EMA Edge - Multi-EMA Backtest Table with Golden/Death CrossEMA Edge — Multi-EMA Backtest Table with Golden/Death Cross
A clean, all-in-one performance dashboard that backtests 6 long-only strategies side-by-side against a Buy & Hold benchmark — 5 single-EMA crossover strategies plus a classic Golden Cross / Death Cross strategy — with on-chart cross markers and built-in alerts.
Instead of guessing which EMA length works best for a given stock or timeframe, this indicator runs the math for you and shows the answer in a single glance, ranked against simply holding the asset.
What It Does
For each of 5 user-defined EMA lengths, the indicator simulates a simple long-only strategy:
Buy when price closes above the EMA (when flat)
Sell when price closes below the EMA (when long)
Equity starts at 100 and compounds across trades using the close-to-close return of each trade
A 6th strategy row tests the classic Golden/Death Cross:
Buy when the fast EMA crosses above the slow EMA (Golden Cross)
Sell when the fast EMA crosses below the slow EMA (Death Cross)
Special first-bar handling: if the fast EMA is already above the slow EMA at the start of your backtest window (i.e., we're mid-trend with no fresh Golden Cross to wait for), the strategy enters immediately at that bar's opening price. This avoids the unrealistic outcome of sitting in cash for years waiting for a cross that already happened.
All strategies are compared against a Buy & Hold baseline that starts at the close of the first in-range bar. If a position is open at the last bar, its equity is marked-to-market so every strategy is compared on equal terms — fully invested vs. partially invested at the cutoff.
Features
5 configurable EMAs — defaults 9 / 21 / 50 / 100 / 200, fully editable
Golden/Death Cross strategy — uses independent fast/slow EMA inputs (default 50 / 200)
Performance table showing Return %, delta vs. Buy & Hold, and Outperform / Underperform status per strategy
Flexible backtest window — X weeks, X years, or full chart lifetime
On-chart GC / DC markers with optional subtle background tint on cross bars
Built-in alerts for both Golden Cross and Death Cross events
Fully customizable table — 9 position options, 6 text sizes, all colors exposed as inputs
Soft, light color palette designed not to dominate the chart
Optional EMA plotting (off by default to keep the chart clean)
How To Read The Table
ColumnMeaningStrategyThe rule being testedReturnTotal % return of the strategy over the chosen windowVs StockDifference between the strategy's return and Buy & HoldStatus▲ Outperform if the strategy beat Buy & Hold, ▼ Underperform if not
Green-tinted rows = strategy beat Buy & Hold
Red-tinted rows = strategy underperformed Buy & Hold
Cream row = the Buy & Hold baseline itself
How To Use
Add the indicator to any chart — works on stocks, ETFs, crypto, forex, any timeframe.
Choose your backtest window (e.g., 1 Year, 5 Years, or Lifetime).
Scan the table to see which strategy historically beat Buy & Hold on this asset.
Use the on-chart GC / DC labels to spot historical and live cross events. Right-click any marker → Add Alert to be notified on new crosses.
Tip: Test the same EMA lengths across daily and weekly timeframes and across different assets. You'll usually find that what works on a steady index like SPY does not work on a volatile single stock, and vice versa. That's the entire point of the table — to make those differences visible instead of assumed.
Key Inputs
EMA 1–5: Lengths for the 5 single-EMA strategies
Show EMAs: Plot EMAs on chart (off by default)
Use Lifetime Performance: Backtest from the very first bar instead of a fixed window
Performance Timeframe Type / Value: Weeks or Years lookback
Crosses group: Toggle GC/DC display, set fast/slow EMA lengths, customize colors
Table Style group: Position, text size, background and text colors
Notes & Limitations
All strategies are long-only — no shorts, no leverage, no stops, no commissions, no slippage. This is a clean rule-based comparison, not a turnkey trading system. Live results will differ.
Entries and exits use close prices, except for the GC/DC strategy's first-bar entry when already in a golden state, which uses open.
