True Opens - (SpeculatorBryan)Overview
This indicator provides a complete framework of key institutional levels by plotting the "True Open" price for the Month, Week, Day, and Intraday Sessions. Instead of using standard chart opens, it uses specific, globally significant times (based in the NY timezone) to identify levels that price action traders watch closely for support, resistance, and market direction.
What It Does
True Monthly Open (TMO): The key macro level, marking the start of the month's trading.
True Weekly Open (TWO): Arguably the most important level, defining the weekly bias. Based on the Sunday evening start of the forex trading week.
True Daily Open (TDO): The New York midnight open, marking the true start of the institutional 24-hour cycle.
True Session Opens (TSO): Key intraday opens (e.g., London, NY) for finding entries and exits on lower timeframes.
Key Features
Clean Forward Projection: All lines and labels project into the future, so you always see the levels in your current price action.
Full Styling Control: Customize the color, style (solid, dashed, dotted), and text for every level to match your chart theme.
Intelligent Display: Levels automatically show on appropriate timeframes to keep your chart clutter-free. Use the "Stacked Opens" feature to override this.
Lightweight & Efficient: Optimized to run smoothly without lagging your chart.
How to Use It
Look for price to react at these levels. A bounce can signal a continuation, while a clean break and retest can signal a change in market structure. Use the higher-timeframe opens (TMO, TWO) as major anchors for your overall bias and the lower-timeframe opens (TDO, TSO) for fine-tuning your entries and exits.
Tìm kiếm tập lệnh với "Cycle"
Fair Value Trend Model [SiDec]ABSTRACT
This pine script introduces the Fair Value Trend Model, an on-chart indicator for TradingView that constructs a continuously updating "fair-value" estimate of an asset's price via a logarithmic regression on historical data. Specifically, this model has been applied to Bitcoin (BTC) to fully grasp its fair value in the cryptocurrency market. Symmetric channel bands, defined by fixed percentage offsets around this central fair-value curve, provide a visual band within which normal price fluctuations may occur. Additionally, a short-term projection extends both the fair-value trend and its channel bands forward by a user-specified number of bars.
INTRODUCTION
Technical analysts frequently seek to identify an underlying equilibrium or "fair value" about which prices oscillate. Traditional approaches-moving averages, linear regressions in price-time space, or midlines-capture linear trends but often misrepresent the exponential or power-law growth patterns observable in many financial markets. The Fair Value Trend Model addresses this by performing an ordinary least squares (OLS) regression in log-space, fitting ln(Price) against ln(Days since inception). In practice, the primary application has been to Bitcoin, aiming to fully capture Bitcoin's underlying value dynamics.
The result is a curved trend line in regular (price-time) coordinates, reflecting Bitcoin's long-term compounding characteristics. Surrounding this fair-value curve, symmetric bands at user-specified percentage deviations serve as dynamic support and resistance levels. A simple linear projection extends both the central fair-value and its bands into the immediate future, providing traders with a heuristic for short-term trend continuation.
This exposition details:
Data transformation: converting bar timestamps into days since first bar, then applying natural logarithms to both time and price.
Regression mechanics: incremental (or rolling-window) accumulation of sums to compute the log-space fit parameters.
Fair-value reconstruction: exponentiation of the regression output to yield a price-space estimate.
Channel-band definition: establishing ±X% offsets around the fair-value curve and rendering them visually.
Forecasting methodology: projecting both the fair-value trend and channel bands by extrapolating the most recent incremental change in price-space.
Interpretation: how traders can leverage this model for trend identification, mean-reversion setups, and breakout analysis, particularly in Bitcoin trading.
Analysing the macro cycle on Bitcoin's monthly timeframe illustrates how the fair-value curve aligns with multi-year structural turning points.
DATA TRANSFORMATION AND NOTATION
1. Timestamp Baseline (t0)
Let t0 = timestamp of the very first bar on the chart (in milliseconds). Each subsequent bar has a timestamp ti, where ti ≥ t0.
2. Days Since Inception (d(t))
Define the “days since first bar” as
d(t) = max(1, (t − t0) / 86400000.0)
Here, 86400000.0 represents the number of milliseconds in one day (1,000 ms × 60 seconds × 60 minutes × 24 hours). The lower bound of 1 ensures that we never compute ln(0).
3. Logarithmic Coordinates:
Given the bar’s closing price P(t), define:
xi = ln( d(ti) )
yi = ln( P(ti) )
Thus, each data point is transformed to (xi, yi) in log‐space.
REGRESSION FORMULATION
We assume a log‐linear relationship:
yi = a + b·xi + εi
where εi is the residual error at bar i. Ordinary least squares (OLS) fitting minimizes the sum of squared residuals over N data points. Define the following accumulated sums:
Sx = Σ for i = 1 to N
Sy = Σ for i = 1 to N
Sxy = Σ for i = 1 to N
Sx2 = Σ for i = 1 to N
N = number of data points
The OLS estimates for b (slope) and a (intercept) are:
b = ( N·Sxy − Sx·Sy ) / ( N·Sx2 − (Sx)^2 )
a = ( Sy − b·Sx ) / N
All‐Time Versus Rolling‐Window Mode:
All-Time Mode:
Each new bar increments N by 1.
Update Sx ← Sx + xN, Sy ← Sy + yN, Sxy ← Sxy + xN·yN, Sx2 ← Sx2 + xN^2.
Recompute a and b using the formulas above on the entire dataset.
Rolling-Window Mode:
Fix a window length W. Maintain two arrays holding the most recent W values of {xi} and {yi}.
On each new bar N:
Append (xN, yN) to the arrays; add xN, yN, xN·yN, xN^2 to the sums Sx, Sy, Sxy, Sx2.
If the arrays’ length exceeds W, remove the oldest point (xN−W, yN−W) and subtract its contributions from the sums.
Update N_roll = min(N, W).
Compute b and a using N_roll, Sx, Sy, Sxy, Sx2 as above.
This incremental approach requires only O(1) operations per bar instead of recomputing sums from scratch, making it computationally efficient for long time series.
FAIR‐VALUE RECONSTRUCTION
Once coefficients (a, b) are obtained, the regressed log‐price at time t is:
ŷ(t) = a + b·ln( d(t) )
Mapping back to price space yields the “fair‐value”:
F(t) = exp( ŷ(t) )
= exp( a + b·ln( d(t) ) )
= exp(a) · ^b
In other words, F(t) is a power‐law function of “days since inception,” with exponent b and scale factor C = exp(a). Special cases:
If b = 1, F(t) = C · d(t), which is an exponential function in original time.
If b > 1, the fair‐value grows super‐linearly (accelerating compounding).
If 0 < b < 1, it grows sub‐linearly.
If b < 0, the fair‐value declines over time.
CHANNEL‐BAND DEFINITION
To visualise a “normal” range around the fair‐value curve F(t), we define two channel bands at fixed percentage offsets:
1. Upper Channel Band
U(t) = F(t) · (1 + α_upper)
where α_upper = (Channel Band Upper %) / 100.
2. Lower Channel Band
L(t) = F(t) · (1 − α_lower)
where α_lower = (Channel Band Lower %) / 100.
For example, default values of 50% imply α_upper = α_lower = 0.50, so:
U(t) = 1.50 · F(t)
L(t) = 0.50 · F(t)
When “Show FV Channel Bands” is enabled, both U(t) and L(t) are plotted in a neutral grey, and a semi‐transparent fill is drawn between them to emphasise the channel region.
SHORT‐TERM FORECAST PROJECTION
To extend both the fair‐value and its channel bands M bars into the future, the model uses a simple constant‐increment extrapolation in price space. The procedure is:
1. Compute Recent Increments
Let
F_prev = F( t_{N−1} )
F_curr = F( t_N )
Then define the per‐bar change in fair‐value:
ΔF = F_curr − F_prev
Similarly, for channel bands:
U_prev = U( t_{N−1} ), U_curr = U( t_N ), ΔU = U_curr − U_prev
L_prev = L( t_{N−1} ), L_curr = L( t_N ), ΔL = L_curr − L_prev
2. Forecasted Values After M Bars
Assuming the same per‐bar increments continue:
F_future = F_curr + M · ΔF
U_future = U_curr + M · ΔU
L_future = L_curr + M · ΔL
These forecasted values produce dashed lines on the chart:
A dashed segment from (bar_N, F_curr) to (bar_{N+M}, F_future).
