FVG Breakaway/3rd Candle (Arjo) [MK]Simple script to identify FVGs (Fair Value Gaps) on the current chart timeframe. The script differs from other FVG indicators on the Tradingview platform by using Arjos 3rd candle rule to identify which gaps are 'Breakway Gaps' and which Gaps are likely to be returned to.
NOTE: As with all 'trading rules' this theory is not 100% accurate.
default settings:
Breakaway Gaps = YELLOW
Gaps that price may return to = GREEN
Mitigated Gaps = 100% TRANSPARENT
What is a FVG:
A FVG is a price area defined by a 3 candle pattern. For a bullish FVG, the low of the 3rd candle must be higher than the high of the 1st candle. This then leaves an area that is drawn as in the example below:
A bearish FVG is defined by the high of the 3rd candle being lower than the low of the 1st candle, as shown in the example below:
FVGs can act like magnets where price will either retrace to or reach for, therefore they can be used as entry points and also for take profit target levels.
If for example, a trader would like to use an FVG for an entry, it would be useful to know which FVGs are more likely for price to re-enter and which FVG will be left un-touched. FVGs that are likely to be left un-touched by price are called 'Breakaway Gaps'.
How do we define a 'Breakaway Gap':
First we identify FVGs using the rules stated above, then we look to see where the 3rd candle closed in relation to the 2nd candle. For a bullish 'Breakaway Gap' we want to see the 3rd candle close above the high of the 2nd candle. An example of a bullish Breakaway Gap is shown in the example below:
A bearish 'Breakaway Gap' is defined by the close of the 3rd candle being lower than the low of the 2nd candle. An example is shown below:
How do we define an FVG that price may return to:
Any gap that does not meet the above rules for a 'Breakway Gap' is therefore considered an FVG that price may return to. So for a bullish FVG that price may return to we would look to see if the close of the 3rd candle is above the high of the 2nd candle. If it is not above the high of the 2nd candle then it more likely that price will retrace into the FVG before continuing higher. An example is shown below:
A bearish gap that price may return to is defined by the close of the 3rd candle not being lower than the low of the 2nd candle. An example is shown below:
The indicator is based on the teachings of 'Arjo'. Note: breakaway gaps will only remain 'breakaway' until a liquidity level is reached. Breakaways therefore do not remain 'breakaway' forever. Users of the indicators must fully comprehend this theory before using the indicator with live markets.
Users of the script should be fully aware of this concept and also have conducted thorough backtesting using a large data set before using this indicator with live accounts.
在腳本中搜尋"liquidity"
MTF OB Supply Demand ZonesHello everyone,
This exceptional indicator provides you with visual representations of bullish and bearish order blocks or supply and demand zones across multiple timeframes. In simple terms, bullish order blocks are represented by a small red candle followed by a large red candle, while bearish order blocks are depicted as a small green candle followed by a large red candle. Supply and demand zones are drawn by using order blocks.
Features:
Display order blocks from up to three different timeframes.
Customize the maximum number of boxes shown and the colors of the zones.
Choose from three different modes: OB (Order Block), Extended OB, and Supply/Demand.
Mode Descriptions:
OB: Includes the body of the candle.
Extended OB: Encompasses the body and wick of the candle.
Supply/Demand: Covers the body, wick, and half the body of the large candle.
Usage:
Ensure that charts 2 and 3 are set to a higher timeframe. For modes 2 and 3, it’s recommended to reduce the maximum number of boxes shown. The zones or boxes are transparent, allowing for overlap. This feature aids in identifying reversal zones or confirmed zones. The more intense the color, the stronger the confirmation. If a green zone overlaps a red zone (or vice versa), it signifies a reversal zone.
Thank you for checking out this indicator!
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Additional Information:
Order blocks refer to specific price areas where large market participants, such as institutional traders, have previously placed significant buy or sell orders. These clusters of orders can impact price movement, liquidity, and market sentiment.
Order blocks are a strategic approach to identifying key levels of support and resistance based on the behavior of institutional traders. These key levels are then utilized as entry or exit points for trades.
An order block is an area where there has been a large concentration of limit orders awaiting execution. These blocks are identified on a chart by observing previous price action and pinpointing areas where the price experienced significant movement or abrupt changes in direction.
Order blocks are used in the following popular trading philosophies:
Smart Money Concepts (SMC)
Inner Circle Trading (ICT)
Price Action
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Credits to: @AGFXTRADING
Crypto Liquidation Heatmap [LuxAlgo]The Crypto Liquidation Heatmap tool offers real-time insights into the liquidations of the top cryptocurrencies by market capitalization, presenting the current state of the market in a visually accessible format. Assets are sorted in descending order, with those experiencing the highest liquidation values placed at the top of the heatmap.
Additional details, such as the breakdown of long and short liquidation values and the current price of each asset, can be accessed by hovering over individual boxes.
🔶 USAGE
The crypto liquidation heatmap tool provides real-time insights into liquidations across all timeframes for the top 29 cryptocurrencies by market capitalization. The assets are visually represented in descending order, prioritizing assets with the highest liquidation values at the top of the heatmap.
Different colors are used to indicate whether long or short liquidations are dominant for each asset. Green boxes indicate that long liquidations surpass short liquidations, while red boxes indicate the opposite, with short liquidations exceeding long liquidations.
Hovering over each box provides additional details, such as the current price of the asset, the breakdown of long and short liquidation values, and the duration for the calculated liquidation values.
🔶 DETAILS
🔹Crypto Liquidation
Crypto liquidation refers to the process of forcibly closing a trader's positions in the cryptocurrency market. It occurs when a trader's margin account can no longer support their open positions due to significant losses or a lack of sufficient margin to meet the maintenance requirements. Liquidations can be categorized as either a long liquidation or a short liquidation.
A long liquidation occurs when long positions are being liquidated, typically due to a sudden drop in the price of the asset being traded. Traders who were bullish on the asset and had opened long positions will face losses as the market moves against them.
On the other hand, a short liquidation occurs when short positions are being liquidated, often triggered by a sudden spike in the price of the asset. Traders who were bearish on the asset and had opened short positions will face losses as the market moves against them.
🔹Liquidation Data
It's worth noting that liquidation data is not readily available on TradingView. However, we recognize the close correlation between liquidation data, trading volumes, and asset price movements. Therefore, this script analyzes accessible data sources, extracts necessary information, and offers an educated estimation of liquidation data. It's important to emphasize that the presented data doesn't reflect precise quantitative values of liquidations. Traders and analysts should instead focus on observing changes over time and identifying correlations between liquidation data and price movements.
🔶 SETTINGS
🔹Cryptocurrency Asset List
It is highly recommended to select instruments from the same exchange with the same currency to maintain proportional integrity among the chosen assets, as different exchanges may have varying trading volumes.
Supported currencies include USD, USDT, USDC, USDP, and USDD. Remember to use the same currency when selecting assets.
List of Crypto Assets: The default options feature the top 29 cryptocurrencies by market capitalization, currently listed on the Binance Exchange. Please note that only crypto assets are supported; any other asset type will not be processed or displayed. To maximize the utility of this tool, it is crucial to heed the warning message displayed above.
🔹Liquidation Heatmap Settings
Position: Specifies the placement of the liquidation heatmap on the chart.
Size: Determines the size of the liquidation heatmap displayed on the chart.
🔶 RELATED SCRIPTS
Liquidations-Meter
Liquidation-Estimates
Liquidation-Levels
Smart Money Interest Index [AlgoAlpha]🌟 Smart Money Interest Index by AlgoAlpha 🌟
Welcome to the innovative Smart Money Interest Index indicator, designed meticulously by AlgoAlpha to revolutionize the way you trade! 📈🧠 This indicator is engineered to decipher the activities of smart money investors relative to the less informed (dumb money) and dynamically display their dominance in the trading landscape through a sophisticated visual index. 🚀💹
🔑 Key Features:
- Smart vs. Dumb Money Analysis: Tracks and compares the movements of smart money (informed investors) and dumb money (general public) within the market to identify potential investment signals.
- Relative Strength Index (RSI) Based Ratios: Utilizes RSI for both smart and dumb money to create a ratio that indicates buying or selling pressures.
- Dynamic Normalization: Employs a long-term peak normalization over a customizable period to ensure the index remains relevant regardless of market conditions.
- Visual Thresholds and Signals: Highlights significant shifts in market dynamics with color-coded thresholds, making it easier to spot changes at a glance.
🛠 How to Use the Smart Money Interest Index:
🔹 🚀 Step 1: Adding the Indicator
- Add the indicator to your favourites.
- Customize the settings according to your analysis needs:
- `Index Period`, `Volume Flow Period`, `Normalization Period`, `High Interest Threshold`
🔹 📊 Step 2: Interpretation of the Index
- Monitor the index plot; a rising index suggests increasing smart money interest, potentially indicating a buying opportunity.
- A value above the high interest threshold (in yellow) highlights significant interest by smart money, suggesting a good time to buy.
🔹 🔔 Step 3: Setting Alerts
- Configure alerts to notify you when the index crosses above the set threshold, enabling you to capitalize on trading opportunities timely and efficiently.
📐 Basic Logic Overview:
The Smart Money Interest Index by AlgoAlpha provides a unique metric that contrasts the investment behaviors of informed (smart money) and general (dumb money) investors. Utilizing the Relative Strength Index (RSI), this indicator evaluates the trading pressure exerted by both groups over specified periods, then forms a ratio of these activities to identify dominance in buying or selling trends. For example, when we see dumb money selling and smart buying, this suggests that the conditions for buying the asset is optimal as smart money is willing to buy the dip. The outputs are normalized against the highest values observed in a user-defined term to maintain consistency through varying market conditions. When the index exceeds a certain threshold, it suggests that smart money presence is particularly strong, possibly indicating that smart money is looking to enter positions on the asset. This tool serves as a sophisticated visual guide to understanding market dynamics and making well-informed trading decisions based on the activities of market-savvy investors. Smart money activity is identified during areas of low volume and the opposite for dumb money, the indicator uses the NVI and PVI metrics as its foundation for smart and dumb money analysis.