Open positions at the last bar are marked-to-market so the comparison vs. Buy & Hold is apples-to-apples.
Past performance is not indicative of future results. Use this as a research and screening tool.
The GC/DC strategy uses separate EMA lengths from the 5 table EMAs by design, so you can run 9/21/50/100/200 in the table while still testing the classic 50/200 cross.
Alerts Available
Golden Cross: Fast EMA crossed above Slow EMA
Death Cross: Fast EMA crossed below Slow EMA
If you find this useful, a boost is appreciated. Suggestions and feedback welcome in the comments.
Open-source — feel free to study, fork, and adapt. Indicator
EMA (15 EMA's in 1)English Version
This is a simple indicator with built-in EMAs and flexible customization.
You can use the default EMA values or modify them according to your strategy.
If you need an additional EMA, simply add it in the code and enable it in the settings.
Example:
ema2222 = Ema(2222)
s2222 = input.bool(true, "2222")
plot(s2222 ? ema2222 : na, "2222", color=color.white, linewidth=1)
To change the period — replace 2222 with any value you prefer.
To add another EMA — duplicate the lines and specify a new period.
RU. Это простой индикатор со встроенными EMA и возможностью гибкой настройки.
Вы можете использовать предустановленные значения EMA или изменить их под свою стратегию.
Если вам нужна дополнительная EMA, просто добавьте её в код и настройках.
Для примера:
ema2222 = Ema(2222)
s2222 = input.bool(true, "2222")
plot(s2222 ? ema2222 : na, "2222", color=color.white, linewidth=1)
Чтобы изменить период — замените число 2222 на любое нужное значение.
Чтобы добавить ещё одну EMA — продублируйте строку и укажите новый период
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NQ 9:45-10:15 ICT Strategy - CompleteNQ 9:45–10:15 ICT Strategy – High-Probability Nasdaq Scalper (ICT/Smart Money)
This open-source strategy is a complete, rules-based implementation of core Inner Circle Trader (ICT) / Smart Money concepts, specifically optimized for **Nasdaq futures (NQ)** during the high-volatility 9:45–10:15 AM New York time window (first 30 minutes after the 9:30 open liquidity grab phase).
Core Philosophy & Why This Combination?
ICT emphasizes that institutional order flow often creates **false moves** (liquidity sweeps of previous day high/low), followed by **market structure shifts (MSS / Break of Structure)** that reveal true directional intent, with entries taken from **mitigation of Order Blocks** (areas of institutional interest / imbalance).
This script enforces strict confluence:
- Daily bias filter (price or prev close vs daily EMA20)
- Liquidity sweep confirmation (PDH/PDL raid + reversal close)
- Bullish/Bearish Market Structure Shift (close beyond last swing high/low)
- Refined Order Block detection (last opposite candle before MSS, with defensive/aggressive mitigation logic inspired by popular ICT order block refinements)
- Tight time filter (only 9:45–10:15 NY) — captures post-open manipulation & directional resolve
- One trade per day rule — prevents overtrading in chop
The tight combination reduces false signals dramatically and aligns with ICT's focus on high-probability setups during specific market sessions.
