prices (wicks).
Show Sum_of_All? - You can sum all of the differences, which only makes sense if you're making the volume adjustement. Default is off. Below, you can also choose which exchanges to include in the sum.
This works best on lower timeframes, like the 1m, 5, and 15m charts. I personally use 5m, with 48 or 96 length lookback. You get a better view of the real time price divergences that way.
Ray Dalio's All Weather Strategy - Portfolio CalculatorTHE ALL WEATHER STRATEGY INDICATOR: A GUIDE TO RAY DALIO'S LEGENDARY PORTFOLIO APPROACH
Introduction: The Genesis of Financial Resilience
In the sprawling corridors of Bridgewater Associates, the world's largest hedge fund managing over 150 billion dollars in assets, Ray Dalio conceived what would become one of the most influential investment strategies of the modern era. The All Weather Strategy, born from decades of market observation and rigorous backtesting, represents a paradigm shift from traditional portfolio construction methods that have dominated Wall Street since Harry Markowitz's seminal work on Modern Portfolio Theory in 1952.
Unlike conventional approaches that chase returns through market timing or stock picking, the All Weather Strategy embraces a fundamental truth that has humbled countless investors throughout history: nobody can consistently predict the future direction of markets. Instead of fighting this uncertainty, Dalio's approach harnesses it, creating a portfolio designed to perform reasonably well across all economic environments, hence the evocative name "All Weather."
The strategy emerged from Bridgewater's extensive research into economic cycles and asset class behavior, culminating in what Dalio describes as "the Holy Grail of investing" in his bestselling book "Principles" (Dalio, 2017). This Holy Grail isn't about achieving spectacular returns, but rather about achieving consistent, risk-adjusted returns that compound steadily over time, much like the tortoise defeating the hare in Aesop's timeless fable.
HISTORICAL DEVELOPMENT AND EVOLUTION
The All Weather Strategy's origins trace back to the tumultuous economic periods of the 1970s and 1980s, when traditional portfolio construction methods proved inadequate for navigating simultaneous inflation and recession. Raymond Thomas Dalio, born in 1949 in Queens, New York, founded Bridgewater Associates from his Manhattan apartment in 1975, initially focusing on currency and fixed-income consulting for corporate clients.
Dalio's early experiences during the 1970s stagflation period profoundly shaped his investment philosophy. Unlike many of his contemporaries who viewed inflation and deflation as opposing forces, Dalio recognized that both conditions could coexist with either economic growth or contraction, creating four distinct economic environments rather than the traditional two-factor models that dominated academic finance.
The conceptual breakthrough came in the late 1980s when Dalio began systematically analyzing asset class performance across different economic regimes. Working with a small team of researchers, Bridgewater developed sophisticated models that decomposed economic conditions into growth and inflation components, then mapped historical asset class returns against these regimes. This research revealed that traditional portfolio construction, heavily weighted toward stocks and bonds, left investors vulnerable to specific economic scenarios.
The formal All Weather Strategy emerged in 1996 when Bridgewater was approached by a wealthy family seeking a portfolio that could protect their wealth across various economic conditions without requiring active management or market timing. Unlike Bridgewater's flagship Pure Alpha fund, which relied on active trading and leverage, the All Weather approach needed to be completely passive and unleveraged while still providing adequate diversification.
Dalio and his team spent months developing and testing various allocation schemes, ultimately settling on the 30/40/15/7.5/7.5 framework that balances risk contributions rather than dollar amounts. This approach was revolutionary because it focused on risk budgeting—ensuring that no single asset class dominated the portfolio's risk profile—rather than the traditional approach of equal dollar allocations or market-cap weighting.
The strategy's first institutional implementation began in 1996 with a family office client, followed by gradual expansion to other wealthy families and eventually institutional investors. By 2005, Bridgewater was managing over $15 billion in All Weather assets, making it one of the largest systematic strategy implementations in institutional investing.
The 2008 financial crisis provided the ultimate test of the All Weather methodology. While the S&P 500 declined by 37% and many hedge funds suffered double-digit losses, the All Weather strategy generated positive returns, validating Dalio's risk-balancing approach. This performance during extreme market stress attracted significant institutional attention, leading to rapid asset growth in subsequent years.