📊 Enhance Your Trading Strategy:
Leverage the Smart Money Interest Index to gain deeper insights into market dynamics and enhance your decision-making process with a powerful, data-driven approach. Whether you're looking to identify entry points or set strategic exits, this tool is designed to provide you with the competitive edge you need in the fast-paced world of trading. 🌐✨
Transform your trading with the power of smart money analysis—start using the Smart Money Interest Index today! 🚀🔔
Dynamic Price Oscillator (Zeiierman)█ Overview
The Dynamic Price Oscillator (DPO) by Zeiierman is designed to gauge the momentum and volatility of asset prices in trading markets. By integrating elements of traditional oscillators with volatility adjustments and Bollinger Bands, the DPO offers a unique approach to understanding market dynamics. This indicator is particularly useful for identifying overbought and oversold conditions, capturing price trends, and detecting potential reversal points.
█ How It Works
The DPO operates by calculating the difference between the current closing price and a moving average of the closing price, adjusted for volatility using the True Range method. This difference is then smoothed over a user-defined period to create the oscillator. Additionally, Bollinger Bands are applied to the oscillator itself, providing visual cues for volatility and potential breakout signals.
█ How to Use
⚪ Trend Confirmation
The DPO can serve as a confirmation tool for existing trends. Traders might look for the oscillator to maintain above or below its mean line to confirm bullish or bearish trends, respectively. A consistent direction in the oscillator's movement alongside price trend can provide additional confidence in the strength and sustainability of the trend.
⚪ Overbought/Oversold Conditions
With the application of Bollinger Bands directly on the oscillator, the DPO can highlight overbought or oversold conditions in a unique manner. When the oscillator moves outside the Bollinger Bands, it signifies an extreme condition.
⚪ Volatility Breakouts
The width of the Bollinger Bands on the oscillator reflects market volatility. Sudden expansions in the bands can indicate a breakout from a consolidation phase, which traders can use to enter trades in the direction of the breakout. Conversely, a contraction suggests a quieter market, which might be a signal for traders to wait or to look for range-bound strategies.
⚪ Momentum Trading
Momentum traders can use the DPO to spot moments when the market momentum is picking up. A sharp move of the oscillator towards either direction, especially when crossing the Bollinger Bands, can indicate the start of a strong price movement.
⚪ Mean Reversion
The DPO is also useful for mean reversion strategies, especially considering its volatility adjustment feature. When the oscillator touches or breaches the Bollinger Bands, it indicates a deviation from the normal price range. Traders might look for opportunities to enter trades anticipating a reversion to the mean.
⚪ Divergence Trading
Divergences between the oscillator and price action can be a powerful signal for reversals. For instance, if the price makes a new high but the oscillator fails to make a corresponding high, it may indicate weakening momentum and a potential reversal. Traders can use these divergence signals to initiate counter-trend moves.
█ Settings
Length: Determines the lookback period for the oscillator and Bollinger Bands calculation. Increasing this value smooths the oscillator and widens the Bollinger Bands, leading to fewer, more significant signals. Decreasing this value makes the oscillator more sensitive to recent price changes, offering more frequent signals but with increased noise.
Smoothing Factor: Adjusts the degree of smoothing applied to the oscillator's calculation. A higher smoothing factor reduces noise, offering clearer trend identification at the cost of signal timeliness. Conversely, a lower smoothing factor increases the oscillator's responsiveness to price movements, which may be useful for short-term trading but at the risk of false signals.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Volume Delta Candles [LuxAlgo]Volume Delta Candles provides insights about Intrabar trading activity in an easy-to-interpret manner. Lower timeframe or real-time data is used for displaying Volume Delta percentage against the total volume as a coloured bar part.
The script also highlights the intrabar price with the maximum trading activity, as well as complementary information.
🔶 USAGE
The tool focuses on intrabar volume to provide more information about the trading activity associated with a candle, without having to use an external volume indicator.
Each indicator components is further explained below:
🔹 Volume Delta
The volume delta is obtained by the difference between buy volume and sell volume, where buy volume is the volume associated with a bullish intrabar candle, and sell volume with a bearish intrabar candle.
Positive volume delta is displayed with a green candle area, while negative delta is displayed with a red candle area.
🔹 Bar Coloring
The script displays VD as a percentage of the whole, or from the candle half, depending on the setting ' Display '.
Bars can be coloured as follows:
Full (100%) when Display is set at ' Full Bar '
Half (50% or 100% of half a bar) when Display is set at ' Half Bar '
A negative VD (more bearish than bullish volume) will fill the bar from the top (or centre) of the bar towards the bottom, and a positive VD will fill a bar from the bottom (or centre) of the bar towards the top.
A negative VD on a green candle will show a red-coloured VD against a green-bordered candle. On the other hand, a positive VD on a red candle will show a green-coloured VD against a red-bordered candle.
Colours for VD sentiment opposite to the candle sentiment can be set differently if desired.
🔹 Highest Volume Price Level
The script displays a white (black on light mode) line highlighting the intrabar price level with the highest volume.
When ' Show Previous Max Volume Price ' is checked, a white (black on light mode) dot is displayed 1 bar to the right.
🔶 DETAILS
🔹 Tick/LTF data
The above example used Lower TimeFrame (LTF) data.
The following example uses real-time tick data ( Settings -> Data From )
Both options, LTF or tick data, will show a vertical dotted line where the data starts.
🔹 LTF settings
When ' Data from ' LTF is chosen and ' Auto ' enabled, the LTF will be the nearest possible x times smaller TF than the current TF. When 'Premium' is disabled, the minimum TF will always be 1 minute to ensure TradingView plans lower than Premium don't get an error.
Examples with current Daily TF (when Premium is enabled):
500 : 3 minute LTF
1500 (default): 1 minute LTF
5000: 30 seconds LTF (1 minute if Premium is disabled)
🔹 Notes
Different LTFs give different data, which means different results; this doesn't mean it isn't correct; they are just different data sets.
(LTF is displayed at the top right corner)
To ensure maximum visibility of values, we recommend using Bars from the Bar's style menu.
🔶 SETTINGS
Data from: Lower TimeFrame or real-time Tick data
Resolution: LTF setting
Auto + multiple: Adjusts the initial set resolution
Premium: Enable when your TradingView plan is Premium or higher
🔹 Intrabar Data
Colours
Display: Full/Half bar
Show previous max volume price: White/black dot, showing previous highest volume price level
🔹 Table
Show TF: Show LTF at the top right corner
Colour + table text size
🔹 Details
Show details: label with 'Volume', 'Delta' (VD) and '%'
See USAGE for more information
PVSRA Candles Auto OverrideWhat does this “PVSRA Candles Auto Override” Indicator
do?
This indicator automates PVSRA analysis for crypto traders. It finds the corresponding Binance Perpetual Futures chart for the current instrument, then replaces the current chart's volume profile with the perpetual futures data (if available) to ensure the PVSRA calculation uses the most relevant volume. This not only reduces human error during market scans but also automatically selects the appropriate Binance Perpetual Futures contract, saving time and improving the accuracy of PVSRA calculations.
How can a trader use this indicator?
This helps the trader to identify if there is volume data available in an equivalent Binance Perpetual Futures chart and automatically displays it, making it easier to switch coins whilst viewing the market. Why do we want to use Binance Perpetual Futures Volume? In most markets Binance volume surpasses those of other crypto exchanges so this will give us a better view on the volume spikes in the market.
What is PVSRA and how can I trade using this indicator?
PVSRA candles are a type of candlestick chart formatting. PVSRA stands for Price, Volume, Support and Resistance Analysis.
Here's a breakdown of what PVSRA candles aim to achieve:
Combine multiple factors: They take into account price movement, trading volume, and support and resistance levels to identify potential trading opportunities.
Highlight potential imbalances: By color-coding candles based on PVSRA analysis, they aim to show areas of high volume activity, potentially representing imbalances created by market makers (large institutions that influence price).
Identify areas of revisit: The theory is that these high-volume zones may be revisited by the market in the future, as there's "unrecovered liquidity" in those areas.
Usage of the Indicator:
By default the indicator will automatically use the Equivalent Binance Perpetual Chart for the Data
You can override the symbol manually if you what to view another instrument’s data.
Trailing Management (Zeiierman)█ Overview
The Trailing Management (Zeiierman) indicator is designed for traders who seek an automated and dynamic approach to managing trailing stops. It helps traders make systematic decisions regarding when to enter and exit trades based on the calculated risk-reward ratio. By providing a clear visual representation of trailing stop levels and risk-reward metrics, the indicator is an essential tool for both novice and experienced traders aiming to enhance their trading discipline.
The Trailing Management (Zeiierman) indicator integrates a Break-Even Curve feature to enhance its utility in trailing stop management and risk-reward optimization. The Break-Even Curve illuminates the precise point at which a trade neither gains nor loses value, offering clarity on the risk-reward landscape. Furthermore, this precise point is calculated based on the required win rate and the risk/reward ratio. This calculation aids traders in understanding the type of strategy they need to employ at any given time to be profitable. In other words, traders can, at any given point, assess the kind of strategy they need to utilize to make money, depending on the price's position within the risk/reward box.