Key Components & Logic
1. Daily Bias
- Bullish if current price > daily EMA20 (or prev daily close > EMA20 — user choice)
- Bearish otherwise
- Background tint + orange EMA line
2. Liquidity Sweep
- Low < PD Low but close > PD Low → bullish sweep (stops taken below)
- High > PD High but close < PD High → bearish sweep
- Resets daily
3. Market Structure Shift (MSS / BoS)
- Bullish MSS: close > last fractal swing high
- Bearish MSS: close < last fractal swing low
- Uses 5-bar fractal detection for swing points
- Resets daily
4. Order Block (OB) Detection & Refinement
- Bullish OB: last bearish candle (close < open) before bullish MSS
- Bearish OB: last bullish candle before bearish MSS
- Refinement options (On/Off):
- Defensive: tightens OB to body or wick depending on candle range vs ATR(55)
- Aggressive: uses full candle wick range
- OB box drawn until mitigated (price touches opposite side)
- Labeled "OB+" / "OB-"
5. Entry Window & Confluence
- Only allowed 9:45–10:15 NY time
- Long: bullish bias + low swept + bullish MSS + price mitigates bullish OB (touches high side, closes inside/above low side)
- Short: mirror logic
- One trade per day max
6. Risk & Exit
- SL = opposite side of Order Block
- TP = 2:1 RR (adjustable) from entry
- No trailing / partials — clean single target
Visuals
- Daily EMA (orange)
- Prev Day High/Low circles
- Bullish/Bearish background tint
- MSS triangles
- Order Block boxes + "OB+"/"OB-" labels
- Entry labels with price/SL/TP
Alerts
- "NQ Long: Bullish Bias + Sweep + MSS + OB"
- "NQ Short: Bearish Bias + Sweep + MSS + OB"
Realistic Backtesting & Usage Guidelines
To publish non-misleading results:
- Initial Capital: $10,000–$50,000 (realistic futures account)
- Position sizing: 1–3% of equity per trade (change default_qty_value from 100%!)
- Commission: $4–$8 round-turn per contract (typical NQ futures commission)
- Slippage: 1–4 ticks (NQ is liquid but fast-moving post-open)
- Dataset: ≥2–3 years of 1-minute or 5-minute NQ data (aim for 300–600+ trades)
- Risk per trade: 0.5–1.5% with defaults — never risk more than sustainable
The 30-minute window produces relatively few trades per year — perfect for statistical significance over long periods, but results vary heavily by market regime (trending vs. choppy opens).
How to Use
1. Apply to NQ1! or MNQ1! (continuous futures) on 1-minute or 5-minute chart.
2. Keep default NY timezone (America/New_York).
3. Start with 2:1 RR, Defensive refinement, show levels on.
4. Trade only during the window — best setups show clear sweep + MSS + OB mitigation.
5. Avoid major news overlapping the window (FOMC, CPI, etc.) or widen SL.
6. Forward-test on demo for several months — this is a high-confluence, low-frequency setup.
Publish Recommendation
- Use a clean chart: only this strategy, no extra indicators/drawings.
- Show realistic Strategy Tester view with commission/slippage applied.
- Screenshot during NY morning session with visible OB & signal.
Test thoroughly and trade responsibly. Strategy
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Stalonte EMA - Stable Long-Term EMA with AlertsStalonte EMA - The Adaptive & Stable EMA - Almost Eternal
Here's why you will love "Stalonte":
The Stalonte (Stable Long-Term EMA) is a highly versatile trend-following tool. Unlike standard EMAs with fixed periods, it uses a configurable smoothing constant (alpha), allowing traders to dial in the exact level of responsiveness and stability they need. Finding the "sweet spot" (e.g., alpha ~0.03) creates a uniquely effective moving average: it is smooth enough to filter out noise and identify safe, high-probability trends, yet responsive enough to provide actionable signals without extreme lag. It includes alerts for crossovers and retests.
Pros and Cons of the Stalonte EMA
Pros:
Unparalleled Adaptability: This is its greatest strength. The alpha input lets you seamlessly transform the indicator from an ultra-slow "trend-revealer" (low alpha) into a highly effective and "safe" trend-following tool (medium alpha, e.g., 0.03), all the way to a more reactive one.
Optimized for Safety & Signal Quality: As you astutely pointed out, with the proper setting (like 0.03), it finds the perfect balance. It provides a smoother path than a standard 20-50 period EMA, which reduces whipsaws and false breakouts, leading to safer, higher-confidence signals.
Superior Trend Visualization: It gives a cleaner and more intuitive representation of the market's direction than many conventional moving averages, making it easier to "see" the trend and stick with it.