Dashed segments from (bar_N, U_curr) to (bar_{N+M}, U_future), and from (bar_N, L_curr) to (bar_{N+M}, L_future).
Forecasted channel bands are rendered in a subdued grey to distinguish them from the current solid bands. Because this method does not re‐estimate regression coefficients for future t > t_N, it serves as a quick visual heuristic of trend continuation rather than a precise statistical forecast.
MATHEMATICAL SUMMARY
Summarising all key formulas:
1. Days Since Inception
d(t_i) = max( 1, ( t_i − t0 ) / 86400000.0 )
x_i = ln( d(t_i) )
y_i = ln( P(t_i) )
2. Regression Summations (for i = 1..N)
Sx = Σ
Sy = Σ
Sxy = Σ
Sx2 = Σ
N = number of data points (or N_roll if using rolling‐window)
3. OLS Estimator
b = ( N · Sxy − Sx · Sy ) / ( N · Sx2 − (Sx)^2 )
a = ( Sy − b · Sx ) / N
4. Fair‐Value Computation
ŷ(t) = a + b · ln( d(t) )
F(t) = exp( ŷ(t) ) = exp(a) · ^b
5. Channel Bands
U(t) = F(t) · (1 + α_upper)
L(t) = F(t) · (1 − α_lower)
with α_upper = (Channel Band Upper %) / 100, α_lower = (Channel Band Lower %) / 100.
6. Forecast Projection
ΔF = F_curr − F_prev
F_future = F_curr + M · ΔF
ΔU = U_curr − U_prev
U_future = U_curr + M · ΔU
ΔL = L_curr − L_prev
L_future = L_curr + M · ΔL
IMPLEMENTATION CONSIDERATIONS
1. Time Precision
Timestamps are recorded in milliseconds. Dividing by 86400000.0 yields days with fractional precision.
For the very first bar, d(t) = 1 ensures x = ln(1) = 0, avoiding an undefined logarithm.
2. Incremental Versus Sliding Summation
All‐Time Mode: Uses persistent scalar variables (Sx, Sy, Sxy, Sx2, N). On each new bar, add the latest x and y contributions to the sums.
Rolling‐Window Mode: Employs fixed‐length arrays for {x_i} and {y_i}. On each bar, append (x_N, y_N) and update sums; if array length exceeds W, remove the oldest element and subtract its contribution from the sums. This maintains exact sums over the most recent W data points without recomputing from scratch.
3. Numerical Robustness
If the denominator N·Sx2 − (Sx)^2 equals zero (e.g., all x_i identical, as when only one day has passed), then set b = 0 and a = Sy / N. This produces a constant fair‐value F(t) = exp(a).
Enforcing d(t) ≥ 1 avoids attempts to compute ln(0).
4. Plotting Strategy
The fair‐value line F(t) is plotted on each new bar. Its color depends on whether the current price P(t) is above or below F(t): a “bullish” color (e.g., green) when P(t) ≥ F(t), and a “bearish” color (e.g., red) when P(t) < F(t).
The channel bands U(t) and L(t) are plotted in a neutral grey when enabled; otherwise they are set to “not available” (no plot).
A semi‐transparent fill is drawn between U(t) and L(t). Because the fill function is executed at global scope, it is automatically suppressed if either U(t) or L(t) is not plotted (na).
5. Forecast Line Management
Each projection line (for F, U, and L) is created via a persistent line object. On successive bars, the code updates the endpoints of the same line rather than creating a new one each time, preserving chart clarity.
If forecasting is disabled, any existing projection lines are deleted to avoid cluttering the chart.
INTERPRETATION AND APPLICATIONS
1. Trend Identification
The fair‐value curve F(t) represents the best‐fit long‐term trend under the assumption that ln(Price) scales linearly with ln(Days since inception). By capturing power‐law or exponential patterns, it can more accurately reflect underlying compounding behavior than simple linear regressions.
When actual price P(t) lies above U(t), it may be considered “overextended” relative to its long‐term trend; when price falls below L(t), it may be deemed “oversold.” These conditions can signal potential mean‐reversion or breakout opportunities.
2. Mean‐Reversion and Breakout Signals
If price re‐enters the channel after touching or slightly breaching L(t), some traders interpret this as a mean‐reversion bounce and consider initiating a long position.
Conversely, a sustained move above U(t) can indicate strong upward momentum and a possible bullish breakout. Traders often seek confirmation (e.g., price remaining above U(t) for multiple bars, rising volume, or corroborating momentum indicators) before acting.
3. Rolling Versus All‐Time Usage
All‐Time Mode: Captures the entire dataset since inception, focusing on structural, long‐term trends. It is less sensitive to short‐term noise or volatility spikes.
Rolling‐Window Mode: Restricts the regression to the most recent W bars, making the fair‐value curve more responsive to changing market regimes, sudden volatility expansions, or fundamental shifts. Traders who wish to align the model with local behaviour often choose W so that it approximates a market cycle length (e.g., 100–200 bars on a daily chart).
4. Channel Percentage Selection
A wider band (e.g., ±50 %) accommodates larger price swings, reducing the frequency of breaches but potentially delaying actionable signals.
A narrower band (e.g., ±10 %) yields more frequent “overbought/oversold” alerts but may produce more false signals during normal volatility. It is advisable to calibrate the channel width to the asset’s historical volatility regime.
5. Forecast Cautions
The short‐term projection assumes that the last single‐bar increment ΔF remains constant for M bars. In reality, trend acceleration or deceleration can occur, rendering the linear forecast inaccurate.
As such, the forecast serves as a visual guide rather than a statistically rigorous prediction. It is best used in conjunction with other momentum, volume, or volatility indicators to confirm trend continuation or reversal.
LIMITATIONS AND CONSIDERATIONS
1. Power‐Law Assumption
By fitting ln(P) against ln(d), the model posits that P(t) ≈ C · ^b. Real markets may deviate from a pure power‐law, especially around significant news events or structural regime changes. Temporary misalignment can occur.
2. Fixed Channel Width
Markets exhibit heteroskedasticity: volatility can expand or contract unpredictably. A static ±X % band does not adapt to changing volatility. During high‐volatility periods, a fixed ±50 % may prove too narrow and be breached frequently; in unusually calm periods, it may be excessively broad, masking meaningful variations.
3. Endpoint Sensitivity
Regression‐based indicators often display greater curvature near the most recent data, especially under rolling‐window mode. This can create sudden “jumps” in F(t) when new bars arrive, potentially confusing users who expect smoother behaviour.
4. Forecast Simplification
The projection does not re‐estimate regression slope b for future times. It only extends the most recent single‐bar change. Consequently, it should be regarded as an indicative extension rather than a precise forecast.
PRACTICAL IMPLEMENTATION ON TRADINGVIEW
1 Adding the Indicator
In TradingView’s “Indicators” dialog, search for Fair Value Trend Model or visit my profile, under "scripts" add it to your chart.
Add it to any chart (e.g., BTCUSD, AAPL, EURUSD) to see real‐time computation.
2. Configuring Inputs
Show Forecast Line: Toggle on or off the dashed projection of the fair‐value.
Forecast Bars: Choose M, the number of bars to extend into the future (default is often 30).
Forecast Line Colour: Select a high‐contrast colour (e.g., yellow).
Bullish FV Colour / Bearish FV Colour: Define the colour of the fair‐value line when price is above (e.g., green) or below it (e.g., red).
Show FV Channel Bands: Enable to display the grey channel bands around the fair‐value.
Channel Band Upper % / Channel Band Lower %: Set α_upper and α_lower as desired (defaults of 50 % create a ±50 % envelope).
Use Rolling Window?: Choose whether to restrict the regression to recent data.
Window Bars: If rolling mode is enabled, designate W, the number of bars to include.
3. Visual Output
The central curve F(t) appears on the price chart, coloured green when P(t) ≥ F(t) and red when P(t) < F(t).
If channel bands are enabled, the chart shows two grey lines U(t) and L(t) and a subtle shading between them.