█ How It Works
The indicator operates by computing the highest high and the lowest low over a user-defined period and then applying this information to determine optimal trailing stop levels for both long and short positions.
Directional Bias:
It establishes the direction of the market trend by comparing the index of the highest high and the lowest low within the lookback period.
Bullish
Bearish
Trailing Stop Adjustment:
The trailing stops are adjusted using one of three methods: an automatic calculation based on the median of recent peak differences, pivot points, or a fixed percentage defined by the user.
The Break-Even Curve:
The Break-Even Curve, along with the risk/reward ratio, is determined through the trailing method. This approach utilizes the current closing price as a hypothetical entry point for trades. All calculations, including those for the curve, are based on this current closing price, ensuring real-time accuracy and relevance. As market conditions fluctuate, the curve dynamically adjusts, offering traders a visual benchmark that signifies the break-even point. This real-time adjustment provides traders with an invaluable tool, allowing them to visually track how shifts in the market could impact the point at which their trades neither gain nor lose value.
Example:
Let's say the price is at the midpoint of the risk/reward box; this means that the risk/reward ratio should be 1:1, and the minimum win rate is 50% to break even.
In this example, we can see that the price is near the stop-loss level. If you are about to take a trade in this area and would respect your stop, you only need to have a minimum win rate of 11% to earn money, given the risk/reward ratio, assuming that you hold the trade to the target.
In other words, traders can, at any given point, assess the kind of strategy they need to employ to make money based on the price's position within the risk/reward box.
█ How to Use
Market Bias:
When using the Auto Bias feature, the indicator calculates the underlying market bias and displays it as either bullish or bearish. This helps traders align their trades with the underlying market trend.
Risk Management:
By observing the plotted trailing stops and the risk-reward ratios, traders can make strategic decisions to enter or exit positions, effectively managing the risk.
Strategy selection:
The Break-Even Curve is a powerful tool for managing risk, allowing traders to visualize the relationship between their trailing stops and the market's price movements. By understanding where the break-even point lies, traders can adjust their strategies to either lock in profits or cut losses.
Based on the plotted risk/reward box and the location of the price within this box, traders can easily see the win rate required by their strategy to make money in the long run, given the risk/reward ratio.
Consider this example: The market is bullish, as indicated by the bias, and the indicator suggests looking into long trades. The price is near the top of the risk/reward box, which means entering the market right now carries a huge risk, and the potential reward is very low. To take this trade, traders must have a strategy with a win rate of at least 90%.
█ Settings
Trailing Method:
Auto: The indicator calculates the trailing stop dynamically based on market conditions.
Pivot: The trailing stop is adjusted to the highest high (long positions) or lowest low (short positions) identified within a specified lookback period. This method uses the pivotal points of the market to set the trailing stop.
Percentage: The trailing stop is set at a fixed percentage away from the peak high or low.
Trailing Size (prd):
This setting defines the lookback period for the highest high and lowest low, which affects the sensitivity of the trailing stop to price movements.
Percentage Step (perc):
If the 'Percentage' method is selected, this setting determines the fixed percentage for the trailing stop distance.
Set Bias (bias):
Allows users to set a market bias which can be Bullish, Bearish, or Auto, affecting how the trailing stop is adjusted in relation to the market trend.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Binance Open Interest (+SMA)# Binance Open Interest + SMA
An indicator showing open interest (OI) in US dollars ($) for Binance USD-margined perpetual contracts. This means the indicator shows the total value of all open perpetual contracts on the Binance platform for the ticker being charted. As such, it can provide insights into market activity for an asset and trading interest, as rising open interest suggests many traders are opening new positions and new money is flowing into the market, and vice-versa. It is also an indicator of the liquidity of the asset's perpetuals contracts, as it reflects the amount of money in a given perpetuals market.
The candle data is shown as expected, with green candles indicating the IU at close is greater than at open, red indicating a lower OI at close than open, with the bodies indicating the open and close prices, and any wicks representing an OI value within that timeframe that went above or below the closing or opening OI.
Unlike other Binance open interest indicators on the platform, this one does not require user input and will automatically pull open interest data for the ticker being looked at, allowing for quicker access to open interest data. It also presents the open interest data in candle format, providing more detail into the open interest at a given timeframe.
Please note that this indicator will only work for assets which Binance offers USD-margined perpetual contracts for, and otherwise will not work.
## Instructions:
Simply add the indicator to your chart and open the asset you would like to chart. If a Binance perpetual contract exists for the asset, the open interest value will be charted. If no chart is generated, no Binance open interest data is available for charting.
To remove the SMA, uncheck the “SMA” box in the style section in the indicator settings. You can also change the source and length of time the SMA data is calculated from in the inputs section. By default, it is based off of the closing value and a length of 15 timeframes.
## Chart example:
The chart shows the price of Ethereum, and below it this indicator for open interest on Binance for their Ethereum perpetual contracts. We can see here open interest is rising steadily, indicating rising interest in holding perpetual contracts backed by Ethereum.
Danger Signals from The Trading MindwheelThe " Danger Signals " indicator, a collaborative creation from the minds at Amphibian Trading and MARA Wealth, serves as your vigilant lookout in the volatile world of stock trading. Drawing from the wisdom encapsulated in "The Trading Mindwheel" and the successful methodologies of legends like William O'Neil and Mark Minervini, this tool is engineered to safeguard your trading journey.
Core Features:
Real-Time Alerts: Identify critical danger signals as they emerge in the market. Whether it's a single day of heightened risk or a pattern forming, stay informed with specific danger signals and a tally of signals for comprehensive decision-making support. The indicator looks for over 30 different signals ranging from simple closing ranges to more complex signals like blow off action.
Tailored Insights with Portfolio Heat Integration: Pair with the "Portfolio Heat" indicator to customize danger signals based on your current positions, entry points, and stops. This personalized approach ensures that the insights are directly relevant to your trading strategy. Certain signals can have different meanings based on where your trade is at in its lifecycle. Blow off action at the beginning of a trend can be viewed as strength, while after an extended run could signal an opportunity to lock in profits.
Forward-Looking Analysis: Leverage the 'Potential Danger Signals' feature to assess future risks. Enter hypothetical price levels to understand potential market reactions before they unfold, enabling proactive trade management.
The indicator offers two different modes of 'Potential Danger Signals', Worst Case or Immediate. Worst Case allows the user to input any price and see what signals would fire based on price reaching that level, while the Immediate mode looks for potential Danger Signals that could happen on the next bar.
This is achieved by adding and subtracting the average daily range to the current bars close while also forecasting the next values of moving averages, vwaps, risk multiples and the relative strength line to see if a Danger Signal would trigger.
User Customization: Flexibility is at your fingertips with toggle options for each danger signal. Tailor the indicator to match your unique trading style and risk tolerance. No two traders are the same, that is why each signal is able to be turned on or off to match your trading personality.
Versatile Application: Ideal for growth stock traders, momentum swing traders, and adherents of the CANSLIM methodology. Whether you're a novice or a seasoned investor, this tool aligns with strategies influenced by trading giants.
Validation and Utility:
Inspired by the trade management principles of Michael Lamothe, the " Danger Signals " indicator is more than just a tool; it's a reflection of tested strategies that highlight the importance of risk management. Through rigorous validation, including the insights from "The Trading Mindwheel," this indicator helps traders navigate the complexities of the market with an informed, strategic approach.
Whether you're contemplating a new position or evaluating an existing one, the " Danger Signals " indicator is designed to provide the clarity needed to avoid potential pitfalls and capitalize on opportunities with confidence. Embrace a smarter way to trade, where awareness and preparation open the door to success.
Let's dive into each of the components of this indicator.
Volume: Volume refers to the number of shares or contracts traded in a security or an entire market during a given period. It is a measure of the total trading activity and liquidity, indicating the overall interest in a stock or market.
Price Action: the analysis of historical prices to inform trading decisions, without the use of technical indicators. It focuses on the movement of prices to identify patterns, trends, and potential reversal points in the market.
Relative Strength Line: The RS line is a popular tool used to compare the performance of a stock, typically calculated as the ratio of the stock's price to a benchmark index's price. It helps identify outperformers and underperformers relative to the market or a specific sector. The RS value is calculated by dividing the close price of the chosen stock by the close price of the comparative symbol (SPX by default).
Average True Range (ATR): ATR is a market volatility indicator used to show the average range prices swing over a specified period. It is calculated by taking the moving average of the true ranges of a stock for a specific period. The true range for a period is the greatest of the following three values:
The difference between the current high and the current low.
The absolute value of the current high minus the previous close.
The absolute value of the current low minus the previous close.
Average Daily Range (ADR): ADR is a measure used in trading to capture the average range between the high and low prices of an asset over a specified number of past trading days. Unlike the Average True Range (ATR), which accounts for gaps in the price from one day to the next, the Average Daily Range focuses solely on the trading range within each day and averages it out.
Anchored VWAP: AVWAP gives the average price of an asset, weighted by volume, starting from a specific anchor point. This provides traders with a dynamic average price considering both price and volume from a specific start point, offering insights into the market's direction and potential support or resistance levels.
Moving Averages: Moving Averages smooth out price data by creating a constantly updated average price over a specific period of time. It helps traders identify trends by flattening out the fluctuations in price data.
Stochastic: A stochastic oscillator is a momentum indicator used in technical analysis that compares a particular closing price of an asset to a range of its prices over a certain period of time. The theory behind the stochastic oscillator is that in a market trending upwards, prices will tend to close near their high, and in a market trending downwards, prices close near their low.