Objective Dynamic Support/Resistance: The line created with a medium alpha setting acts as a powerful dynamic support in uptrends and resistance in downtrends, offering excellent areas for entries on retests with integrated alerts.
Cons:
Requires Calibration: The only "con" is that its performance is not plug-and-play; it requires the user to find their optimal alpha value for their specific trading style and the instrument they are trading. This demands a period of testing and customization, which a standard 50-period EMA does not.
Conceptual Hurdle: For traders only familiar with period-based EMAs, the concept of a "smoothing constant" can be initially confusing compared to simply setting a "length."
In summary:
The Stalonte EMA is not a laggy relic. It is a highly sophisticated and adaptable tool. Its design allows for precise tuning, enabling a trader to discover a setting that offers a superior blend of stability and responsiveness—a "sweet spot" that provides safer and often more effective signals than many traditional moving averages. Thank you for pushing for a more accurate and fair assessment.
Use Case Example:
You can combine it with classical EMAs to find the perfect entry.
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Moving Average Exponential (Daily Frozen EMA)This script plots an Exponential Moving Average (EMA) based on the daily timeframe, but with a unique twist:
✅ The EMA value is frozen for the entire current daily session, only updating when a new daily candle begins.
🔍 How it works:
The EMA is calculated using the 1-day timeframe, regardless of the chart's current timeframe.
This EMA value remains fixed throughout the day — it doesn't fluctuate intrabar.
It updates only once the daily candle has closed, providing a stable and reliable reference point during the trading day.
The default is the 5 day EMA but can be changed to any EMA timeframe you desire such as 9, 21, 50, 100. 200, etc.
✨ Additional Features:
✅ Optional smoothing with various moving average types (SMA, EMA, WMA, SMMA, VWMA).
✅ Optional Bollinger Bands on top of the smoothed EMA.
✅ Adjustable settings for EMA length, smoothing type, Bollinger Band deviation, and display options.
🛠️ Use Cases:
Ideal for traders who want a non-reactive EMA during intraday trading.
Helps reduce signal noise by anchoring EMA to higher timeframe structure.
Useful for strategy development where EMA should represent confirmed daily bias only.
Hope this helps, happy trading! Indicator
Hybrid Triple Exponential Smoothing🙏🏻 TV, I present you HTES aka Hybrid Triple Exponential Smoothing, designed by Holt & Winters in the US, assembled by me in Saint P. I apply exponential smoothing individually to the data itself, then to residuals from the fitted values, and lastly to one-point forecast (OPF) errors, hence 'hybrid'. At the same time, the method is a closed-form solution and purely online, no need to make any recalculations & optimize anything, so the method is O(1).
^^ historical OPFs and one-point forecasting interval plotted instead of fitted values and prediction interval
Before the How-to, first let me tell you some non-obvious things about Triple Exponential smoothing (and about Exponential Smoothing in general) that not many catch. Expo smoothing seems very straightforward and obvious, but if you look deeper...
1) The whole point of exponential smoothing is its incremental/online nature, and its O(1) algorithm complexity, making it dope for high-frequency streaming data that is also univariate and has no weights. Consequently:
- Any hybrid models that involve expo smoothing and any type of ML models like gradient boosting applied to residuals rarely make much sense business-wise: if you have resources to boost the residuals, you prolly have resources to use something instead of expo smoothing;
- It also concerns the fashion of using optimizers to pick smoothing parameters; honestly, if you use this approach, you have to retrain on each datapoint, which is crazy in a streaming context. If you're not in a streaming context, why expo smoothing? What makes more sense is either picking smoothing parameters once, guided by exogenous info, or using dynamic ones calculated in a minimalistic and elegant way (more on that in further drops).