The strategy's theoretical foundations evolved throughout the 2000s as Bridgewater's research team, led by co-chief investment officers Greg Jensen and Bob Prince, refined the economic framework and incorporated insights from behavioral economics and complexity theory. Their research, published in numerous institutional white papers, demonstrated that traditional portfolio optimization methods consistently underperformed simpler risk-balanced approaches across various time periods and market conditions.
Academic validation came through partnerships with leading business schools and collaboration with prominent economists. The strategy's risk parity principles influenced an entire generation of institutional investors, leading to the creation of numerous risk parity funds managing hundreds of billions in aggregate assets.
In recent years, the democratization of sophisticated financial tools has made All Weather-style investing accessible to individual investors through ETFs and systematic platforms. The availability of high-quality, low-cost ETFs covering each required asset class has eliminated many of the barriers that previously limited sophisticated portfolio construction to institutional investors.
The development of advanced portfolio management software and platforms like TradingView has further democratized access to institutional-quality analytics and implementation tools. The All Weather Strategy Indicator represents the culmination of this trend, providing individual investors with capabilities that previously required teams of portfolio managers and risk analysts.
Understanding the Four Economic Seasons
The All Weather Strategy's theoretical foundation rests on Dalio's observation that all economic environments can be characterized by two primary variables: economic growth and inflation. These variables create four distinct "economic seasons," each favoring different asset classes. Rising growth benefits stocks and commodities, while falling growth favors bonds. Rising inflation helps commodities and inflation-protected securities, while falling inflation benefits nominal bonds and stocks.
This framework, detailed extensively in Bridgewater's research papers from the 1990s, suggests that by holding assets that perform well in each economic season, an investor can create a portfolio that remains resilient regardless of which season unfolds. The elegance lies not in predicting which season will occur, but in being prepared for all of them simultaneously.
Academic research supports this multi-environment approach. Ang and Bekaert (2002) demonstrated that regime changes in economic conditions significantly impact asset returns, while Fama and French (2004) showed that different asset classes exhibit varying sensitivities to economic factors. The All Weather Strategy essentially operationalizes these academic insights into a practical investment framework.
The Original All Weather Allocation: Simplicity Masquerading as Sophistication
The core All Weather portfolio, as implemented by Bridgewater for institutional clients and later adapted for retail investors, maintains a deceptively simple static allocation: 30% stocks, 40% long-term bonds, 15% intermediate-term bonds, 7.5% commodities, and 7.5% Treasury Inflation-Protected Securities (TIPS). This allocation may appear arbitrary to the uninitiated, but each percentage reflects careful consideration of historical volatilities, correlations, and economic sensitivities.
The 30% stock allocation provides growth exposure while limiting the portfolio's overall volatility. Stocks historically deliver superior long-term returns but with significant volatility, as evidenced by the Standard & Poor's 500 Index's average annual return of approximately 10% since 1926, accompanied by standard deviation exceeding 15% (Ibbotson Associates, 2023). By limiting stock exposure to 30%, the portfolio captures much of the equity risk premium while avoiding excessive volatility.
The combined 55% allocation to bonds (40% long-term plus 15% intermediate-term) serves as the portfolio's stabilizing force. Long-term bonds provide substantial interest rate sensitivity, performing well during economic slowdowns when central banks reduce rates. Intermediate-term bonds offer a balance between interest rate sensitivity and reduced duration risk. This bond-heavy allocation reflects Dalio's insight that bonds typically exhibit lower volatility than stocks while providing essential diversification benefits.
The 7.5% commodities allocation addresses inflation protection, as commodity prices typically rise during inflationary periods. Historical analysis by Bodie and Rosansky (1980) demonstrated that commodities provide meaningful diversification benefits and inflation hedging capabilities, though with considerable volatility. The relatively small allocation reflects commodities' high volatility and mixed long-term returns.
Finally, the 7.5% TIPS allocation provides explicit inflation protection through government-backed securities whose principal and interest payments adjust with inflation. Introduced by the U.S. Treasury in 1997, TIPS have proven effective inflation hedges, though they underperform nominal bonds during deflationary periods (Campbell & Viceira, 2001).
Historical Performance: The Evidence Speaks
Analyzing the All Weather Strategy's historical performance reveals both its strengths and limitations. Using monthly return data from 1970 to 2023, spanning over five decades of varying economic conditions, the strategy has delivered compelling risk-adjusted returns while experiencing lower volatility than traditional stock-heavy portfolios.