While each of these components offer unique insights into market behavior, providing sell signals under specific conditions, the power of combining these different signals lies in their ability to confirm each other's signals. This in turn reduces false positives and provides a more reliable basis for trading decisions
These signals can be recognized at any time, however the indicators power is in it's ability to take into account where a trade is in terms of your entry price and stop.
If a trade just started, it hasn’t earned much leeway. Kind of like a new employee that shows up late on the first day of work. It’s less forgivable than say the person who has been there for a while, has done well, is on time, and then one day comes in late.
Contextual Sensitivity:
For instance, a high volume sell-off coupled with a bearish price action pattern significantly strengthens the sell signal. When the price closes below an Anchored VWAP or a critical moving average in this context, it reaffirms the bearish sentiment, suggesting that the momentum is likely to continue downwards.
By considering the relative strength line (RS) alongside volume and price action, the indicator can differentiate between a normal retracement in a strong uptrend and a when a stock starts to become a laggard.
The integration of ATR and ADR provides a dynamic framework that adjusts to the market's volatility. A sudden increase in ATR or a character change detected through comparing short-term and long-term ADR can alert traders to emerging trends or reversals.
The "Danger Signals" indicator exemplifies the power of integrating diverse technical indicators to create a more sophisticated, responsive, and adaptable trading tool. This approach not only amplifies the individual strengths of each indicator but also mitigates their weaknesses.
Portfolio Heat Indicator can be found by clicking on the image below
Danger Signals Included
Price Closes Near Low - Daily Closing Range of 30% or Less
Price Closes Near Weekly Low - Weekly Closing Range of 30% or Less
Price Closes Near Daily Low on Heavy Volume - Daily Closing Range of 30% or Less on Heaviest Volume of the Last 5 Days
Price Closes Near Weekly Low on Heavy Volume - Weekly Closing Range of 30% or Less on Heaviest Volume of the Last 5 Weeks
Price Closes Below Moving Average - Price Closes Below One of 5 Selected Moving Averages
Price Closes Below Swing Low - Price Closes Below Most Recent Swing Low
Price Closes Below 1.5 ATR - Price Closes Below Trailing ATR Stop Based on Highest High of Last 10 Days
Price Closes Below AVWAP - Price Closes Below Selected Anchored VWAP (Anchors include: High of base, Low of base, Highest volume of base, Custom date)
Price Shows Aggressive Selling - Current Bars High is Greater Than Previous Day's High and Closes Near the Lows on Heaviest Volume of the Last 5 Days
Outside Reversal Bar - Price Makes a New High and Closes Near the Lows, Lower Than the Previous Bar's Low
Price Shows Signs of Stalling - Heavy Volume with a Close of Less than 1%
3 Consecutive Days of Lower Lows - 3 Days of Lower Lows
Close Lower than 3 Previous Lows - Close is Less than 3 Previous Lows
Character Change - ADR of Last Shorter Length is Larger than ADR of Longer Length
Fast Stochastic Crosses Below Slow Stochastic - Fast Stochastic Crosses Below Slow Stochastic
Fast & Slow Stochastic Curved Down - Both Stochastic Lines Close Lower than Previous Day for 2 Consecutive Days
Lower Lows & Lower Highs Intraday - Lower High and Lower Low on 30 Minute Timeframe
Moving Average Crossunder - Selected MA Crosses Below Other Selected MA
RS Starts Curving Down - Relative Strength Line Closes Lower than Previous Day for 2 Consecutive Days
RS Turns Negative Short Term - RS Closes Below RS of 7 Days Ago
RS Underperforms Price - Relative Strength Line Not at Highs, While Price Is
Moving Average Begins to Flatten Out - First Day MA Doesn't Close Higher
Price Moves Higher on Lighter Volume - Price Makes a New High on Light Volume and 15 Day Average Volume is Less than 50 Day Average
Price Hits % Target - Price Moves Set % Higher from Entry Price
Price Hits R Multiple - Price hits (Entry - Stop Multiplied by Setting) and Added to Entry
Price Hits Overhead Resistance - Price Crosses a Swing High from a Monthly Timeframe Chart from at Least 1 Year Ago
Price Hits Fib Level - Price Crosses a Fib Extension Drawn From Base High to Low
Price Hits a Psychological Level - Price Crosses a Multiple of 0 or 5
Heavy Volume After Significant Move - Above Average and Heaviest Volume of the Last 5 Days 35 Bars or More from Breakout
Moving Averages Begin to Slope Downward - Moving Averages Fall for 2 Consecutive Days
Blow Off Action - Highest Volume, Largest Spread, Multiple Gaps in a Row 35 Bars or More Post Breakout
Late Buying Frenzy - ANTS 35 Bars or More Post Breakout
Exhaustion Gap - Gap Up 5% or Higher with Price 125% or More Above 200sma
Session LiquidityDescribes if markets are liquid enough for institutions to manipulate. Its often difficult to determine if markets will trend or chop, but by looking at how much volume we have at the open, we can determine of the session will be choppy or trendy, and take trades based on that.
Settings predefined for 1m timeframe on SPY. May work with other tickers, but I have not tested it out yet.
Designed for stocks(as of now, may update later)
Periodic Activity Tracker [LuxAlgo]The Periodic Activity Tracker tool periodically tracks the cumulative buy and sell volume in a user-defined period and draws the corresponding matching bars and volume delta for each period.
Users can select a predefined aggregation period from the following options: Hourly, Daily, Weekly, and Monthly.
🔶 USAGE
This tool provides a simple and clear way of analyzing volumes for each aggregated period and is made up of the following elements:
Buy and sell volumes by period as red and green lines with color gradient area
Delta (difference) between buy & sell volume for each period
Buy & sell volume bars for each period
Separator between lines and bars, and period tags below each pair of bars for ease of reading
On the chart above we can see all the elements displayed, the volume level on the lines perfectly matches the volume level on the bars for each period.
In this case, the tool has the default settings so the anchor period is set to Daily and we can see how the period tag (each day of the week) is displayed below each pair of bars.
Users can disable the delta display and adjust the bar size.
🔹 Reading The Tool
In trading, assessing the strength of the bulls (buyers) and bears (sellers) is key to understanding the current trading environment. Which side, if any, has the upper hand? To answer this question, some traders look at volume in relation to price.
This tool provides you with a view of buy volume versus sell volume, allowing you to compare both sides of the market.
As with any volume tool, the key is to understand when the forces of the two groups are balanced or unbalanced.
As we can observe on the chart:
NOV '23: Buy volume greater than sell volume, both moving up close together, flat delta. We can see that the price is in range.
DEC '23: Buy volume bigger than Sell volume, both moving up but with a bigger difference, bigger delta than last month but still flat. We can see the price in the range above last month's range.
JAN '24: Buy and sell volume tied together, no delta whatsoever. We can see the price in range but testing above and below last month's range.
FEB '24: Buy volume explodes higher and sell volume cannot keep up, big growing delta. Price explodes higher above last month's range.
Traders need to understand that there is always an equal number of buyers and sellers in a liquid market, the quality here is how aggressive or passive they are. Who is 'attacking' and who is 'defending', who is using market orders to move prices, and who is using limit orders waiting to be filled?
This tool gives you the following information:
Lines: if the top line is green, the buyers are attacking, if it is red, the sellers are attacking.
Delta: represents the difference in their strength, if it is above 0 the buyers are stronger, if it is below 0 the sellers are stronger.
Bars: help you to see the difference in strength between buyers and sellers for each period at a glance.
🔹 Anchor Period
By default, the tool is set to Hourly. However, users can select from a number of predefined time periods.
Depending on the user's selection, the bars are displayed as follows:
Hourly : hours of the current day
Daily : days of the current week
Weekly : weeks of the current month
Monthly : months of the current year
On the chart above we can see the four periods displayed, starting at the top left and moving clockwise we have hourly, daily, weekly, and monthly.
🔶 DETAILS
🔹 Chart TimeFrame
The chart timeframe has a direct impact on the visualization of the tool, and the user should select a chart timeframe that is compatible with the Anchor period in the tool's settings panel.
For the chart timeframe to be compatible it must be less than the Anchor period parameter. If the user selects an incompatible chart timeframe, a warning message will be displayed.
As a rule of thumb, the smaller the chart timeframe, the more data the tool will collect, returning indications for longer-term price variations.
These are the recommended chart timeframes for each period:
Hourly : 5m charts or lower
Daily : 1H charts or lower
Weekly : 4H charts or lower
Monthly : 1D charts or lower
🔹 Warnings
This chart shows both types of warnings the user may receive
At the top, we can see the warning that is given when the 'Bar Width' parameter exceeds the allowed value.
At the bottom is the incompatible chart timeframe warning, which prompts the user to select a smaller chart timeframe or a larger "Anchor Period" parameter.
🔶 SETTINGS
🔹 Data Gathering
Anchor period: Time period representing each bar: hours of the day, days of the week, weeks of the month, and months of the year. The timeframe of the chart must be less than this parameter, otherwise a warning will be displayed.
🔹 Style
Bars width: Size of each bar, there is a maximum limit so a warning will be displayed if it is reached.
Volume color
Delta: Enable/Disable Delta Area Display
HTF FVG and Wick Fill trackingImbalances in the charts are some of the clearest and most traded price areas. Two of the best and most used are fair value gaps FVGs and large candle wicks. In both of these price appears to move in such a way that most are left behind having 'missed' the move. But in reality price will often come back to these price points to re-balance and absorb the liquidity that was left behind.
This indicator takes these areas and makes viewing and tracking them clearer than ever. It does this, by first allowing the user to overlay a higher timeframe candle on the current chart. This in itself provides an in depth look at a higher timeframe candle both as it forms and in its final form.