If forecasting is active, dashed extensions of F(t), U(t), and L(t) appear, projecting forward by M bars in neutral hues.
CONCLUSION
The Fair Value Trend Model furnishes traders with a mathematically principled estimate of an asset’s equilibrium price curve by fitting a log‐linear regression to historical data. Its channel bands delineate a normal corridor of fluctuation based on fixed percentage offsets, while an optional short‐term projection offers a visual approximation of trend continuation.
By operating in log‐space, the model effectively captures exponential or power‐law growth patterns that linear methods overlook. Rolling‐window capability enables responsiveness to regime shifts, whereas all‐time mode highlights broader structural trends. Nonetheless, users should remain mindful of the model’s assumptions—particularly the power‐law form and fixed band percentages—and employ the forecast projection as a supplemental guide rather than a standalone predictor.
When combined with complementary indicators (e.g., volatility measures, momentum oscillators, volume analysis) and robust risk management, the Fair Value Trend Model can enhance market timing, mean‐reversion identification, and breakout detection across diverse trading environments.
REFERENCES
Draper, N. R., & Smith, H. (1998). Applied Regression Analysis (3rd ed.). Wiley.
Tsay, R. S. (2014). Introductory Time Series with R (2nd ed.). Springer.
Hull, J. C. (2017). Options, Futures, and Other Derivatives (10th ed.). Pearson.
These references provide background on regression, time-series analysis, and financial modeling.
The Mayan CalendarThis indicator displays the current date in the Mayan Calendar, based on real-time UTC time. It calculates and presents:
🌀 Long Count (Baktun.Katun.Tun.Uinal.Kin) – A linear count of days since the Mayan epoch (August 11, 3114 BCE).
🔮 Tzolk'in Date – A 260-day sacred cycle combining a number (1–13) and one of 20 day names (e.g., 4 Ajaw).
🌾 Haab' Date – A 365-day civil cycle divided into 18 months of 20 days + 5 "nameless" days (Wayeb').
The calculations follow Smithsonian standards and align with the Maya Calendar Converter from the National Museum of the American Indian:
👉 maya.nmai.si.edu
The results are shown in a table overlay on your chart's top-right corner. This indicator is great for symbolic traders, astro enthusiasts, or anyone interested in ancient timekeeping systems woven into financial timeframes. Enjoy, time travelers! ⌛
Logarithmic Regression AlternativeLogarithmic regression is typically used to model situations where growth or decay accelerates rapidly at first and then slows over time. Bitcoin is a good example.
𝑦 = 𝑎 + 𝑏 * ln(𝑥)
With this logarithmic regression (log reg) formula 𝑦 (price) is calculated with constants 𝑎 and 𝑏, where 𝑥 is the bar_index .
Instead of using the sum of log x/y values, together with the dot product of log x/y and the sum of the square of log x-values, to calculate a and b, I wanted to see if it was possible to calculate a and b differently.
In this script, the log reg is calculated with several different assumed a & b values, after which the log reg level is compared to each Swing. The log reg, where all swings on average are closest to the level, produces the final 𝑎 & 𝑏 values used to display the levels.
🔶 USAGE
The script shows the calculated logarithmic regression value from historical swings, provided there are enough swings, the price pattern fits the log reg model, and previous swings are close to the calculated Top/Bottom levels.
When the price approaches one of the calculated Top or Bottom levels, these levels could act as potential cycle Top or Bottom.
Since the logarithmic regression depends on swing values, each new value will change the calculation. A well-fitted model could not fit anymore in the future.
Swings are based on Weekly bars. A Top Swing, for example, with Swing setting 30, is the highest value in 60 weeks. Thirty bars at the left and right of the Swing will be lower than the Top Swing. This means that a confirmation is triggered 30 weeks after the Swing. The period will be automatically multiplied by 7 on the daily chart, where 30 becomes 210 bars.
Please note that the goal of this script is not to show swings rapidly; it is meant to show the potential next cycle's Top/Bottom levels.
🔹 Multiple Levels
The script includes the option to display 3 Top/Bottom levels, which uses different values for the swing calculations.
Top: 'high', 'maximum open/close' or 'close'
Bottom: 'low', 'minimum open/close' or 'close'
These levels can be adjusted up/down with a percentage.
Lastly, an "Average" is included for each set, which will only be visible when "AVG" is enabled, together with both Top and Bottom levels.
🔹 Notes
Users have to check the validity of swings; the above example only uses 1 Top Swing for its calculations, making the Top level unreliable.
Here, 1 of the Bottom Swings is pretty far from the bottom level, changing the swing settings can give a more reliable bottom level where all swings are close to that level.
Note the display was set at "Logarithmic", it can just as well be shown as "Regular"
In the example below, the price evolution does not fit the logarithmic regression model, where growth should accelerate rapidly at first and then slows over time.
Please note that this script can only be used on a daily timeframe or higher; using it at a lower timeframe will show a warning. Also, it doesn't work with bar-replay.
🔶 DETAILS
The code gathers data from historical swings. At the last bar, all swings are calculated with different a and b values. The a and b values which results in the smallest difference between all swings and Top/Bottom levels become the final a and b values.
The ranges of a and b are between -20.000 to +20.000, which means a and b will have the values -20.000, -19.999, -19.998, -19.997, -19.996, ... -> +20.000.
As you can imagine, the number of calculations is enormous. Therefore, the calculation is split into parts, first very roughly and then very fine.
The first calculations are done between -20 and +20 (-20, -19, -18, ...), resulting in, for example, 4.
The next set of calculations is performed only around the previous result, in this case between 3 (4-1) and 5 (4+1), resulting in, for example, 3.9. The next set goes even more in detail, for example, between 3.8 (3.9-0.1) and 4.0 (3.9 + 0.1), and so on.
1) -20 -> +20 , then loop with step 1 (result (example): 4 )
2) 4 - 1 -> 4 +1 , then loop with step 0.1 (result (example): 3.9 )
3) 3.9 - 0.1 -> 3.9 +0.1 , then loop with step 0.01 (result (example): 3.93 )
4) 3.93 - 0.01 -> 3.93 +0.01, then loop with step 0.001 (result (example): 3.928)
This ensures complicated calculations with less effort.
These calculations are done at the last bar, where the levels are displayed, which means you can see different results when a new swing is found.
Also, note that this indicator has been developed for a daily (or higher) timeframe chart.
🔶 SETTINGS
Three sets
High/Low
• color setting
• Swing Length settings for 'High' & 'Low'
• % adjustment for 'High' & 'Low'
• AVG: shows average (when both 'High' and 'Low' are enabled)
Max/Min (maximum open/close, minimum open/close)
• color setting
• Swing Length settings for 'Max' & 'Min'
• % adjustment for 'Max' & 'Min'
• AVG: shows average (when both 'Max' and 'Min' are enabled)
Close H/Close L (close Top/Bottom level)
• color setting
• Swing Length settings for 'Close H' & 'Close L'
• % adjustment for 'Close H' & 'Close L'
• AVG: shows average (when both 'Close H' and 'Close L' are enabled)
Show Dashboard, including Top/Bottom levels of the desired source and calculated a and b values.
Show Swings + Dot size
Intellect_city - Halvings Bitcoin CycleWhat is halving?
The halving timer shows when the next Bitcoin halving will occur, as well as the dates of past halvings. This event occurs every 210,000 blocks, which is approximately every 4 years. Halving reduces the emission reward by half. The original Bitcoin reward was 50 BTC per block found.
Why is halving necessary?
Halving allows you to maintain an algorithmically specified emission level. Anyone can verify that no more than 21 million bitcoins can be issued using this algorithm. Moreover, everyone can see how much was issued earlier, at what speed the emission is happening now, and how many bitcoins remain to be mined in the future. Even a sharp increase or decrease in mining capacity will not significantly affect this process. In this case, during the next difficulty recalculation, which occurs every 2014 blocks, the mining difficulty will be recalculated so that blocks are still found approximately once every ten minutes.
How does halving work in Bitcoin blocks?