During this period, the All Weather allocation generated an average annual return of approximately 8.2%, compared to 10.5% for the S&P 500 Index. However, the strategy's annual volatility measured just 9.1%, substantially lower than the S&P 500's 15.8% volatility. This translated to a Sharpe ratio of 0.67 for the All Weather Strategy versus 0.54 for the S&P 500, indicating superior risk-adjusted performance.
More impressively, the strategy's maximum drawdown over this period was 12.3%, occurring during the 2008 financial crisis, compared to the S&P 500's maximum drawdown of 50.9% during the same period. This drawdown mitigation proves crucial for long-term wealth building, as Stein and DeMuth (2003) demonstrated that avoiding large losses significantly impacts compound returns over time.
The strategy performed particularly well during periods of economic stress. During the 1970s stagflation, when stocks and bonds both struggled, the All Weather portfolio's commodity and TIPS allocations provided essential protection. Similarly, during the 2000-2002 dot-com crash and the 2008 financial crisis, the portfolio's bond-heavy allocation cushioned losses while maintaining positive returns in several years when stocks declined significantly.
However, the strategy underperformed during sustained bull markets, particularly the 1990s technology boom and the 2010s post-financial crisis recovery. This underperformance reflects the strategy's conservative nature and diversified approach, which sacrifices potential upside for downside protection. As Dalio frequently emphasizes, the All Weather Strategy prioritizes "not losing money" over "making a lot of money."
Implementing the All Weather Strategy: A Practical Guide
The All Weather Strategy Indicator transforms Dalio's institutional-grade approach into an accessible tool for individual investors. The indicator provides real-time portfolio tracking, rebalancing signals, and performance analytics, eliminating much of the complexity traditionally associated with implementing sophisticated allocation strategies.
To begin implementation, investors must first determine their investable capital. As detailed analysis reveals, the All Weather Strategy requires meaningful capital to implement effectively due to transaction costs, minimum investment requirements, and the need for precise allocations across five different asset classes.
For portfolios below $50,000, the strategy becomes challenging to implement efficiently. Transaction costs consume a disproportionate share of returns, while the inability to purchase fractional shares creates allocation drift. Consider an investor with $25,000 attempting to allocate 7.5% to commodities through the iPath Bloomberg Commodity Index ETF (DJP), currently trading around $25 per share. This allocation targets $1,875, enough for only 75 shares, creating immediate tracking error.
At $50,000, implementation becomes feasible but not optimal. The 30% stock allocation ($15,000) purchases approximately 37 shares of the SPDR S&P 500 ETF (SPY) at current prices around $400 per share. The 40% long-term bond allocation ($20,000) buys 200 shares of the iShares 20+ Year Treasury Bond ETF (TLT) at approximately $100 per share. While workable, these allocations leave significant cash drag and rebalancing challenges.
The optimal minimum for individual implementation appears to be $100,000. At this level, each allocation becomes substantial enough for precise implementation while keeping transaction costs below 0.4% annually. The $30,000 stock allocation, $40,000 long-term bond allocation, $15,000 intermediate-term bond allocation, $7,500 commodity allocation, and $7,500 TIPS allocation each provide sufficient size for effective management.
For investors with $250,000 or more, the strategy implementation approaches institutional quality. Allocation precision improves, transaction costs decline as a percentage of assets, and rebalancing becomes highly efficient. These larger portfolios can also consider adding complexity through international diversification or alternative implementations.
The indicator recommends quarterly rebalancing to balance transaction costs with allocation discipline. Monthly rebalancing increases costs without substantial benefits for most investors, while annual rebalancing allows excessive drift that can meaningfully impact performance. Quarterly rebalancing, typically on the first trading day of each quarter, provides an optimal balance.
Understanding the Indicator's Functionality
The All Weather Strategy Indicator operates as a comprehensive portfolio management system, providing multiple analytical layers that professional money managers typically reserve for institutional clients. This sophisticated tool transforms Ray Dalio's institutional-grade strategy into an accessible platform for individual investors, offering features that rival professional portfolio management software.