Next the indicator identifies either the FVG or large wicks, on the chosen higher timeframe, all while the chart remains on a lower timeframe. As seen here the fair value gaps are clearly highlighted, taken from a 4 hour timeframe, while the actual chart is on 15 minutes. This allows the user even greater accuracy in identifying their key trading areas.
Utilizing the indicators unique feature, these areas can optionally be extended forward to the current timeframe and 'filled' in realtime. Areas that are filled to the users defined level, will be removed from the chart.
With supplementary settings for how much history to show, how large of a wick should be highlighted and complete control over the colour scheme, users will be able to track and understand the filling of imbalances like never before.
Awakening CHECHLISTThe Awakening Checklist indicator is a tool designed to help traders evaluate certain key market conditions and elements before making trading decisions. It consists of a series of questions that the trader must answer using the options "Yes", "No" or "N/A" (not applicable).
“Has Asia Session ended?” : This question aims to determine if the Asian trading session has ended. The answer to this question can influence trading strategies depending on market conditions.
“Have you identified potential medium induction?” : This question concerns the identification of potential average inductions on the market. Recognizing these inductions can help traders anticipate future price movements.
"Have you identified potential PoI's": This question asks about the identification of potential points of interest on the market. These points of interest can indicate areas of significant support or resistance.
"Have you identified in which direction they are creating lQ?" : This question aims to determine in which direction market participants create liquidity (lQ). Understanding this dynamic can help make informed trade decisions.
“Have they induced Asia Range”: This question concerns the induction of the Asian range by market participants. Recognizing this induction can be important in assessing future price movements.
“Have you had a medium induction”: This question asks about the presence of a medium induction on the market. The answer to this question can influence trading prospects.
“Do you have a BoS away from the induction”: This question aims to find out if the trader has an offer (BoS) far from the identified induction. This can be a risk management strategy.
"Doas your induction PoI have imbalance": This question concerns the imbalance of points of interest (PoI) linked to induction. Recognizing this imbalance can help anticipate price movements.
“Do you have a valid target in mind”: This question aims to find out if the trader has a clear trading objective in mind. Having a goal can help guide trading decisions and manage risk.
Dynamic Trailing (Zeiierman)█ Overview
The Dynamic Trailing (Zeiierman) indicator enhances the traditional SuperTrend approach by providing a more nuanced, adaptable tool for trend analysis and market volatility assessment. It combines techniques to identify dynamic support and resistance levels, trend directions, and market volatility. By integrating the Average True Range (ATR) with a unique multiplier system and smoothing mechanisms, this indicator offers a nuanced approach to trend-following strategies, making it a valuable asset for traders looking to leverage SuperTrend methodologies with additional insights into market dynamics.
█ How It Works
At its core, this indicator builds on the traditional SuperTrend formula by utilizing a modified ATR calculation to define the deviation for dynamic support and resistance levels. These levels are dynamically adjusted based on market volatility. The innovation lies in the addition of the Hull Moving Average (HMA) and the Triple Exponential Moving Average (TEMA) for an enhanced smoothing effect, making the indicator's trend signals more reliable and less prone to market noise. The trend direction is determined by comparing the closing price with the dynamic levels, facilitating clear bullish or bearish signals.
The indicator incorporates a 'Supertrend' function, which uses the dynamic levels and the price’s position relative to them to determine the trend direction. This determination is visualized through color-coded lines and a cloud zone, which expands or contracts based on the ATR and a user-defined width setting, illustrating the market's volatility and trend strength.
ATR Calculation: Utilizes the Average True Range (ATR) to measure market volatility. The ATR is a cornerstone of this indicator, helping to dynamically adjust the support and resistance levels according to the market’s changing conditions.
Supertrend Calculation: Implements a supertrend formula that combines the ATR with user-defined multipliers to plot potential trend directions. This feature helps in identifying whether the market is in an uptrend or downtrend, offering visual cues for potential reversals.
TEMA Calculation: Employs the Triple Exponential Moving Average (TEMA) through a Hull Moving Average (HMA) calculation to smooth out price data. This smoothing process helps in reducing market noise and makes the trend direction clearer.
Dynamic Support and Resistance: Calculates dynamic support and resistance levels by applying a deviation (derived from the ATR and user-defined multiplier) to the smoothed price data. These levels adapt to market conditions, providing areas where price might experience support or resistance.
Trend and Cloud Calculation: Determines the overall trend direction and plots a 'Cloud' zone around it, which adjusts in width based on the ATR and a user-defined cloud width setting. This cloud acts as a visual buffer, indicating the strength and stability of the current trend.
█ How to Use
Trend Identification: The primary function of this indicator is to help traders quickly identify the prevailing market trend. A change in the color of the dynamic trailing line or its position relative to the price can signal potential trend reversals.
Dynamic Support and Resistance: Unlike static levels, the dynamic levels adjust with market conditions, providing current areas where the price might experience support or resistance.
Dynamic Support
Dynamic Resistance
█ Settings
Mult (Multiplier): Adjusts the multiplier for the ATR calculation, affecting the deviation distance for support and resistance levels. Higher values decrease sensitivity and vice versa.
Len (Length): Sets the period for the HMA in the TEMA calculation, influencing the indicator's responsiveness to price changes.
Smoothness: Determines the smoothness of the dynamic support and resistance lines by setting the SMA length. Higher values result in smoother lines.
Cloud Width : Modifies the width of the cloud, providing a visual representation of market volatility.
Color Settings (upcol and dncol): Allows users to customize the colors of the indicator's lines and cloud, aiding in visual trend identification.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
NASDAQ 100 Peak Hours StrategyNASDAQ 100 Peak Hours Trading Strategy
Description
Our NASDAQ 100 Peak Hours Trading Strategy leverages a carefully designed algorithm to trade within specific hours of high market activity, particularly focusing on the first two hours of the trading session from 09:30 AM to 11:30 AM GMT-5. This period is identified for its increased volatility and liquidity, offering numerous trading opportunities.
The strategy incorporates a blend of technical indicators to identify entry and exit points for both long and short positions. These indicators include:
Exponential Moving Averages (EMAs) : A short-term 9-period EMA and a longer-term 21-period EMA to determine the market trend and momentum.
Relative Strength Index (RSI) : A 14-period RSI to gauge the market's momentum.
Average True Range (ATR) : A 14-period ATR to assess market volatility and to set dynamic stop losses and trailing stops.
Volume Weighted Average Price (VWAP) : To identify the market's average price weighted by volume, serving as a benchmark for the trading day.
Our strategy uniquely applies a volatility filter using the ATR, ensuring trades are only executed in conditions that favor our setup. Additionally, we consider the direction of the EMAs to confirm the market's trend before entering trades.
Originality and Usefulness
This strategy stands out by combining these indicators within the NASDAQ 100's peak hours, exploiting the specific market conditions that prevail during these times. The inclusion of a volatility filter and dynamic stop-loss mechanisms based on the ATR provides a robust method for managing risk.
By focusing on the early trading hours, the strategy aims to capture the initial market movements driven by overnight news and the opening rush, often characterized by higher volatility. This approach is particularly useful for traders looking to maximize gains from short-term fluctuations while limiting exposure to longer-term market uncertainty.
Strategy Results
To ensure the strategy's effectiveness and reliability, it has undergone rigorous backtesting over a significant dataset to produce a sample size of more than 100 trades. This testing phase helps in identifying the strategy's potential in various market conditions, its consistency, and its risk-to-reward ratio.
Our backtesting adheres to realistic trading conditions, accounting for slippage and commission to reflect actual trading scenarios accurately. The strategy is designed with a conservative approach to risk management, advising not to risk more than 5-10% of equity on a single trade. The default settings in the script align with these principles, ensuring that users can replicate our tested conditions.
Using the Strategy
The strategy is designed for simplicity and ease of use:
Trade Hours : Focuses on 09:30 AM to 11:30 AM GMT-5, during the NASDAQ 100's peak activity hours.
Entry Conditions : Trades are initiated based on the alignment of EMAs, RSI, VWAP, and the ATR's volatility filter within the designated time frame.
Exit Conditions : Includes dynamic trailing stops based on ATR, a predefined time exit strategy, and a trend reversal exit condition for risk management.
This script is a powerful tool for traders looking to leverage the NASDAQ 100's peak hours, providing a structured approach to navigating the early market hours with a robust set of criteria for making informed trading decisions.
Order Blocks Indicator [TradingFinder] Lightning|CHOCH |OB | BOS🔵 Introduction
In "Price Action," an "Order Block" is essentially an area on the price chart where significant players such as institutional traders have executed their moves by placing noteworthy orders. These points often indicate areas where price either attempts to break through (resistance) or returns when it reaches there (support).
Therefore, when discussing the identification of order blocks, we typically refer to finding points where the price has stalled for a while and has accumulated strength before making a significant move in one direction.
Essentially, order blocks assist traders in understanding where large players with "smart money" have likely placed their bulk orders in the market. Traders use these order blocks as part of their overall analysis to identify probable levels where price may change direction.
This version of the order block indicator is designed for traders, adding many indicators to their charts. The minimal design helps minimize disruptions to user focus.
🔵 Identification of Order Blocks
To identify order blocks, first, a "Level Break" must occur. To identify a "Demand Zone," a "High Level Break" is required, and to identify a "Supply Zone," a "Low Level Break" is needed.
Demand Zone :
Supply Zone :
🔵 "Change of Character" or "Market Shift Structure"
"ChoCh" or "MSS" is the "Break Level" that is contrary to the previous trend. For example, if a "Bearish Level" is established in the market and consecutive "Low Levels" are being broken, the price turns upward, breaking a "High Level." This break is called "ChoCh" or "MSS."