2) No matter how 'right' you choose the smoothing parameters, all the resulting components (level, trend, seasonal) are not pure; each of them contains a bit of info from the other components, this is just how non-sequential expo smoothing works. You gotta know this if you wanna use expo smoothing to decompose your time series into separate components. The only pure component there, lol, is the residuals;
3) Given what I've just said, treating the level (that does contain trend and seasonal components partially) as the resulting fit is a mistake. The resulting fit is level (l) + trend (b) + seasonal (s). And from this fit, you calculate residuals;
4) The residuals component is not some kind of bad thing; it is simply the component that contains info you consciously decide not to include in your model for whatever reason;
5) Forecasting Errors and Residuals from fitted values are 2 different things. The former are deltas between the forecasts you've made and actual values you've observed, the latter are simply differences between actual datapoints and in-sample fitted values;
6) Residuals are used for in-sample prediction intervals, errors for out-of-sample forecasting intervals;
7) Choosing between single, double, or triple expo smoothing should not be based exclusively on the nature of your data, but on what you need to do as well. For example:
- If you have trending seasonal data and you wanna do forecasting exclusively within the expo smoothing framework, then yes, you need Triple Exponential Smoothing;
- If you wanna use prediction intervals for generating trend-trading signals and you disregard seasonality, then you need single (simple) expo smoothing, even on trending data. Otherwise, the trend component will be included in your model's fitted values → prediction intervals.
8) Kind of not non-obvious, but when you put one smoothing parameter to zero, you basically disregard this component. E.g., in triple expo smoothing, when you put gamma and beta to zero, you basically end up with single exponential smoothing.
^^ data smoothing, beta and gamma zeroed out, forecasting steps = 0
About the implementation
* I use a simple power transform that results in a log transform with lambda = 0 instead of the mainstream-used transformers (if you put lambda on 2 in Box-Cox, you won't get a power of 2 transform)
* Separate set of smoothing parameters for data, residuals, and errors smoothing
* Separate band multipliers for residuals and errors
* Both typical error and typical residuals get multiplied by math.sqrt(math.pi / 2) in order to approach standard deviation so you can ~use Z values and get more or less corresponding probabilities
* In script settings → style, you can switch on/off plotting of many things that get calculated internally:
- You can visualize separate components (just remember they are not pure);
- You can switch off fit and switch on OPF plotting;
- You can plot residuals and their exponentially smoothed typical value to pick the smoothing parameters for both data and residuals;
- Or you might plot errors and play with data smoothing parameters to minimize them (consult SAE aka Sum of Absolute Errors plot);
^^ nuff said
More ideas on how to use the thing
1) Use Double Exponential Smoothing (data gamma = 0) to detrend your time series for further processing (Fourier likes at least weakly stationary data);
2) Put single expo smoothing on your strategy/subaccount equity chart (data alpha = data beta = 0), set prediction interval deviation multiplier to 1, run your strat live on simulator, start executing on real market when equity on simulator hits upper deviation (prediction interval), stop trading if equity hits lower deviation on simulator. Basically, let the strat always run on simulator, but send real orders to a real market when the strat is successful on your simulator;
3) Set up the model to minimize one-point forecasting errors, put error forecasting steps to 1, now you're doing nowcasting;
4) Forecast noisy trending sine waves for fun.
^^ nuff said 2
All Good TV ∞ Indicator
Exponential Grid [Phi, Pi, Euler]If you disagree with one of the EMH principles that price is too random, then by definition you must agree that historic price has deterministic function to a scenario ahead.
I personally believe that constants like phi, pi and e can mimic exponential growth of the price.
In this script, first grid is based on the Lowest price multiplied with self fraction of the constant.
For example:
If you are familiar with fib ratio 1.272, then you must know that it is 1.618 to the power of 0.5.
With default settings of exponent step 0.25
First grid = Lowest price x phi^0.25
Second grid = Lowest price x phi^0.25x2
Third grid = Lowest price x phi^0.25x3 and so on
The script will automatically find the lowest price and update the grid values.
Or you can set up your custom Lowest price manually if you feel like the All Time Low level loses its relevance value after long period.