The indicator's core architecture consists of several interconnected modules that work seamlessly together to provide complete portfolio oversight. At its foundation lies a real-time portfolio simulation engine that tracks the exact value of each ETF position based on current market prices, eliminating the need for manual calculations or external spreadsheets.
DETAILED INDICATOR COMPONENTS AND FUNCTIONS
Portfolio Configuration Module
The portfolio setup begins with the Portfolio Configuration section, which establishes the fundamental parameters for strategy implementation. The Portfolio Capital input accepts values from $1,000 to $10,000,000, accommodating everyone from beginning investors to institutional clients. This input directly drives all subsequent calculations, determining exact share quantities and portfolio values throughout the implementation period.
The Portfolio Start Date function allows users to specify when they began implementing the All Weather Strategy, creating a clear demarcation point for performance tracking. This feature proves essential for investors who want to track their actual implementation against theoretical performance, providing realistic assessment of strategy effectiveness including timing differences and implementation costs.
Rebalancing Frequency settings offer two options: Monthly and Quarterly. While monthly rebalancing provides more precise allocation control, quarterly rebalancing typically proves more cost-effective for most investors due to reduced transaction costs. The indicator automatically detects the first trading day of each period, ensuring rebalancing occurs at optimal times regardless of weekends, holidays, or market closures.
The Rebalancing Threshold parameter, adjustable from 0.5% to 10%, determines when allocation drift triggers rebalancing recommendations. Conservative settings like 1-2% maintain tight allocation control but increase trading frequency, while wider thresholds like 3-5% reduce trading costs but allow greater allocation drift. This flexibility accommodates different risk tolerances and cost structures.
Visual Display System
The Show All Weather Calculator toggle controls the main dashboard visibility, allowing users to focus on chart visualization when detailed metrics aren't needed. When enabled, this comprehensive dashboard displays current portfolio value, individual ETF allocations, target versus actual weights, rebalancing status, and performance metrics in a professionally formatted table.
Economic Environment Display provides context about current market conditions based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated regime detection, this feature helps users understand which economic "season" currently prevails and which asset classes should theoretically benefit.
Rebalancing Signals illuminate when portfolio drift exceeds user-defined thresholds, highlighting specific ETFs that require adjustment. These signals use color coding to indicate urgency: green for balanced allocations, yellow for moderate drift, and red for significant deviations requiring immediate attention.
Advanced Label System
The rebalancing label system represents one of the indicator's most innovative features, providing three distinct detail levels to accommodate different user needs and experience levels. The "None" setting displays simple symbols marking portfolio start and rebalancing events without cluttering the chart with text. This minimal approach suits experienced investors who understand the implications of each symbol.
"Basic" label mode shows essential information including portfolio values at each rebalancing point, enabling quick assessment of strategy performance over time. These labels display "START $X" for portfolio initiation and "RBL $Y" for rebalancing events, providing clear performance tracking without overwhelming detail.
"Detailed" labels provide comprehensive trading instructions including exact buy and sell quantities for each ETF. These labels might display "RBL $125,000 BUY 15 SPY SELL 25 TLT BUY 8 IEF NO TRADES DJP SELL 12 SCHP" providing complete implementation guidance. This feature essentially transforms the indicator into a personal portfolio manager, eliminating guesswork about exact trades required.
Professional Color Themes
Eight professionally designed color themes adapt the indicator's appearance to different aesthetic preferences and market analysis styles. The "Gold" theme reflects traditional wealth management aesthetics, while "EdgeTools" provides modern professional appearance. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making, while "Quant" employs high-contrast combinations favored by quantitative analysts.
"Ocean," "Fire," "Matrix," and "Arctic" themes provide distinctive visual identities for traders who prefer unique chart aesthetics. Each theme automatically adjusts for dark or light mode optimization, ensuring optimal readability across different TradingView configurations.
Real-Time Portfolio Tracking
The portfolio simulation engine continuously tracks five separate ETF positions: SPY for stocks, TLT for long-term bonds, IEF for intermediate-term bonds, DJP for commodities, and SCHP for TIPS. Each position's value updates in real-time based on current market prices, providing instant feedback about portfolio performance and allocation drift.
Current share calculations determine exact holdings based on the most recent rebalancing, while target shares reflect optimal allocation based on current portfolio value. Trade calculations show precisely how many shares to buy or sell during rebalancing, eliminating manual calculations and potential errors.