🔵 "Break of Structure"
"Break of Structure," or "BoS" for short, is the "Break Level" in the direction of the current trend. For example, if a "Bullish Level" is established in the market, when the price breaks a "High Level," a "BoS" has occurred.
🔵 Features
🟣 Major Level
This feature helps you easily identify major levels. These levels form when the price breaks another major level.
🟣 Refine Order Block
The "Refinement" feature allows you to adjust the width of the order block based on your strategy. There are two modes, "Aggressive" and "Defensive," in Order Block Refine. The difference between "Aggressive" and "Defensive" lies in the width of the order block. For "Risk Averse" traders, the "Defensive" mode is suitable because it provides smaller stop losses and larger reward-to-risk ratios. For "Risk Taker" traders, the "Aggressive" mode is more suitable. These traders prefer to enter trades at higher prices and this mode, where the width of the order block is greater, is more suitable for this group of individuals.
🔵 How to Use
After adding the indicator to your chart, you will see a visual similar to the image below. Green order blocks are "Demand Zones" and red order blocks are "Supply Zones." The midpoint of the order blocks also indicates 50% of it.
Refine Order Block is defaulted to On and refines the order blocks. If you want the order blocks to remain original, you should set it to Off.
Refine is defaulted to "Defensive" mode. If you want it to be in "Aggressive" mode, you should change its mode through Refine Type.
Displaying "Major Levels" is turned off by default and to display them, you should set "Show High Level" and "Show Low Level" to "Yes." You can use these lines to identify liquidity or determine stop loss and take profit levels.
Bitcoin Momentum StrategyThis is a very simple long-only strategy I've used since December 2022 to manage my Bitcoin position.
I'm sharing it as an open-source script for other traders to learn from the code and adapt it to their liking if they find the system concept interesting.
General Overview
Always do your own research and backtesting - this script is not intended to be traded blindly (no script should be) and I've done limited testing on other markets beyond Ethereum and BTC, it's just a template to tweak and play with and make into one's own.
The results shown in the strategy tester are from Bitcoin's inception so as to get a large sample size of trades, and potential returns have diminished significantly as BTC has grown to become a mega cap asset, but the script includes a date filter for backtesting and it has still performed solidly in recent years (speaking from personal experience using it myself - DYOR with the date filter).
The main advantage of this system in my opinion is in limiting the max drawdown significantly versus buy & hodl. Theoretically much better returns can be made by just holding, but that's also a good way to lose 70%+ of your capital in the inevitable bear markets (also speaking from experience).
In saying all of that, the future is fundamentally unknowable and past results in no way guarantee future performance.
System Concept:
Capture as much Bitcoin upside volatility as possible while side-stepping downside volatility as quickly as possible.
The system uses a simple but clever momentum-style trailing stop technique I learned from one of my trading mentors who uses this approach on momentum/trend-following stock market systems.
Basically, the system "ratchets" up the stop-loss to be much tighter during high bearish volatility to protect open profits from downside moves, but loosens the stop loss during sustained bullish momentum to let the position ride.
It is invested most of the time, unless BTC is trading below its 20-week EMA in which case it stays in cash/USDT to avoid holding through bear markets. It only trades one position (no pyramiding) and does not trade short, but can easily be tweaked to do whatever you like if you know what you're doing in Pine.
Default parameters:
HTF: Weekly Chart
EMA: 20-Period
ATR: 5-period
Bar Lookback: 7
Entry Rule #1:
Bitcoin's current price must be trading above its higher-timeframe EMA (Weekly 20 EMA).
Entry Rule #2:
Bitcoin must not be in 'caution' condition (no large bearish volatility swings recently).
Enter at next bar's open if conditions are met and we are not already involved in a trade.
"Caution" Condition:
Defined as true if BTC's recent 7-bar swing high minus current bar's low is > 1.5x ATR, or Daily close < Daily 20-EMA.
Trailing Stop:
Stop is trailed 1 ATR from recent swing high, or 20% of ATR if in caution condition (ie. 0.2 ATR).
Exit on next bar open upon a close below stop loss.
I typically use a limit order to open & exit trades as close to the open price as possible to reduce slippage, but the strategy script uses market orders.
I've never had any issues getting filled on limit orders close to the market price with BTC on the Daily timeframe, but if the exchange has relatively low slippage I've found market orders work fine too without much impact on the results particularly since BTC has consistently remained above $20k and highly liquid.
Cost of Trading:
The script uses no leverage and a default total round-trip commission of 0.3% which is what I pay on my exchange based on their tier structure, but this can vary widely from exchange to exchange and higher commission fees will have a significantly negative impact on realized gains so make sure to always input the correct theoretical commission cost when backtesting any script.
Static slippage is difficult to estimate in the strategy tester given the wide range of prices & liquidity BTC has experienced over the years and it largely depends on position size, I set it to 150 points per buy or sell as BTC is currently very liquid on the exchange I trade and I use limit orders where possible to enter/exit positions as close as possible to the market's open price as it significantly limits my slippage.
But again, this can vary a lot from exchange to exchange (for better or worse) and if BTC volatility is high at the time of execution this can have a negative impact on slippage and therefore real performance, so make sure to adjust it according to your exchange's tendencies.
Tax considerations should also be made based on short-term trade frequency if crypto profits are treated as a CGT event in your region.
Summary:
A simple, but effective and fairly robust system that achieves the goals I set for it.
From my preliminary testing it appears it may also work on altcoins but it might need a bit of tweaking/loosening with the trailing stop distance as the default parameters are designed to work with Bitcoin which obviously behaves very differently to smaller cap assets.
Good luck out there!
Adaptive Moving Average (AMA) Signals (Zeiierman)█ Overview
The Adaptive Moving Average (AMA) Signals indicator, enhances the classic concept of moving averages by making them adaptive to the market's volatility. This adaptability makes the AMA particularly useful in identifying market trends with varying degrees of volatility.
The core of the AMA's adaptability lies in its Efficiency Ratio (ER), which measures the directionality of the market over a given period. The ER is calculated by dividing the absolute change in price over a period by the sum of the absolute differences in daily prices over the same period.
⚪ Why It's Useful
The AMA Signals indicator is particularly useful because of its adaptability to changing market conditions. Unlike static moving averages, it dynamically adjusts, providing more relevant signals that can help traders capture trends earlier or identify reversals with greater accuracy. Its configurability makes it suitable for various trading strategies and timeframes, from day trading to swing trading.
█ How It Works
The AMA Signals indicator operates on the principle of adapting to market efficiency through the calculation of the Efficiency Ratio (ER), which measures the directionality of the market over a specified period. By comparing the net price change to total price movements, the AMA adjusts its sensitivity, becoming faster during trending markets and slower during sideways markets. This adaptability is enhanced by a gamma parameter that filters signals for either trend continuation or reversal, making it versatile across different market conditions.
change = math.abs(close - close )
volatility = math.sum(math.abs(close - close ), n)
ER = change / volatility
Efficiency Ratio (ER) Calculation: The AMA begins with the computation of the Efficiency Ratio (ER), which measures the market's directionality over a specified period. The ER is a ratio of the net price change to the total price movements, serving as a measure of the efficiency of price movements.
Adaptive Smoothing: Based on the ER, the indicator calculates the smoothing constants for the fastest and slowest Exponential Moving Averages (EMAs). These constants are then used to compute a Scaled Smoothing Coefficient (SC) that adapts the moving average to the market's efficiency, making it faster during trending periods and slower in sideways markets.
Signal Generation: The AMA applies a filter, adjusted by a "gamma" parameter, to identify trading signals. This gamma influences the sensitivity towards trend or reversal signals, with options to adjust for focusing on either trend-following or counter-trend signals.
█ How to Use
Trend Identification: Use the AMA to identify the direction of the trend. An upward moving AMA indicates a bullish trend, while a downward moving AMA suggests a bearish trend.
Trend Trading: Look for buy signals when the AMA is trending upwards and sell signals during a downward trend. Adjust the fast and slow EMA lengths to match the desired sensitivity and timeframe.
Reversal Trading: Set the gamma to a positive value to focus on reversal signals, identifying potential market turnarounds.
█ Settings
Period for ER calculation: Defines the lookback period for calculating the Efficiency Ratio, affecting how quickly the AMA responds to changes in market efficiency.
Fast EMA Length and Slow EMA Length: Determine the responsiveness of the AMA to recent price changes, allowing traders to fine-tune the indicator to their trading style.
Signal Gamma: Adjusts the sensitivity of the filter applied to the AMA, with the ability to focus on trend signals or reversal signals based on its value.
AMA Candles: An innovative feature that plots candles based on the AMA calculation, providing visual cues about the market trend and potential reversals.
█ Alerts
The AMA Signals indicator includes configurable alerts for buy and sell signals, as well as positive and negative trend changes.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
BAERMThe Bitcoin Auto-correlation Exchange Rate Model: A Novel Two Step Approach
THIS IS NOT FINANCIAL ADVICE. THIS ARTICLE IS FOR EDUCATIONAL AND ENTERTAINMENT PURPOSES ONLY.
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Prelude
It has been previously established that the Bitcoin daily USD exchange rate series is extremely auto-correlated
In this article, we will utilise this fact to build a model for Bitcoin/USD exchange rate. But not a model for predicting the exchange rate, but rather a model to understand the fundamental reasons for the Bitcoin to have this exchange rate to begin with.
This is a model of sound money, scarcity and subjective value.
Introduction
Bitcoin, a decentralised peer to peer digital value exchange network, has experienced significant exchange rate fluctuations since its inception in 2009. In this article, we explore a two-step model that reasonably accurately captures both the fundamental drivers of Bitcoin’s value and the cyclical patterns of bull and bear markets. This model, whilst it can produce forecasts, is meant more of a way of understanding past exchange rate changes and understanding the fundamental values driving the ever increasing exchange rate. The forecasts from the model are to be considered inconclusive and speculative only.