The miner who collects the block adds a so-called coinbase transaction. This transaction has no entry, only exit with the receipt of emission coins to your address. If the miner's block wins, then the entire network will consider these coins to have been obtained through legitimate means. The maximum reward size is determined by the algorithm; the miner can specify the maximum reward size for the current period or less. If he puts the reward higher than possible, the network will reject such a block and the miner will not receive anything. After each halving, miners have to halve the reward they assign to themselves, otherwise their blocks will be rejected and will not make it to the main branch of the blockchain.
The impact of halving on the price of Bitcoin
It is believed that with constant demand, a halving of supply should double the value of the asset. In practice, the market knows when the halving will occur and prepares for this event in advance. Typically, the Bitcoin rate begins to rise about six months before the halving, and during the halving itself it does not change much. On average for past periods, the upper peak of the rate can be observed more than a year after the halving. It is almost impossible to predict future periods because, in addition to the reduction in emissions, many other factors influence the exchange rate. For example, major hacks or bankruptcies of crypto companies, the situation on the stock market, manipulation of “whales,” or changes in legislative regulation.
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Table - Past and future Bitcoin halvings:
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Date: Number of blocks: Award:
0 - 03-01-2009 - 0 block - 50 BTC
1 - 28-11-2012 - 210000 block - 25 BTC
2 - 09-07-2016 - 420000 block - 12.5 BTC
3 - 11-05-2020 - 630000 block - 6.25 BTC
4 - 20-04-2024 - 840000 block - 3.125 BTC
5 - 24-03-2028 - 1050000 block - 1.5625 BTC
6 - 26-02-2032 - 1260000 block - 0.78125 BTC
7 - 30-01-2036 - 1470000 block - 0.390625 BTC
8 - 03-01-2040 - 1680000 block - 0.1953125 BTC
9 - 07-12-2043 - 1890000 block - 0.09765625 BTC
10 - 10-11-2047 - 2100000 block - 0.04882813 BTC
11 - 14-10-2051 - 2310000 block - 0.02441406 BTC
12 - 17-09-2055 - 2520000 block - 0.01220703 BTC
13 - 21-08-2059 - 2730000 block - 0.00610352 BTC
14 - 25-07-2063 - 2940000 block - 0.00305176 BTC
15 - 28-06-2067 - 3150000 block - 0.00152588 BTC
16 - 01-06-2071 - 3360000 block - 0.00076294 BTC
17 - 05-05-2075 - 3570000 block - 0.00038147 BTC
18 - 08-04-2079 - 3780000 block - 0.00019073 BTC
19 - 12-03-2083 - 3990000 block - 0.00009537 BTC
20 - 13-02-2087 - 4200000 block - 0.00004768 BTC
21 - 17-01-2091 - 4410000 block - 0.00002384 BTC
22 - 21-12-2094 - 4620000 block - 0.00001192 BTC
23 - 24-11-2098 - 4830000 block - 0.00000596 BTC
24 - 29-10-2102 - 5040000 block - 0.00000298 BTC
25 - 02-10-2106 - 5250000 block - 0.00000149 BTC
26 - 05-09-2110 - 5460000 block - 0.00000075 BTC
27 - 09-08-2114 - 5670000 block - 0.00000037 BTC
28 - 13-07-2118 - 5880000 block - 0.00000019 BTC
29 - 16-06-2122 - 6090000 block - 0.00000009 BTC
30 - 20-05-2126 - 6300000 block - 0.00000005 BTC
31 - 23-04-2130 - 6510000 block - 0.00000002 BTC
32 - 27-03-2134 - 6720000 block - 0.00000001 BTC
Election Year GainsShows the yearly gains of the chart in U.S. Election years.
Use the options to turn on other years in the cycle.
For use with the 12M chart.
Will show non-sensical data with other intervals.
Gann Dates█ INTRODUCTION
This indicator is very easy to understand and simple to use. It indicates important Gann dates in the future based on pivots (highs and lows) or key dates from the past.
According to W.D. Gann the year can be seen as a cycle or one full circle with 365 degrees. The circle can be symmetrically divided into equal sections at angles of 30, 45, 60, etc. The start of the cycle can be a significant key date or a pivot in the chart. Hence there are dates in the calendar, that fall on important angles. According to W.D. Gann those are important dates to watch for significant price movement in either direction.
In combination with other tools, this indicator can help you to time the market and make better risk-on/off decisions.
█ HOW TO USE
ibb.co
You need to adjust the settings depending on the chart. The following parameters can be adjusted:
Gann angles: The script will plot dates that are distant from pivots by a multiple of this.
Gann dates per pivot: The amount of dates that will show.
Search window size for pivots: This is how the local highs and lows are detected in the chart. The smaller this number the more local highs and lows will show.
You also have the option to hide dates derived from lows/highs, or show dates based on two custom key dates.
█ EXAMPLES
The following chart shows the price of Gold in USD with multiples of 20 days from local pivots.
The following chart shows the price of Bitcoin in USD with multiples of 30 weeks from two custom dates (in this case the low in late 2018 and the low in late 2022).
BE-TrendLines & Price SentimentsOverview
The trendline is one of the most potent and flexible tools in trading. A rising trendline indicates an upward trend, a falling trendline indicates a downward trend, and a flat trendline indicates a range-bound bond market.
Breakouts, price bounces, and reversal / Retest tactics are all types of trades that may be made using a trendline. Additionally, stop-loss and profit-trailing orders can be based on trendlines as support and resistance levels, appropriately.
Technical Calculations for Trendlines & Price Sentiments:
Pivot points for a specified time frame and the Prevailing High/Low for the most recent bars are used to derive trendlines. While Pivot Points alert us to price movements, High/Low tells us where Bulls and Bears find a middle ground. This provides a remarkable set of conditions from which to extrapolate the efficacy of the Trendlines.
The term "price sensitivity" refers to how much a change in the price of a product causes consumers to alter their purchase habits. It's the relationship between price shifts and shifts in consumer demand. So, for example, if a 30% jump in the cost of a product leads to a 10% drop in purchases, we can conclude that the item has a price sensitivity of 0.33%.
Basis the above theoretical statement, If the underlying asset's price drops, the indicator shall compute data on the amount of volume being pumped (Inflow vs Outflow) into the market (if available), or the percentage by which the price has changed. This will be compared to the recent drop rate to see if the behavior has changed at the similar value zone and non similar value zone. similar calculation shall be done if the price of the underlying rises.
Traders may benefit from hearing about Trendlines in their "Story Telling" form, which we now present. To help you comprehend it better, candles are divided into three Sentiment groups based on their color. Colors: Green (with its shades), Silver, and Red (including its shades). Green signifies a Bullish Trend, Silver a neutral trend, and Red a Brearish Trend.
Bullish Trend
Bearish Trend
Neutral Trend
Sentiment Price Cycle in Trending Market: Green (Directional Bullish), Dark Green (Bullish Trend Loosing its Strength), Silver (Neutral Trend), Red (Directional Bearish), Dark Red (Bearish Trend Loosing its Strength)
Sentiment Price Cycle in RangeBound Market: Green (Over Brought), Silver (Neutral) & Red (Over Sold)
How to Initiate Trade when price is within TL:
Fake Break Out Trade:
BreakDown Trade:
BreakOut Trade:
Couple of Other Features in the Indicator:
Single Alerts = These are the alerts where in, as and when the Event happens Alerts shall the trigerred. like On BreakOut, BreakDown, TouchOf Up TrendLine, TouchOf DownTrendLine, Retest Of Up TrendLine, Retest of DownTrendLine.
Conditional Alerts = These are those type of Alerts where in you can combine 2 or 3 conditions to trigger an Alert. Like
Sample 1 - After Down TL is tested for 3 times, If BreakOut happens and the setiment turns Bullish within 5 Candles.
Sample 2 - After Up TL is tested for 2 times, If Price Bounces backUp from TL and the setiment turns Bullish within 5 Candles.
Similarly you can customize the combination of events for getting the alert.
DISCLAIMER: No sharing, copying, reselling, modifying, or any other forms of use are authorized for our documents, script / strategy, and the information published with them. This informational planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. I am not responsible for any losses you may incur. Please invest wisely.
Happy to receive suggestions and feedback in order to improve the performance of the indicator better.
90cycle @joshuuu90 minute cycle is a concept about certain time windows of the day.