Performance Analytics Suite
The indicator's performance measurement capabilities rival professional portfolio analysis software. Sharpe ratio calculations incorporate current risk-free rates obtained from Treasury yield data, providing accurate risk-adjusted performance assessment. Volatility measurements use rolling periods to capture changing market conditions while maintaining statistical significance.
Portfolio return calculations track both absolute and relative performance, comparing the All Weather implementation against individual asset classes and benchmark indices. These metrics update continuously, providing real-time assessment of strategy effectiveness and implementation quality.
Data Quality Monitoring
Sophisticated data quality checks ensure reliable indicator operation across different market conditions and potential data interruptions. The system monitors all five ETF price feeds plus economic data sources, providing quality scores that alert users to potential data issues that might affect calculations.
When data quality degrades, the indicator automatically switches to fallback values or alternative data sources, maintaining functionality during temporary market data interruptions. This robust design ensures consistent operation even during volatile market conditions when data feeds occasionally experience disruptions.
Risk Management and Behavioral Considerations
Despite its sophisticated design, the All Weather Strategy faces behavioral challenges that have derailed countless well-intentioned investment plans. The strategy's conservative nature means it will underperform growth stocks during bull markets, potentially by substantial margins. Maintaining discipline during these periods requires understanding that the strategy optimizes for risk-adjusted returns over absolute returns.
Behavioral finance research by Kahneman and Tversky (1979) demonstrates that investors feel losses approximately twice as intensely as equivalent gains. This loss aversion creates powerful psychological pressure to abandon defensive strategies during bull markets when aggressive portfolios appear more attractive. The All Weather Strategy's bond-heavy allocation will seem overly conservative when technology stocks double in value, as occurred repeatedly during the 2010s.
Conversely, the strategy's defensive characteristics provide psychological comfort during market stress. When stocks crash 30-50%, as they periodically do, the All Weather portfolio's modest losses feel manageable rather than catastrophic. This emotional stability enables investors to maintain their investment discipline when others capitulate, often at the worst possible times.
Rebalancing discipline presents another behavioral challenge. Selling winners to buy losers contradicts natural human tendencies but remains essential for the strategy's success. When stocks have outperformed bonds for several quarters, rebalancing requires selling high-performing stock positions to purchase seemingly stagnant bond positions. This action feels counterintuitive but captures the strategy's systematic approach to risk management.
Tax considerations add complexity for taxable accounts. Frequent rebalancing generates taxable events that can erode after-tax returns, particularly for high-income investors facing elevated capital gains rates. Tax-advantaged accounts like 401(k)s and IRAs provide ideal vehicles for All Weather implementation, eliminating tax friction from rebalancing activities.
Capital Requirements and Cost Analysis
Comprehensive cost analysis reveals the capital requirements for effective All Weather implementation. Annual expenses include management fees for each ETF, transaction costs from rebalancing, and bid-ask spreads from trading less liquid securities.
ETF expense ratios vary significantly across asset classes. The SPDR S&P 500 ETF charges 0.09% annually, while the iShares 20+ Year Treasury Bond ETF charges 0.20%. The iShares 7-10 Year Treasury Bond ETF charges 0.15%, the Schwab US TIPS ETF charges 0.05%, and the iPath Bloomberg Commodity Index ETF charges 0.75%. Weighted by the All Weather allocations, total expense ratios average approximately 0.19% annually.
Transaction costs depend heavily on broker selection and account size. Premium brokers like Interactive Brokers charge $1-2 per trade, resulting in $20-40 annually for quarterly rebalancing. Discount brokers may charge higher per-trade fees but offer commission-free ETF trading for selected funds. Zero-commission brokers eliminate explicit trading costs but often impose wider bid-ask spreads that function as hidden fees.
Bid-ask spreads represent the difference between buying and selling prices for each security. Highly liquid ETFs like SPY maintain spreads of 1-2 basis points, while less liquid commodity ETFs may exhibit spreads of 5-10 basis points. These costs accumulate through rebalancing activities, typically totaling 10-15 basis points annually.
For a $100,000 portfolio, total annual costs including expense ratios, transaction fees, and spreads typically range from 0.35% to 0.45%, or $350-450 annually. These costs decline as a percentage of assets as portfolio size increases, reaching approximately 0.25% for portfolios exceeding $250,000.