Data preparation
To develop the BAERM, we used historical Bitcoin data from Coin Metrics, a leading provider of Bitcoin market data. The dataset includes daily USD exchange rates, block counts, and other relevant information. We pre-processed the data by performing the following steps:
Fixing date formats and setting the dataset’s time index
Generating cumulative sums for blocks and halving periods
Calculating daily rewards and total supply
Computing the log-transformed price
Step 1: Building the Base Model
To build the base model, we analysed data from the first two epochs (time periods between Bitcoin mining reward halvings) and regressed the logarithm of Bitcoin’s exchange rate on the mining reward and epoch. This base model captures the fundamental relationship between Bitcoin’s exchange rate, mining reward, and halving epoch.
where Yt represents the exchange rate at day t, Epochk is the kth epoch (for that t), and epsilont is the error term. The coefficients beta0, beta1, and beta2 are estimated using ordinary least squares regression.
Base Model Regression
We use ordinary least squares regression to estimate the coefficients for the betas in figure 2. In order to reduce the possibility of over-fitting and ensure there is sufficient out of sample for testing accuracy, the base model is only trained on the first two epochs. You will notice in the code we calculate the beta2 variable prior and call it “phaseplus”.
The code below shows the regression for the base model coefficients:
\# Run the regression
mask = df\ < 2 # we only want to use Epoch's 0 and 1 to estimate the coefficients for the base model
reg\_X = df.loc\ [mask, \ \].shift(1).iloc\
reg\_y = df.loc\ .iloc\
reg\_X = sm.add\_constant(reg\_X)
ols = sm.OLS(reg\_y, reg\_X).fit()
coefs = ols.params.values
print(coefs)
The result of this regression gives us the coefficients for the betas of the base model:
\
or in more human readable form: 0.029, 0.996869586, -0.00043. NB that for the auto-correlation/momentum beta, we did NOT round the significant figures at all. Since the momentum is so important in this model, we must use all available significant figures.
Fundamental Insights from the Base Model
Momentum effect: The term 0.997 Y suggests that the exchange rate of Bitcoin on a given day (Yi) is heavily influenced by the exchange rate on the previous day. This indicates a momentum effect, where the price of Bitcoin tends to follow its recent trend.
Momentum effect is a phenomenon observed in various financial markets, including stocks and other commodities. It implies that an asset’s price is more likely to continue moving in its current direction, either upwards or downwards, over the short term.
The momentum effect can be driven by several factors:
Behavioural biases: Investors may exhibit herding behaviour or be subject to cognitive biases such as confirmation bias, which could lead them to buy or sell assets based on recent trends, reinforcing the momentum.
Positive feedback loops: As more investors notice a trend and act on it, the trend may gain even more traction, leading to a self-reinforcing positive feedback loop. This can cause prices to continue moving in the same direction, further amplifying the momentum effect.
Technical analysis: Many traders use technical analysis to make investment decisions, which often involves studying historical exchange rate trends and chart patterns to predict future exchange rate movements. When a large number of traders follow similar strategies, their collective actions can create and reinforce exchange rate momentum.
Impact of halving events: In the Bitcoin network, new bitcoins are created as a reward to miners for validating transactions and adding new blocks to the blockchain. This reward is called the block reward, and it is halved approximately every four years, or every 210,000 blocks. This event is known as a halving.
The primary purpose of halving events is to control the supply of new bitcoins entering the market, ultimately leading to a capped supply of 21 million bitcoins. As the block reward decreases, the rate at which new bitcoins are created slows down, and this can have significant implications for the price of Bitcoin.
The term -0.0004*(50/(2^epochk) — (epochk+1)²) accounts for the impact of the halving events on the Bitcoin exchange rate. The model seems to suggest that the exchange rate of Bitcoin is influenced by a function of the number of halving events that have occurred.
Exponential decay and the decreasing impact of the halvings: The first part of this term, 50/(2^epochk), indicates that the impact of each subsequent halving event decays exponentially, implying that the influence of halving events on the Bitcoin exchange rate diminishes over time. This might be due to the decreasing marginal effect of each halving event on the overall Bitcoin supply as the block reward gets smaller and smaller.
This is antithetical to the wrong and popular stock to flow model, which suggests the opposite. Given the accuracy of the BAERM, this is yet another reason to question the S2F model, from a fundamental perspective.
The second part of the term, (epochk+1)², introduces a non-linear relationship between the halving events and the exchange rate. This non-linear aspect could reflect that the impact of halving events is not constant over time and may be influenced by various factors such as market dynamics, speculation, and changing market conditions.
The combination of these two terms is expressed by the graph of the model line (see figure 3), where it can be seen the step from each halving is decaying, and the step up from each halving event is given by a parabolic curve.
NB - The base model has been trained on the first two halving epochs and then seeded (i.e. the first lag point) with the oldest data available.
Constant term: The constant term 0.03 in the equation represents an inherent baseline level of growth in the Bitcoin exchange rate.
In any linear or linear-like model, the constant term, also known as the intercept or bias, represents the value of the dependent variable (in this case, the log-scaled Bitcoin USD exchange rate) when all the independent variables are set to zero.
The constant term indicates that even without considering the effects of the previous day’s exchange rate or halving events, there is a baseline growth in the exchange rate of Bitcoin. This baseline growth could be due to factors such as the network’s overall growth or increasing adoption, or changes in the market structure (more exchanges, changes to the regulatory environment, improved liquidity, more fiat on-ramps etc).
Base Model Regression Diagnostics
Below is a summary of the model generated by the OLS function
OLS Regression Results
\==============================================================================
Dep. Variable: logprice R-squared: 0.999
Model: OLS Adj. R-squared: 0.999
Method: Least Squares F-statistic: 2.041e+06
Date: Fri, 28 Apr 2023 Prob (F-statistic): 0.00
Time: 11:06:58 Log-Likelihood: 3001.6
No. Observations: 2182 AIC: -5997.
Df Residuals: 2179 BIC: -5980.
Df Model: 2
Covariance Type: nonrobust
\==============================================================================
coef std err t P>|t| \
\------------------------------------------------------------------------------
const 0.0292 0.009 3.081 0.002 0.011 0.048
logprice 0.9969 0.001 1012.724 0.000 0.995 0.999
phaseplus -0.0004 0.000 -2.239 0.025 -0.001 -5.3e-05
\==============================================================================
Omnibus: 674.771 Durbin-Watson: 1.901
Prob(Omnibus): 0.000 Jarque-Bera (JB): 24937.353
Skew: -0.765 Prob(JB): 0.00
Kurtosis: 19.491 Cond. No. 255.
\==============================================================================
Below we see some regression diagnostics along with the regression itself.
Diagnostics: We can see that the residuals are looking a little skewed and there is some heteroskedasticity within the residuals. The coefficient of determination, or r2 is very high, but that is to be expected given the momentum term. A better r2 is manually calculated by the sum square of the difference of the model to the untrained data. This can be achieved by the following code:
\# Calculate the out-of-sample R-squared
oos\_mask = df\ >= 2
oos\_actual = df.loc\
oos\_predicted = df.loc\
residuals\_oos = oos\_actual - oos\_predicted
SSR = np.sum(residuals\_oos \*\* 2)
SST = np.sum((oos\_actual - oos\_actual.mean()) \*\* 2)
R2\_oos = 1 - SSR/SST
print("Out-of-sample R-squared:", R2\_oos)
The result is: 0.84, which indicates a very close fit to the out of sample data for the base model, which goes some way to proving our fundamental assumption around subjective value and sound money to be accurate.
Step 2: Adding the Damping Function
Next, we incorporated a damping function to capture the cyclical nature of bull and bear markets. The optimal parameters for the damping function were determined by regressing on the residuals from the base model. The damping function enhances the model’s ability to identify and predict bull and bear cycles in the Bitcoin market. The addition of the damping function to the base model is expressed as the full model equation.
This brings me to the question — why? Why add the damping function to the base model, which is arguably already performing extremely well out of sample and providing valuable insights into the exchange rate movements of Bitcoin.
Fundamental reasoning behind the addition of a damping function:
Subjective Theory of Value: The cyclical component of the damping function, represented by the cosine function, can be thought of as capturing the periodic fluctuations in market sentiment. These fluctuations may arise from various factors, such as changes in investor risk appetite, macroeconomic conditions, or technological advancements. Mathematically, the cyclical component represents the frequency of these fluctuations, while the phase shift (α and β) allows for adjustments in the alignment of these cycles with historical data. This flexibility enables the damping function to account for the heterogeneity in market participants’ preferences and expectations, which is a key aspect of the subjective theory of value.
Time Preference and Market Cycles: The exponential decay component of the damping function, represented by the term e^(-0.0004t), can be linked to the concept of time preference and its impact on market dynamics. In financial markets, the discounting of future cash flows is a common practice, reflecting the time value of money and the inherent uncertainty of future events. The exponential decay in the damping function serves a similar purpose, diminishing the influence of past market cycles as time progresses. This decay term introduces a time-dependent weight to the cyclical component, capturing the dynamic nature of the Bitcoin market and the changing relevance of past events.
Interactions between Cyclical and Exponential Decay Components: The interplay between the cyclical and exponential decay components in the damping function captures the complex dynamics of the Bitcoin market. The damping function effectively models the attenuation of past cycles while also accounting for their periodic nature. This allows the model to adapt to changing market conditions and to provide accurate predictions even in the face of significant volatility or structural shifts.