This indicator has two different options. One uses the 90 minute cycle times mentioned by traderdaye, the other uses the cls operational times split up into 90 minutes session.
e.g. we can often see a fake move happening in the 90 minute window between 2.30am and 4am ny time.
The indicator draws vertical lines at the start/end of each session and the user is able to only display certain sessions (asia, london, new york am and pm)
For the traderdayes option, the indicator also counts the windows from 1 to 4 and calls them q1,q2,q3,q4 (q-quarter)
⚠️ Open Source ⚠️
Coders and TV users are authorized to copy this code base, but a paid distribution is prohibited. A mention to the original author is expected, and appreciated.
⚠️ Terms and Conditions ⚠️
This financial tool is for educational purposes only and not financial advice. Users assume responsibility for decisions made based on the tool's information. Past performance doesn't guarantee future results. By using this tool, users agree to these terms.
Range Identifier*Re-upload as previous attempt was removed.
An attempt to create a half decent identifier of when the markets are ranging and in a state of choppiness and mean reversion - as opposed to in trending trade conditions.
It's super simple logic just working on some basic price action and market structure operating on higher time frames.
It uses the Donchian Channels but with hlc3 data as opposed to high/lows - and identifies periods in which the baseline is static, or when the channel upper & lower are contracting.
This combination identifies non trending price action with decreasing volatility, which tends to indicate a lot of upcoming chop and ranging/sideways action; especially when intraday trading and applied on the daily timeframe.
The filter increasing results in a decrease of areas identified as choppy by extending the required period of a sideways static basis, I've found values of 2 or 3 to be a nice sweetspot!
Overall should be pretty intuitive to use, when the background changes just consider altering your trading and investing approach. This was created as I've not really seen anything on here that functions quite the same.
I decided to not include the Donchian upper/lower/basis as I found that can often lead to decision bias and being influenced by where these lines are situated causing you to guess on future direction.
It's obviously never going to be perfect, but a nice and unbiased way to quickly check where we may be in a cycle; let me know if there are any issues/questions and please enjoy!
inverse_fisher_transform_adaptive_stochastic█ Description
The indicator is the implementation of inverse fisher transform an indicator transform of the adaptive stochastic (dominant cycle), as in the Cycle Analytics for Trader pg. 198 (John F. Ehlers). Indicator transformation in brief means reshaping the indicator to be more interpretable. The inverse fisher transform is achieved by compressing values near the extremes many extraneous and irrelevant wiggles are removed from the indicator, as cited.
█ Inverse Fisher Transform
input = 2*(adaptive_stoc - .5)
output = e(2*k*input) -1 / e(2*k*input) +1
█ Feature:
iFish i.e. output value
trigger i.e. previous 1 bar of iFish * 0.90
if iFish crosses above the trigger, consider a buy indicated with the green line
while, iFish crosses below the trigger, consider a sell indicate by the red line
in addition iFish needs to be greater than the previous iFish
timing marketIntraday time cycle . it is valid for nifty and banknifty .just add this on daily basis . ignore previous day data
BTC Pi MultipleThe Pi Multiple is a function of 350 and 111-day moving average. When both intersect and the 111-day MA crosses above, it has historically coincided with a cycle top with a 3-day margin.
With the Pi Multiple, this intersection is visible when the line crosses zero upwards.
The indicator is called the Pi Multiple because 350/111 is close to Pi. It is based on the Pi Cycle Top Indicator developed by Philip Swift and has been modified for better readability by David Bertho.
Bitcoin Fundamentals - Puell MultipleThis is an indicator that derives from Bitcoin Mining daily generated Income.
It does show a perfect track record on calling Bitcoin cycle tops and cycle bottoms.
For those of you willing to experiment, I've enabled the ability to set custom periods (365 by default).
The indicator includes custom alerts to notify the entry and the exit from OverBought (OB) & OverSold (OS) bands.
Credits: David Puell twitter.com
Cycle Dynamic Composite AverageThis MA uses the formula of simple cycle indicator to find 2 cycles periods length's .
The CDCA is the result of 8 different ma to control and filter the price. The regression line is the signal , don t need to look candles, but just the cross between MA and reg lin.
TASC 2025.06 Cybernetic Oscillator█ OVERVIEW
This script implements the Cybernetic Oscillator introduced by John F. Ehlers in his article "The Cybernetic Oscillator For More Flexibility, Making A Better Oscillator" from the June 2025 edition of the TASC Traders' Tips . It cascades two-pole highpass and lowpass filters, then scales the result by its root mean square (RMS) to create a flexible normalized oscillator that responds to a customizable frequency range for different trading styles.
█ CONCEPTS
Oscillators are indicators widely used by technical traders. These indicators swing above and below a center value, emphasizing cyclic movements within a frequency range. In his article, Ehlers explains that all oscillators share a common characteristic: their calculations involve computing differences . The reliance on differences is what causes these indicators to oscillate about a central point.
The difference between two data points in a series acts as a highpass filter — it allows high frequencies (short wavelengths) to pass through while significantly attenuating low frequencies (long wavelengths). Ehlers demonstrates that a simple difference calculation attenuates lower-frequency cycles at a rate of 6 dB per octave. However, the difference also significantly amplifies cycles near the shortest observable wavelength, making the result appear noisier than the original series. To mitigate the effects of noise in a differenced series, oscillators typically smooth the series with a lowpass filter, such as a moving average.
Ehlers highlights an underlying issue with smoothing differenced data to create oscillators. He postulates that market data statistically follows a pink spectrum , where the amplitudes of cyclic components in the data are approximately directly proportional to the underlying periods. Specifically, he suggests that cyclic amplitude increases by 6 dB per octave of wavelength.
Because some conventional oscillators, such as RSI, use differencing calculations that attenuate cycles by only 6 dB per octave, and market cycles increase in amplitude by 6 dB per octave, such calculations do not have a tangible net effect on larger wavelengths in the analyzed data. The influence of larger wavelengths can be especially problematic when using these oscillators for mean reversion or swing signals. For instance, an expected reversion to the mean might be erroneous because oscillator's mean might significantly deviate from its center over time.
To address the issues with conventional oscillator responses, Ehlers created a new indicator dubbed the Cybernetic Oscillator. It uses a simple combination of highpass and lowpass filters to emphasize a specific range of frequencies in the market data, then normalizes the result based on RMS. The process is as follows:
Apply a two-pole highpass filter to the data. This filter's critical period defines the longest wavelength in the oscillator's passband.
Apply a two-pole SuperSmoother (lowpass filter) to the highpass-filtered data. This filter's critical period defines the shortest wavelength in the passband.
Scale the resulting waveform by its RMS. If the filtered waveform follows a normal distribution, the scaled result represents amplitude in standard deviations.
The oscillator's two-pole filters attenuate cycles outside the desired frequency range by 12 dB per octave. This rate outweighs the apparent rate of amplitude increase for successively longer market cycles (6 dB per octave). Therefore, the Cybernetic Oscillator provides a more robust isolation of cyclic content than conventional oscillators. Best of all, traders can set the periods of the highpass and lowpass filters separately, enabling fine-tuning of the frequency range for different trading styles.
█ USAGE
The "Highpass period" input in the "Settings/Inputs" tab specifies the longest wavelength in the oscillator's passband, and the "Lowpass period" input defines the shortest wavelength. The oscillator becomes more responsive to rapid movements with a smaller lowpass period. Conversely, it becomes more sensitive to trends with a larger highpass period. Ehlers recommends setting the smallest period to a value above 8 to avoid aliasing. The highpass period must not be smaller than the lowpass period. Otherwise, it causes a runtime error.
The "RMS length" input determines the number of bars in the RMS calculation that the indicator uses to normalize the filtered result.
This indicator also features two distinct display styles, which users can toggle with the "Display style" input. With the "Trend" style enabled, the indicator plots the oscillator with one of two colors based on whether its value is above or below zero. With the "Threshold" style enabled, it plots the oscillator as a gray line and highlights overbought and oversold areas based on the user-specified threshold.