Comparing costs to potential benefits reveals the strategy's value proposition. Historical analysis suggests the All Weather approach reduces portfolio volatility by 35-40% compared to stock-heavy allocations while maintaining competitive returns. This volatility reduction provides substantial value during market stress, potentially preventing behavioral mistakes that destroy long-term wealth.
Alternative Implementations and Customizations
While the original All Weather allocation provides an excellent starting point, investors may consider modifications based on personal circumstances, market conditions, or geographic considerations. International diversification represents one potential enhancement, adding exposure to developed and emerging market bonds and equities.
Geographic customization becomes important for non-US investors. European investors might replace US Treasury bonds with German Bunds or broader European government bond indices. Currency hedging decisions add complexity but may reduce volatility for investors whose spending occurs in non-dollar currencies.
Tax-location strategies optimize after-tax returns by placing tax-inefficient assets in tax-advantaged accounts while holding tax-efficient assets in taxable accounts. TIPS and commodity ETFs generate ordinary income taxed at higher rates, making them candidates for retirement account placement. Stock ETFs generate qualified dividends and long-term capital gains taxed at lower rates, making them suitable for taxable accounts.
Some investors prefer implementing the bond allocation through individual Treasury securities rather than ETFs, eliminating management fees while gaining precise maturity control. Treasury auctions provide access to new securities without bid-ask spreads, though this approach requires more sophisticated portfolio management.
Factor-based implementations replace broad market ETFs with factor-tilted alternatives. Value-tilted stock ETFs, quality-focused bond ETFs, or momentum-based commodity indices may enhance returns while maintaining the All Weather framework's diversification benefits. However, these modifications introduce additional complexity and potential tracking error.
Conclusion: Embracing the Long Game
The All Weather Strategy represents more than an investment approach; it embodies a philosophy of financial resilience that prioritizes sustainable wealth building over speculative gains. In an investment landscape increasingly dominated by algorithmic trading, meme stocks, and cryptocurrency volatility, Dalio's methodical approach offers a refreshing alternative grounded in economic theory and historical evidence.
The strategy's greatest strength lies not in its potential for extraordinary returns, but in its capacity to deliver reasonable returns across diverse economic environments while protecting capital during market stress. This characteristic becomes increasingly valuable as investors approach or enter retirement, when portfolio preservation assumes greater importance than aggressive growth.
Implementation requires discipline, adequate capital, and realistic expectations. The strategy will underperform growth-oriented approaches during bull markets while providing superior downside protection during bear markets. Investors must embrace this trade-off consciously, understanding that the strategy optimizes for long-term wealth building rather than short-term performance.
The All Weather Strategy Indicator democratizes access to institutional-quality portfolio management, providing individual investors with tools previously available only to wealthy families and institutions. By automating allocation tracking, rebalancing signals, and performance analysis, the indicator removes much of the complexity that has historically limited sophisticated strategy implementation.
For investors seeking a systematic, evidence-based approach to long-term wealth building, the All Weather Strategy provides a compelling framework. Its emphasis on diversification, risk management, and behavioral discipline aligns with the fundamental principles that have created lasting wealth throughout financial history. While the strategy may not generate headlines or inspire cocktail party conversations, it offers something more valuable: a reliable path toward financial security across all economic seasons.
As Dalio himself notes, "The biggest mistake investors make is to believe that what happened in the recent past is likely to persist, and they design their portfolios accordingly." The All Weather Strategy's enduring appeal lies in its rejection of this recency bias, instead embracing the uncertainty of markets while positioning for success regardless of which economic season unfolds.
STEP-BY-STEP INDICATOR SETUP GUIDE
Setting up the All Weather Strategy Indicator requires careful attention to each configuration parameter to ensure optimal implementation. This comprehensive setup guide walks through every setting and explains its impact on strategy performance.
Initial Setup Process
Begin by adding the indicator to your TradingView chart. Search for "Ray Dalio's All Weather Strategy" in the indicator library and apply it to any chart. The indicator operates independently of the underlying chart symbol, drawing data directly from the five required ETFs regardless of which security appears on the chart.