There are 64 grids including Lowest price level. And it wasn't by a chance. Pine Script has a limitation of max 64 plots. Number of grids shown in the chart depends on the highest price. Once price breaks above ATH a couple of next grids will be plotted automatically. In most cases if everything is plotted, the chart appears squeezed and you'll need to zoom in to see it. Therefore, I adjusted it relatively to the scale of the chart for the comfort.
In some cases 64 plots aren't enough to cover the whole chart. For example, let's take a look at NVIDIA chart:
Since the price has started with 0.0333, it is way too small to cover all with default settings.
We are left with 2 choices:
Either Enable "Round"
OR increase Exponent Step (from 0.25 to 0.5 in the particular example below)
If you set constant to pi or e which is a bigger number than phi, expect the gaps to be bigger. To reduce it to a more gradual way of expansion you can decrease Exponent Step.
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ChartRage - ELMAELMA - Exponential Logarithmic Moving Average
This is a new kind of moving average that is using exponential normalization of a logarithmic formula. The exponential function is used to average the weight on the moving average while the logarithmic function is used to calculate the overall price effect.
Features and Settings:
◻️ Following rate of change instead of absolute levels
◻️ Choose input source of the data
◻️ Real time signals through price interaction
◻️ Change ELMA length
◻️ Change the exponential decay rate
◻️ Customize base color and signal color
Equation of the ELMA:
This formula calculates a weighted average of the logarithm of prices, where more recent prices have a higher weight. The result is then exponentiated to return the ELMA value. This approach emphasizes the relative changes in price, making the ELMA sensitive to the % rate of change rather than absolute price levels. The decay rate can be adjusted in the settings.
Comparison EMA vs ELMA:
In this image we see the differences to the Exponential Moving Average.
Price Interaction and earlier Signals:
In this image we have added the bars, so we can see that the ELMA provides different signals of resistance and support zones and highlights them, by changing to the color yellow, when prices interact with the ELMA.
Strategy by trading Support and Resistance Zones:
The ELMA helps to evaluate trends and find entry points in bullish market conditions, and exit points in bearish conditions. When prices drop below the ELMA in a bull market, it is considered a buying signal. Conversely, in a bear market, it serves as an exit signal when prices trade above the ELMA.
Volatile Markets:
The ELMA works on all timeframes and markets. In this example we used the default value for Bitcoin. The ELMA clearly shows support and resistance zones. Depending on the asset, the length and the decay rate should be adjusted to provide the best results.
Real Time Signals:
Signals occur not after a candle closes but when price interacts with the ELMA level, providing real time signals by shifting color. (default = yellow)
Disclaimer* All analyses, charts, scripts, strategies, ideas, or indicators developed by us are provided for informational and educational purposes only. We do not guarantee any future results based on the use of these tools or past data. Users should trade at their own risk.
This work is licensed under Attribution-NonCommercial-ShareAlike 4.0 International
creativecommons.org Indicator
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T3 + BB
TRES EMAS + BANDAS DE BOLLINGER
INDICADOR 1: TRES EMAS (MEDIA MOVIL EXPONENCIAL)
Con este indicador puede visualizar el promedio de precios con mayor peso a los datos mas recientes.
Se calculan y dibujan tres medias móviles exponenciales: 4, 20 y 200 últimas velas.
-Rápida EMA1 = 4, paso = 1
-Media EMA2 = 20, paso = 2
-Lenta EMA 3 = 200, paso = 10
INDICADOR 2: BANDAS DE BOLLINGER
Con este indicador podrá ver la fuerza y la tendencia del mercado, es decir la mide la volatilidad del precio del activo.
Si el precio sobrepasa la banda superior, el activo está sobrecomprado.
Si el precio sobrepasa la banda inferior, el activo está sobrevendido.
Longitud tendencia - BASE = 20, paso = 1
Desviación Estándar - Multiplicador = 2, paso = 0.2
Estos 2 indicadores sirven para todo tipo de activos: FOREX, CRIPTO, CFD´s, ETC.