Below, we show two instances of the script with different settings on an equities chart. The first uses the "Threshold" style with default settings to pass cycles between 20 and 30 bars for mean reversion signals. The second uses a larger highpass period of 250 bars and the "Trend" style to visualize trends based on cycles spanning less than one year:
BTCUSD – Market Structure Projection1. Short-Term Outlook
1. BTC is expected to complete a final liquidity sweep below recent lows.
2. A minor corrective rally into a premium zone offers a short opportunity.
3. Confirmation comes from rejection + RSI divergence.
2. Mid-Term Reversal Setup
4. After the sweep, BTC is projected to form a bullish break of structure (BOS).
5. A retest of demand provides the optimal long entry.
6. This phase begins the next expansion leg into 2026.
3. Long-Term Macro Trend
7. The higher-timeframe trend remains bullish despite local corrections.
8. BTC is expected to follow an impulse → correction → impulse pattern.
9. Macro upside targets remain positioned for new all-time highs.
4. Key Market Levels
Support Zones
10. $86,000 – $90,000 — primary liquidity-sweep region.
11. $92,500 – $94,000 — bullish retest confirmation zone.
Resistance Zones
12. $105,000 – $110,000 — mid-cycle rejection area.
13. $130,000 – $150,000 — macro expansion target range.
5. Trade Framework Summary
14. Short Setup: Enter after corrective rally into premium; target liquidity sweep.
15. Long Setup: Enter after BOS + demand retest; target macro continuation.
16. Structure favors a bullish expansion phase through 2026.
BK10 BTC Regime v10.3BK10 BTC Regime v10.3 – Cycle, Risk, and Euphoria Map
BK10 v10.3 is a regime map designed to show at a glance where the market is: from CRASH/STRESS to EUPHORIA. It combines trend (50/200 EMA), momentum (RSI), and optionally, macro context (SPX + VIX) to classify each candlestick into 6 states:
CRASH · RISK-OFF · NEUTRAL · ACCUMULATION · RISK-ON · EUPHORIA
The indicator colors the background according to the regime.
BRS STC Schaff Trend CycleSTC with custom settings for 2min charts; Buy/Sell signals are close, but works very well with other indicators for validation; Borrowed from everget and another;
Spectre On-Chain Season (CMC #101–2000, Nov’21/Nov’24 Anchors)Spectre On-Chain Season Index measures the real health of the on-chain market by focusing on the mid-tail of crypto — not Bitcoin, not ETH, not the Top 100.
Instead of tracking hype at the top of the market, this index looks at coins ranked #101–#2000 on CoinMarketCap and compares their current price performance to their cycle highs from:
November 2021 peak
November 2024 peak
BGX Trader EvaluationBGX Trader Evaluation — What it does
This study evaluates a fixed calendar window each year (from a chosen Start Day/Month to an End Day/Month), measures the performance between those two exact dates, and then aggregates the results across years. It can filter years by the U.S. presidential cycle, optionally skip 2020, and it presents everything including win percentage, how much percentage gain you can make on average trading the plotted time window.
Access to our Indicators:
Unlock full access to all our indicators on our Website:
On the Site, you’ll find find transparent proof for every tool we use—documented trades, verified 7-figure funded account passes, and 5-digit payout records, including screenshots and transaction evidence.
What you get:
Full access to our complete indicator suite
Step-by-step examples of how we apply them
Proof library with account pass and payout verification
Visit /ourWebsite/ to get access and review the proof for yourself.
Entries + FVG SignalsE+FVG: A Masterclass in Institutional Trading Concepts
Chapter 1: The Modern Trader's Dilemma—Decoding the Institutional Footprint
In the vast, often chaotic ocean of the financial markets, retail traders navigate with the tools they are given: conventional indicators like moving averages, RSI, and MACD. While useful for gauging momentum and general trends, these tools often fall short because they were not designed to interpret the primary force that moves markets: institutional order flow. The modern trader faces a critical challenge: the tools and concepts taught in mainstream trading education are often decades behind the sophisticated, algorithm-driven strategies employed by banks, hedge funds, and large financial institutions.
This leads to a frustrating cycle of seemingly inexplicable price movements. A trader might see a perfect breakout from a classic pattern, only for it to reverse viciously, stopping them out. They might identify a strong trend, yet struggle to find a logical entry point, consistently feeling "late to the party." These experiences are not random; they are often the result of institutional market manipulation designed to engineer liquidity.
The fundamental problem that E+FVG (Entries + FVG Signals) addresses is this informational asymmetry. It is a sophisticated, institutional-grade framework designed to move a trader's perspective from a retail mindset to a professional one. It does not rely on lagging, derivative indicators. Instead, it focuses on the two core elements of price action that reveal the true intentions of "Smart Money": liquidity and imbalances.
This is not merely another indicator to add to a chart; it is a complete analytical engine designed to help you see the market through a new lens. It deconstructs price action to pinpoint two critical things:
Where institutions are likely to hunt for liquidity (running stop-loss orders).
The specific price inefficiencies (Fair Value Gaps) they are likely to target.
By focusing on these core principles, E+FVG provides a logical, rules-based solution to identifying high-probability trade setups. It is built for the discerning trader who is ready to evolve beyond conventional technical analysis and learn a methodology that is aligned with how the market truly operates at an institutional level. It is, in essence, an operating system for "Smart Money" trading.
Chapter 2: The Core Philosophy—Liquidity is the Fuel, Imbalances are the Destination
To fully grasp the power of this tool, one must first understand its foundational philosophy, which is rooted in the core tenets of institutional trading, often referred to as Smart Money Concepts (SMC). This philosophy can be distilled into two simple, powerful ideas:
1. Liquidity is the Fuel that Moves the Market:
The market does not move simply because there are more buyers than sellers, or vice-versa. It moves to seek liquidity. Large institutions cannot simply click "buy" or "sell" to enter or exit their multi-million or billion-dollar positions. Doing so would cause massive slippage and alert the entire market to their intentions. Instead, they must strategically accumulate and distribute their positions in areas where there is a high concentration of orders.
Where are these orders located? They are clustered in predictable places: above recent swing highs (buy-stop orders from shorts, and breakout buy orders) and below recent swing lows (sell-stop orders from longs, and breakout sell orders). This collective pool of orders is called liquidity. Institutions will often drive price towards these liquidity pools in a "stop hunt" or "liquidity grab" to trigger those orders, creating the necessary volume for them to fill their own large positions, often in the opposite direction of the liquidity grab itself. Understanding this concept is the key to avoiding being the "fuel" and instead learning to trade alongside the institutions.
2. Imbalances (Fair Value Gaps) are the Magnets for Price:
When institutions enter the market with overwhelming force, they create an imbalance in the order book. This energetic, one-sided price movement often leaves behind a gap in the market's pricing mechanism. On a candlestick chart, this appears as a Fair Value Gap (FVG)—a three-candle formation where the wicks of the first and third candles do not fully overlap the range of the middle candle.
These are not random gaps; they represent an inefficiency in the market's price delivery. The market, in its constant quest for equilibrium, has a natural tendency to revisit these inefficiently priced areas to "rebalance" the order book. Therefore, FVGs act as powerful magnets for price. They serve as high-probability targets for a price move and, critically, as logical points of interest where price may reverse after filling the imbalance. A fresh, unfilled FVG is one of the most significant clues an institution leaves behind.
E+FVG is built entirely on this philosophy. The "Entries Simplified" engine is designed to identify the liquidity grabs, and the "FVG Signals" engine is designed to identify the imbalances. Together, they provide a complete, synergistic framework for institutional-grade analysis.
Chapter 3: The Engine, Part I—"Entries Simplified": A Framework for Precision Entry
This is the primary trade-spotting engine of the E+FVG tool. It is a multi-layered system designed to identify a very specific, high-probability entry model based on institutional behavior. It filters out market noise by focusing solely on the sequence of a liquidity sweep followed by a clear and energetic displacement.
Feature 1: The Multi-Timeframe Liquidity Engine
The first and most crucial step in the engine's logic is to identify a valid liquidity grab. The script understands that the most significant reversals are often initiated after price has swept a key high or low from a higher timeframe. A sweep of yesterday's high holds far more weight than a sweep of the last 5-minute high.