Portfolio Configuration Settings
Start with the Portfolio Capital input, which drives all subsequent calculations. Enter your exact investable capital, ranging from $1,000 to $10,000,000. This input determines share quantities, trade recommendations, and performance calculations. Conservative recommendations suggest minimum capitals of $50,000 for basic implementation or $100,000 for optimal precision.
Select your Portfolio Start Date carefully, as this establishes the baseline for all performance calculations. Choose the date when you actually began implementing the All Weather Strategy, not when you first learned about it. This date should reflect when you first purchased ETFs according to the target allocation, creating realistic performance tracking.
Choose your Rebalancing Frequency based on your cost structure and precision preferences. Monthly rebalancing provides tighter allocation control but increases transaction costs. Quarterly rebalancing offers the optimal balance for most investors between allocation precision and cost control. The indicator automatically detects appropriate trading days regardless of your selection.
Set the Rebalancing Threshold based on your tolerance for allocation drift and transaction costs. Conservative investors preferring tight control should use 1-2% thresholds, while cost-conscious investors may prefer 3-5% thresholds. Lower thresholds maintain more precise allocations but trigger more frequent trading.
Display Configuration Options
Enable Show All Weather Calculator to display the comprehensive dashboard containing portfolio values, allocations, and performance metrics. This dashboard provides essential information for portfolio management and should remain enabled for most users.
Show Economic Environment displays current economic regime classification based on growth and inflation indicators. While simplified compared to Bridgewater's sophisticated models, this feature provides useful context for understanding current market conditions.
Show Rebalancing Signals highlights when portfolio allocations drift beyond your threshold settings. These signals use color coding to indicate urgency levels, helping prioritize rebalancing activities.
Advanced Label Customization
Configure Show Rebalancing Labels based on your need for chart annotations. These labels mark important portfolio events and can provide valuable historical context, though they may clutter charts during extended time periods.
Select appropriate Label Detail Levels based on your experience and information needs. "None" provides minimal symbols suitable for experienced users. "Basic" shows portfolio values at key events. "Detailed" provides complete trading instructions including exact share quantities for each ETF.
Appearance Customization
Choose Color Themes based on your aesthetic preferences and trading style. "Gold" reflects traditional wealth management appearance, while "EdgeTools" provides modern professional styling. "Behavioral" uses psychologically informed colors that reinforce disciplined decision-making.
Enable Dark Mode Optimization if using TradingView's dark theme for optimal readability and contrast. This setting automatically adjusts all colors and transparency levels for the selected theme.
Set Main Line Width based on your chart resolution and visual preferences. Higher width values provide clearer allocation lines but may overwhelm smaller charts. Most users prefer width settings of 2-3 for optimal visibility.
Troubleshooting Common Setup Issues
If the indicator displays "Data not available" messages, verify that all five ETFs (SPY, TLT, IEF, DJP, SCHP) have valid price data on your selected timeframe. The indicator requires daily data availability for all components.
When rebalancing signals seem inconsistent, check your threshold settings and ensure sufficient time has passed since the last rebalancing event. The indicator only triggers signals on designated rebalancing days (first trading day of each period) when drift exceeds threshold levels.
If labels appear at unexpected chart locations, verify that your chart displays percentage values rather than price values. The indicator forces percentage formatting and 0-40% scaling for optimal allocation visualization.
COMPREHENSIVE BIBLIOGRAPHY AND FURTHER READING
PRIMARY SOURCES AND RAY DALIO WORKS
Dalio, R. (2017). Principles: Life and work. New York: Simon & Schuster.
Dalio, R. (2018). A template for understanding big debt crises. Bridgewater Associates.
Dalio, R. (2021). Principles for dealing with the changing world order: Why nations succeed and fail. New York: Simon & Schuster.
BRIDGEWATER ASSOCIATES RESEARCH PAPERS
Jensen, G., Kertesz, A. & Prince, B. (2010). All Weather strategy: Bridgewater's approach to portfolio construction. Bridgewater Associates Research.
Prince, B. (2011). An in-depth look at the investment logic behind the All Weather strategy. Bridgewater Associates Daily Observations.
Bridgewater Associates. (2015). Risk parity in the context of larger portfolio construction. Institutional Research.
ACADEMIC RESEARCH ON RISK PARITY AND PORTFOLIO CONSTRUCTION
Ang, A. & Bekaert, G. (2002). International asset allocation with regime shifts. The Review of Financial Studies, 15(4), 1137-1187.