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THREE EMAS + BOLLINGER BANDS
INDICATOR 1: THREE EMAS (EXPONENTIAL MOVING AVERAGE)
With this indicator you can visualize the average of prices with greater weight to the most recent data.
Three exponential moving averages are calculated and drawn: 4, 20 and 200 last candles.
-Fast EMA1 = 4, step = 1
-Average EMA2 = 20, step = 2
-Slow EMA 3 = 200, step = 10
INDICATOR 2: BOLLINGER BANDS
With this indicator you can see the strength and trend of the market, that is, it is measured by the volatility of the asset price.
If the price goes above the upper band, the asset is overbought.
If the price goes above the lower band, the asset is oversold.
Trend length - BASE = 20, step = 1
Standard Deviation - Multiplier = 2, step = 0.2
These 2 indicators are used for all types of assets: FOREX, CRYPT, CFD's, ETC. Indicator
Fear Of Missing Out grid of forex tradingAbstract
This script finds potential safe grids placing limit orders without fear of missing out.
This script computes grids according to power of 1.0025 .
You can reference those price levels for your trading.
Introduction
Grid trading is a popular trading method.
Traders plan several price levels as grids and repeat buying at lower grids and selling at higher grids.
Grids can be round number like multiple of 100 pips.
Grids can also be support and resistance according to price history.
Some traders may think they need to adjust grids to trade.
However, there are several problems in choosing grids.
One problem is rate of change is related and therefore exponential. 20 to 30 is different from 30 to 40.
Another interesting point is there are some special impressing reversal price levels.
Several months ago, I had a question why usdjpy bounced near 108.3 .
After using a calculator, I found that 108.3 = 100 * 1.083 ≒ 100 * pow(1.0025,31) .
1.0025 , as known as 0.25% of change, is a potential stop out zone.
Therefore, we can compute grids and one grid is a little more than 1.0025 times than an another one.
After we finished computing grids, we can consider buy and sell near those grids.
Note that different traders may obtain different grid values.
For example, from 1.0 to 2.0 , it can be splited as 270 grids or 277 grids because pow(1.0025,277)<2 .
Those grids cannot always imply potential reversal points but they can be useful for traders looking for 0.25% profit targets with reducing fearing of buying or selling too early.
Computing grids
This script split from 1.0 to 10.0 into three segments.
One is 1.0 to 2.0 .
The second segment is from 2.0 to 5.0 .
The third segment is from 5.0 to 10.0 .
This script does the same thing for 0.1 to 1.0 , 10.0 to 100.0 , and so on.
For 1.0 to 2.0 and 5.0 to 10.0 , this script split a segment as 270 grids.
For 2.0 to 5.0 , this script split a segment as 360 grids.
The last step is display the next grids to the daily low and daily high.
Maybe also display the grids behind grids shown.
Parameters
x1,x2,x3,x4 : display the next x1,x2,x3,x4 grids to daily high and daily low. 1 means the next grid to daily high and daily low. 2 means the next grid to 1.
x_seg : default 2.0 . This script split from 1.0 to 10.0 into three segments. One is 1.0 to x_seg. The second segment is from x_seg to 10.0/x_seg . The third segment is from 10.0/x_seg to 10.0 .
x_grid1 : how many grids in the first segment
x_grid2 : how many grids in the second segment
x_lowprice : add this number for bigger grid distance. Generally, you don't need this number when trading forex but you may need it in stock trading. For stocks with price between 50 to 100, I recommend you use x_lowprice=100.
Conclusion and suggestions
This script can find potential grids for trading.
If price touches grids usually, we can consider buy and sell after price touches grids.
If price reverses before touching grids usually, we may consider buy and sell before price touches grids.
Those grids can remind us don't buy too much unless the price touches the next grid.
For instruments with less volatility, maybe we need more grids.
For traders with more money, they may also consider more grids for more dedicated range trading to collect more profit.
Reference
Sorry, I forgot them.
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