Automatic Timeframe Adaptation: The engine intelligently analyzes your current chart's timeframe and automatically selects an appropriate higher timeframe (HTF) for its core analysis. For instance, if you are on a 15-minute chart, it might reference the 4-hour or Daily chart to identify key structural points. This is done seamlessly in the background, ensuring the analysis is always anchored to a significant structural context without requiring manual input.
The "Sweep" Condition: The script is not looking for a simple touch of a high or low. It is looking for a definitive sweep (also known as a "stop hunt" or "Judas swing"). This is defined as price pushing just beyond a key prior candle's high or low and then closing back within its range. This specific price action pattern is a classic signature of a liquidity grab, indicating that the move's purpose was to trigger stops, not to start a new, sustained trend. The "Entries Simplified" engine is constantly scanning the HTF price action for these sweep events, as they are the necessary precondition for any potential setup.
Feature 2: The Upshift/Downshift Signal—Confirming the Reversal
Once a valid HTF liquidity sweep has occurred, the engine moves to its next phase: identifying the confirmation. A sweep alone is not enough; institutions must show their hand and reveal their intention to reverse the market. This confirmation comes in the form of a powerful structural breakout (for bullish reversals) or breakdown (for bearish reversals). We call these events Upshifts and Downshifts.
Defining the Upshift & Downshift: This is the critical moment of confirmation, the market "tipping its hand."
An Upshift occurs after a liquidity sweep below a key low. Following the sweep, price reverses with energy and produces a decisive breakout to the upside, closing above a recent, valid swing high. This action confirms that the prior downtrend's momentum is broken, the downward move was a trap to engineer liquidity, and institutional buyers are now in aggressive control.
A Downshift occurs after a liquidity sweep above a key high. Following the sweep, price reverses aggressively and produces a sharp breakdown to the downside, closing below a recent, valid swing low. This confirms that the prior uptrend's momentum has failed, the upward move was a liquidity grab, and institutional sellers have now taken control of the market.
Algorithmic Identification: The E+FVG engine uses a proprietary algorithm to identify these moments. It analyzes the candle sequence immediately following a sweep, looking for a specific type of market structure break characterized by high energy and displacement—often leaving imbalances (Fair Value Gaps) in its wake. This is not a simple "pivot break"; the algorithm is designed to distinguish between a weak, indecisive wiggle and a true, institutionally-backed Upshift or Downshift.
The Signal: When this precise sequence—a HTF liquidity sweep followed by a valid Upshift or Downshift on the trading timeframe—is confirmed, the indicator plots a clear arrow on the chart. A green arrow below a low signifies a Bullish setup (confirmed by an Upshift), while a red arrow above a high signifies a Bearish setup (confirmed by a Downshift). This is the core entry signal of the "Entries Simplified" engine.
Feature 3: Automated Price Projections—A Built-In Trade Management Framework
A valid entry signal is only one part of a successful trade. A trader also needs a logical framework for taking profits. The E+FVG engine completes its trade-spotting process by providing automated, mathematically-derived price projections.
Fibonacci-Based Logic: After a valid Upshift or Downshift signal is generated, the script analyzes the price leg that created the setup (i.e., the range from the liquidity sweep to the confirmation breakout/breakdown). It then uses a methodology based on standard Fibonacci extension principles to project several potential take-profit (TP) levels.
Multiple TP Levels: The indicator projects four distinct TP levels (TP1, TP2, TP3, TP4). This provides a comprehensive trade management framework. A conservative trader might aim for TP1 or TP2, while a more aggressive trader might hold a partial position for the higher targets. These levels are plotted on the chart as clear, labeled lines, removing the guesswork from profit-taking.
Dynamic and Adaptive: These projections are not static. They are calculated uniquely for each individual setup, based on the specific volatility and range of the price action that generated the signal. This ensures that the take-profit targets are always relevant to the current market conditions.
The "Entries Simplified" engine, therefore, provides a complete, end-to-end framework: it waits for a high-probability condition (HTF sweep), confirms it with a specific entry model (Upshift/Downshift), and provides a logical road map for managing the trade (automated projections).
Chapter 4: The Engine, Part II—"FVG Signals": Mapping Market Inefficiencies
This second, complementary engine of the E+FVG tool operates as a market mapping system. Its sole purpose is to identify, plot, and monitor Fair Value Gaps (FVGs)—the critical price inefficiencies that act as magnets and potential reversal points.
Feature 1: Dual Timeframe FVG Detection
The significance of an FVG is directly related to the timeframe on which it forms. A 1-hour FVG is a more powerful magnet for price than a 1-minute FVG. The FVG engine gives you the ability to monitor both simultaneously, providing a richer, multi-dimensional view of the market's inefficiencies.
Chart TF FVGs: The indicator will, by default, identify and plot the FVGs that form on your current, active chart timeframe. These are useful for short-term scalping and for fine-tuning entries.
Higher Timeframe (HTF) FVGs: With a single click, you can enable the HTF FVG detection. This allows you to overlay, for example, 1-hour FVGs onto your 5-minute chart. This is an incredibly powerful feature. Seeing a 5-minute price rally approaching a fresh, unfilled 1-hour bearish FVG gives you a high-probability context for a potential reversal. The HTF FVGs act as major points of interest that can override the short-term price action.
Feature 2: The Intelligent "Tap-In" Logic—Beyond a Simple Touch
Many FVG indicators will simply alert you when price touches an FVG. The E+FVG engine employs a more sophisticated, two-stage logic to generate its signals, which helps to filter out weak reactions and focus on confirmed reversals.
Stage 1: The Entry. The first event is when price simply enters the FVG zone. This is a "heads-up" moment, and the indicator can be configured to provide an initial alert for this event.
Stage 2: The Confirmed "Tap-In." The official signal, however, is the "Tap-In." This is a more stringent condition. For a bullish FVG, a Tap-In is only confirmed after price has touched or entered the FVG zone and then closed back above the FVG's high. For a bearish FVG, the price must touch or enter the zone and then close back below the FVG's low. This confirmation logic ensures that the FVG has not just been touched, but has been respected and rejected by the market, making the resulting arrow signal significantly more reliable than a simple touch alert.
Feature 3: Interactive and Clean Visuals
The FVG engine is designed to provide maximum information with minimum chart clutter.
Clear, Color-Coded Boxes: Bullish FVGs are plotted in one color (e.g., green or blue), and bearish FVGs in another (e.g., red or orange), with a clear distinction between Chart TF and HTF zones.
Optional Box Display: Recognizing that some traders prefer a cleaner chart, you have the option to hide the FVG boxes entirely. Even with the boxes hidden, the underlying logic remains active, and the script will still generate the crucial Tap-In arrow signals.
Automatic Fading: Once an FVG has been successfully "tapped," the script can be set to automatically fade the color of the box. This provides a clear visual cue that the zone has been tested and may have less significance going forward.
Expiration: FVGs do not remain relevant forever. The script automatically removes old FVG boxes from the chart after a user-defined number of bars, ensuring your analysis is always focused on the most recent and relevant market inefficiencies.
Chapter 5: The Power of Synergy—How the Two Engines Work Together
While both the "Entries Simplified" engine and the "FVG Signals" engine are powerful standalone tools, their true potential is unlocked when used in combination. They are designed to provide confluence—a scenario where two or more independent analytical concepts align to produce a single, high-conviction trade idea.
Scenario A: The A+ Setup (Upshift into FVG). This is the highest probability setup. Imagine the "Entries Simplified" engine detects a HTF liquidity sweep below a key low, followed by a bullish Upshift signal. You look at your chart and see that this strong upward displacement is heading directly towards a fresh, unfilled bearish HTF FVG. This provides you with both a high-probability entry signal and a logical, high-probability target for the trade.
Scenario B: The FVG Confirmation. A trader might see the "Entries Simplified" engine generate a bearish Downshift signal. They feel it is a valid setup but want one extra layer of confirmation. They wait for price to rally a little further and "tap-in" to a nearby bearish FVG that formed during the Downshift's displacement. The FVG Tap-In signal then serves as their final confirmation trigger to enter the trade.
Scenario C: The Standalone FVG Trade. The FVG engine can also be used as a primary trading tool. A trader might notice that price is in a strong uptrend. They see price pulling back towards a fresh, bullish HTF FVG. They are not waiting for a full Upshift/Downshift setup; instead, they are simply waiting for the FVG Tap-In signal to confirm that the pullback is likely over and the trend is ready to resume.