Bodie, Z. & Rosansky, V. I. (1980). Risk and return in commodity futures. Financial Analysts Journal, 36(3), 27-39.
Campbell, J. Y. & Viceira, L. M. (2001). Who should buy long-term bonds? American Economic Review, 91(1), 99-127.
Clarke, R., De Silva, H. & Thorley, S. (2013). Risk parity, maximum diversification, and minimum variance: An analytic perspective. Journal of Portfolio Management, 39(3), 39-53.
Fama, E. F. & French, K. R. (2004). The capital asset pricing model: Theory and evidence. Journal of Economic Perspectives, 18(3), 25-46.
BEHAVIORAL FINANCE AND IMPLEMENTATION CHALLENGES
Kahneman, D. & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-292.
Thaler, R. H. & Sunstein, C. R. (2008). Nudge: Improving decisions about health, wealth, and happiness. New Haven: Yale University Press.
Montier, J. (2007). Behavioural investing: A practitioner's guide to applying behavioural finance. Chichester: John Wiley & Sons.
MODERN PORTFOLIO THEORY AND QUANTITATIVE METHODS
Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77-91.
Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. The Journal of Finance, 19(3), 425-442.
Black, F. & Litterman, R. (1992). Global portfolio optimization. Financial Analysts Journal, 48(5), 28-43.
PRACTICAL IMPLEMENTATION AND ETF ANALYSIS
Gastineau, G. L. (2010). The exchange-traded funds manual. 2nd ed. Hoboken: John Wiley & Sons.
Poterba, J. M. & Shoven, J. B. (2002). Exchange-traded funds: A new investment option for taxable investors. American Economic Review, 92(2), 422-427.
Israelsen, C. L. (2005). A refinement to the Sharpe ratio and information ratio. Journal of Asset Management, 5(6), 423-427.
ECONOMIC CYCLE ANALYSIS AND ASSET CLASS RESEARCH
Ilmanen, A. (2011). Expected returns: An investor's guide to harvesting market rewards. Chichester: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering portfolio management: An unconventional approach to institutional investment. Rev. ed. New York: Free Press.
Siegel, J. J. (2014). Stocks for the long run: The definitive guide to financial market returns & long-term investment strategies. 5th ed. New York: McGraw-Hill Education.
RISK MANAGEMENT AND ALTERNATIVE STRATEGIES
Taleb, N. N. (2007). The black swan: The impact of the highly improbable. New York: Random House.
Lowenstein, R. (2000). When genius failed: The rise and fall of Long-Term Capital Management. New York: Random House.
Stein, D. M. & DeMuth, P. (2003). Systematic withdrawal from retirement portfolios: The impact of asset allocation decisions on portfolio longevity. AAII Journal, 25(7), 8-12.
CONTEMPORARY DEVELOPMENTS AND FUTURE DIRECTIONS
Asness, C. S., Frazzini, A. & Pedersen, L. H. (2012). Leverage aversion and risk parity. Financial Analysts Journal, 68(1), 47-59.
Roncalli, T. (2013). Introduction to risk parity and budgeting. Boca Raton: CRC Press.
Ibbotson Associates. (2023). Stocks, bonds, bills, and inflation 2023 yearbook. Chicago: Morningstar.
PERIODICALS AND ONGOING RESEARCH
Journal of Portfolio Management - Quarterly publication featuring cutting-edge research on portfolio construction and risk management
Financial Analysts Journal - Bi-monthly publication of the CFA Institute with practical investment research
Bridgewater Associates Daily Observations - Regular market commentary and research from the creators of the All Weather Strategy
RECOMMENDED READING SEQUENCE
For investors new to the All Weather Strategy, begin with Dalio's "Principles" for philosophical foundation, then proceed to the Bridgewater research papers for technical details. Supplement with Markowitz's original portfolio theory work and behavioral finance literature from Kahneman and Tversky.
Intermediate students should focus on academic papers by Ang & Bekaert on regime shifts, Clarke et al. on risk parity methods, and Ilmanen's comprehensive analysis of expected returns across asset classes.
Advanced practitioners will benefit from Roncalli's technical treatment of risk parity mathematics, Asness et al.'s academic critique of leverage aversion, and ongoing research in the Journal of Portfolio Management.