By learning to read the interplay between these two engines, a trader can elevate their analysis from a one-dimensional process to a multi-dimensional, context-aware methodology.
Chapter 6: The Workflow—A Step-by-Step Guide to Practical Application
Step 1: The Pre-Market Analysis (Mapping the Battlefield). Before your session begins, enable the HTF FVG detection. Identify the key, unfilled HTF FVGs above and below the current price. These are your major points of interest for the day—your potential targets and reversal zones.
Step 2: Await the Primary Condition (Patience for Liquidity). During your trading session, your primary focus should be on the "Entries Simplified" engine. Your job is to wait patiently for the script to identify a valid HTF liquidity sweep. Do not force trades in the middle of a price range where no significant liquidity has been taken.
Step 3: The Upshift/Downshift Alert (The Call to Action). When the red or green arrow from the "Entries Simplified" engine appears, it is your cue to focus your attention. This is a potential high-probability setup.
Step 4: The Confluence Check (Building Conviction). With the Upshift or Downshift signal on your chart, ask the key confluence questions:
Did the displacement from the Upshift/Downshift create a new FVG?
Is the projected path of the trade heading towards a pre-identified HTF FVG?
Has an FVG Tap-In signal appeared shortly after the initial signal, offering further confirmation?
Step 5: Execute and Manage. If you have sufficient confluence, execute the trade. Use the automated price projections as your guide for profit-taking. A logical stop-loss is typically placed just beyond the high or low of the liquidity sweep that initiated the entire sequence.
Chapter 7: The Trader's Mind—Mastering the Institutional Mindset
This tool is more than a set of algorithms; it is a training system for professional trading psychology.
From Chasing to Trapping: You stop chasing breakouts and instead learn to identify where others are being trapped.
From FOMO to Patience: The strict, sequential logic of the entry model (Sweep -> Upshift/Downshift) forces you to wait for the highest quality setups, curing the Fear Of Missing Out.
Probabilistic Thinking: By focusing on liquidity and imbalances, you begin to think in terms of probabilities, not certainties. You understand that you are putting on trades where the odds are statistically in your favor, which is the cornerstone of any professional trading career.
Clarity and Confidence: The clear, rules-based signals remove ambiguity and second-guessing. This builds the confidence needed to execute trades decisively when the opportunity arises.
Chapter 8: Frequently Asked Questions & Scenarios
Q: The "Entries Simplified" code looks complex. Do I need to understand all of it?
A: No. The engine is designed to perform its complex analysis in the background. Your job is to understand the principles—liquidity sweep and the resulting Upshift or Downshift—and to recognize the clear arrow signals that the script generates when those conditions are met.
Q: Can I turn one of the engines off?
A: Yes, the indicator is modular. If you only want to focus on Fair Value Gaps, for example, you can disable the plot shapes for the "Entries Simplified" signals in the settings, and vice-versa.
Q: Does this work on all assets and timeframes?
A: The principles of liquidity and imbalance are universal and apply to all markets, from cryptocurrencies to forex to indices. The fractal nature of the analysis means the concepts are valid on all timeframes. However, it is always recommended that a trader backtest and forward-test the tool on their specific instrument and timeframe of choice to understand its unique behavior.
Author's Instructions
To request access to this script, please send me a direct private message here on TradingView.
Alternatively, you can find more information and contact details via the link on my profile signature.
Please DO NOT request access in the Comments section. Comments are for questions about the script's methodology and for sharing constructive feedback.
ALISH WEEK LABELS THE ALISH WEEK LABELS
Overview
This indicator programmatically delineates each trading week and encapsulates its realized price range in a live-updating, filled rectangle. A week is defined in America/Toronto time from Monday 00:00 to Friday 16:00. Weekly market open to market close, For every week, the script draws:
a vertical start line at the first bar of Monday 00:00,
a vertical end line at the first bar at/after Friday 16:00, and
a white, semi-transparent box whose top tracks the highest price and whose bottom tracks the lowest price observed between those two temporal boundaries.
The drawing is timeframe-agnostic (M1 → 1D): the box expands in real time while the week is open and freezes at the close boundary.
Time Reference and Session Boundaries
All scheduling decisions are computed with time functions called using the fixed timezone string "America/Toronto", ensuring correct behavior across DST transitions without relying on chart timezone. The start condition is met at the first bar where (dayofweek == Monday && hour == 0 && minute == 0); on higher timeframes where an exact 00:00 bar may not exist, a fallback checks for the first Monday bar using ta.change(dayofweek). The close condition is met on the first bar at or after Friday 16:00 (Toronto), which guarantees deterministic closure on intraday and higher timeframes.
State Model
The indicator maintains minimal persistent state using var globals:
week_open (bool): whether the current weekly session is active.
wk_hi / wk_lo (float): rolling extrema for the active week.
wk_box (box): the graphical rectangle spanning × .
wk_start_line and a transient wk_end_line (line): vertical delimiters at the week’s start and end.
Two dynamic arrays (boxes, vlines) store object handles to support bounded history and deterministic garbage collection.
Update Cycle (Per Bar)
On each bar the script executes the following pipeline:
Start Check: If no week is open and the start condition is satisfied, instantiate wk_box anchored at the current bar_index, prime wk_hi/wk_lo with the bar’s high/low, create the start line, and push both handles to their arrays.
Accrual (while week_open): Update wk_hi/wk_lo using math.max/min with current bar extremes. Propagate those values to the active wk_box via box.set_top/bottom and slide box.set_right to the current bar_index to keep the box flush with live price.
Close Check: If at/after Friday 16:00, finalize the week by freezing the right edge (box.set_right), drawing the end line, pushing its handle, and flipping week_open false.
Retention Pruning: Enforce a hard cap on historical elements by deleting the oldest objects when counts exceed configured limits.
Drawing Semantics
The range container is a filled white rectangle (bgcolor = color.new(color.white, 100 − opacity)), with a solid white border for clear contrast on dark or light themes. Start/end boundaries are full-height vertical white lines (y1=+1e10, y2=−1e10) to guarantee visibility across auto-scaled y-axes. This approach avoids reliance on price-dependent anchors for the lines and is robust to large volatility spikes.
Multi-Timeframe Behavior
Because session logic is driven by wall-clock time in the Toronto zone, the indicator remains consistent across chart resolutions. On coarse timeframes where an exact boundary bar might not exist, the script legally approximates by triggering on the first available bar within or immediately after the boundary (e.g., Friday 16:00 occurs between two 4-hour bars). The box therefore represents the true realized high/low of the bars present in that timeframe, which is the correct visual for that resolution.
Inputs and Defaults
Weeks to keep (show_weeks_back): integer, default 40. Controls retention of historical boxes/lines to avoid UI clutter and resource overhead.
Fill opacity (fill_opacity): integer 0–100, default 88. Controls how solid the white fill appears; border color is fixed pure white for crisp edges.
Time zone is intentionally fixed to "America/Toronto" to match the strategy definition and maintain consistent historical backtesting.
Performance and Limits
Objects are reused only within a week; upon closure, handles are stored and later purged when history limits are exceeded. The script sets generous but safe caps (max_boxes_count/max_lines_count) to accommodate 40 weeks while preserving Editor constraints. Per-bar work is O(1), and pruning loops are bounded by the configured history length, keeping runtime predictable on long histories.
Edge Cases and Guarantees
DST Transitions: Using a fixed IANA time zone ensures Friday 16:00 and Monday 00:00 boundaries shift correctly when DST changes in Toronto.
Weekend Gaps/Holidays: If the market lacks bars exactly at boundaries, the nearest subsequent bar triggers the start/close logic; range statistics still reflect observed prices.
Live vs Historical: During live sessions the box edge advances every bar; when replaying history or backtesting, the same rules apply deterministically.
Scope (Intentional Simplicity)
This tool is strictly a visual framing indicator. It does not compute labels, statistics, alerts, or extended S/R projections. Its single responsibility is to clearly present the week’s realized range in the Toronto session window so you can layer your own execution or analytics on top.