Now we have the fundamental reasoning for the addition of the function, we can explore the actual implementation and look to other analogies for guidance —
Financial and physical analogies to the damping function:
Mathematical Aspects: The exponential decay component, e^(-0.0004t), attenuates the amplitude of the cyclical component over time. This attenuation factor is crucial in modelling the diminishing influence of past market cycles. The cyclical component, represented by the cosine function, accounts for the periodic nature of market cycles, with α determining the frequency of these cycles and β representing the phase shift. The constant term (+3) ensures that the function remains positive, which is important for practical applications, as the damping function is added to the rest of the model to obtain the final predictions.
Analogies to Existing Damping Functions: The damping function in the BAERM is similar to damped harmonic oscillators found in physics. In a damped harmonic oscillator, an object in motion experiences a restoring force proportional to its displacement from equilibrium and a damping force proportional to its velocity. The equation of motion for a damped harmonic oscillator is:
x’’(t) + 2γx’(t) + ω₀²x(t) = 0
where x(t) is the displacement, ω₀ is the natural frequency, and γ is the damping coefficient. The damping function in the BAERM shares similarities with the solution to this equation, which is typically a product of an exponential decay term and a sinusoidal term. The exponential decay term in the BAERM captures the attenuation of past market cycles, while the cosine term represents the periodic nature of these cycles.
Comparisons with Financial Models: In finance, damped oscillatory models have been applied to model interest rates, stock prices, and exchange rates. The famous Black-Scholes option pricing model, for instance, assumes that stock prices follow a geometric Brownian motion, which can exhibit oscillatory behavior under certain conditions. In fixed income markets, the Cox-Ingersoll-Ross (CIR) model for interest rates also incorporates mean reversion and stochastic volatility, leading to damped oscillatory dynamics.
By drawing on these analogies, we can better understand the technical aspects of the damping function in the BAERM and appreciate its effectiveness in modelling the complex dynamics of the Bitcoin market. The damping function captures both the periodic nature of market cycles and the attenuation of past events’ influence.
Conclusion
In this article, we explored the Bitcoin Auto-correlation Exchange Rate Model (BAERM), a novel 2-step linear regression model for understanding the Bitcoin USD exchange rate. We discussed the model’s components, their interpretations, and the fundamental insights they provide about Bitcoin exchange rate dynamics.
The BAERM’s ability to capture the fundamental properties of Bitcoin is particularly interesting. The framework underlying the model emphasises the importance of individuals’ subjective valuations and preferences in determining prices. The momentum term, which accounts for auto-correlation, is a testament to this idea, as it shows that historical price trends influence market participants’ expectations and valuations. This observation is consistent with the notion that the price of Bitcoin is determined by individuals’ preferences based on past information.
Furthermore, the BAERM incorporates the impact of Bitcoin’s supply dynamics on its price through the halving epoch terms. By acknowledging the significance of supply-side factors, the model reflects the principles of sound money. A limited supply of money, such as that of Bitcoin, maintains its value and purchasing power over time. The halving events, which reduce the block reward, play a crucial role in making Bitcoin increasingly scarce, thus reinforcing its attractiveness as a store of value and a medium of exchange.
The constant term in the model serves as the baseline for the model’s predictions and can be interpreted as an inherent value attributed to Bitcoin. This value emphasizes the significance of the underlying technology, network effects, and Bitcoin’s role as a medium of exchange, store of value, and unit of account. These aspects are all essential for a sound form of money, and the model’s ability to account for them further showcases its strength in capturing the fundamental properties of Bitcoin.
The BAERM offers a potential robust and well-founded methodology for understanding the Bitcoin USD exchange rate, taking into account the key factors that drive it from both supply and demand perspectives.
In conclusion, the Bitcoin Auto-correlation Exchange Rate Model provides a comprehensive fundamentally grounded and hopefully useful framework for understanding the Bitcoin USD exchange rate.
Gann Box (Zeiierman)█ Overview
The Gann Box (Zeiierman) is an indicator that provides visual insights using the principles of W.D. Gann's trading methods. Gann's techniques are based on geometry, astronomy, and astrology, and are used to predict important price levels and market trends. This indicator helps traders identify potential support and resistance levels, and forecast future price movements.
Gann used angles and various geometric constructions to divide time and price into proportionate parts. Gann indicators are often used to predict areas of support and resistance, key tops and bottoms, and future price moves.
█ How It Works
The indicator operates by identifying high and low points within a visible range on the chart and drawing a Gann Box between these points. The box is divided into segments based on selected percentages, which represent key levels for observing market reactions. It includes options to display labels, a Gann fan, and Gann angles for analysis. Advanced features allow extending the box into the future for predictive analysis and reversing its orientation for alternative viewpoints.
High and Low Points Identification: It starts by locating the highest and lowest price points visible on the chart.
Gann Box Construction: Draws a box from these points and divides it according to specified percentages, highlighting potential support and resistance levels.
█ How to Use
Support and Resistance Levels
Using a Gann angle to forecast support and resistance is probably the most popular way they are used. This technique frames the market, allowing the analyst to read the movement of the market inside this framework.
The lines within the Gann Box, drawn at the key percentages, create a grid of potential support and resistance levels. As prices fluctuate, these lines can act as barriers to price movement, with the price often pausing or reversing at these intervals.
Forecasting with the 'Extend' Feature: The indicator's ability to extend lines and boxes into the future provides traders with a forward-looking tool to anticipate potential market movements and prepare for them.
Gann Fan: This feature draws lines at a significant price angle, helping traders identify potential support and resistance levels based on the theory that prices move in predictable patterns.
Gann Curves: Gann Curves display dynamic support and resistance levels, aiding in the analysis of momentum and trend strength.
█ Settings
The indicator includes several settings that allow customization of its appearance and functionality:
⚪ General Settings
Reverse: This setting changes the orientation of labels and calculations within the Gann Box, providing alternative analytical perspectives. It essentially flips the Gann Box's direction, which can be useful in different market conditions or analysis scenarios.
Extend: Extends the drawing of Gann lines or boxes into the future beyond the current last bar. This feature is essential for forecasting future price movements and identifying potential support or resistance levels that lie outside the current price action.
⚪ Gann Box
Show Box: Toggles the visibility of the Gann Box on the chart. The Gann Box is a fundamental tool in Gann analysis, highlighting key levels based on selected high and low points to identify potential support and resistance areas.
Show Fibonacci Labels: Controls the display of Fibonacci labels within the Gann Box. These labels mark specific Fibonacci retracement levels, aiding traders in recognizing significant levels for potential reversals.
Box Visibility: Allows users to enable or disable individual boxes within the Gann Box, providing flexibility in focusing on specific levels of interest.
Percentage Levels: Defines the Fibonacci levels within the Gann Box. Traders can adjust these levels to customize the Gann Box according to their specific analysis needs.
Coloring: Customizes the color of each level within the Gann Box, enhancing visual clarity and differentiation between levels.
⚪ Gann Fan
Show Fan: Enables the Gann Fan, which draws lines at significant Gann angles from a particular point on the chart, helping identify potential support and resistance levels.
Fan Percentages and Coloring: Similar to the Gann Box, these settings allow traders to customize which Gann angles are displayed and how they are colored.
⚪ Gann Curves
Show Curves: When enabled, this setting draws Gann Curves on the chart. These curves are based on Gann percentages and provide a dynamic view of support and resistance levels as they adapt to changing market conditions.
Curve Percentages and Coloring: Define which curves are displayed and their colors, allowing for a tailored analysis experience.
⚪ Gann Angles
Show Angles: Toggles the display of Gann Angles, which are crucial for understanding the market's price and time dynamics, offering insights into future support and resistance levels.
Coloring: Customizes the color of the Gann Angles, making it easier to differentiate between various angles on the chart.
█ Alerts
The indicator includes several alert conditions for price breakouts from the Gann Box and specific levels, enabling traders to be notified of significant market movements.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Optimal Buy Day (Zeiierman)█ Overview
The Optimal Buy Day (Zeiierman) indicator identifies optimal buying days based on historical price data, starting from a user-defined year. It simulates investing a fixed initial capital and making regular monthly contributions. The unique aspect of this indicator involves comparing systematic investment on specific days of the month against a randomized buying day each month, aiming to analyze which method might yield more shares or a better average price over time. By visualizing the potential outcomes of systematic versus randomized buying, traders can better understand the impact of market timing and how regular investments might accumulate over time.
These statistics are pivotal for traders and investors using the script to analyze historical performance and strategize future investments. By understanding which days offered more shares for their money or lower average prices, investors can tailor their buying strategies to potentially enhance returns.
█ Key Statistics
⚪ Shares
Definition: Represents the total number of shares acquired on a particular day of the month across the entire simulation period.
How It Works: The script calculates how many shares can be bought each day, given the available capital or monthly contribution. This calculation takes into account the day's opening price and accumulates the total shares bought on that day over the simulation period.
Interpretation: A higher number of shares indicates that the day consistently offered better buying opportunities, allowing the investor to acquire more shares for the same amount of money. This metric is crucial for understanding which days historically provided more value.
⚪ AVG Price
Definition: The average price paid per share on a particular day of the month, averaged over the simulation period.
How It Works: Each time shares are bought, the script calculates the average price per share, factoring in the new shares purchased at the current price. This average evolves over time as more shares are bought at varying prices.
Interpretation: The average price gives insight into the cost efficiency of buying shares on specific days. A lower average price suggests that buying on that day has historically led to better pricing, making it a potentially more attractive investment strategy.
⚪ Buys
Definition: The total number of transactions or buys executed on a particular day of the month throughout the simulation.
How It Works: This metric increments each time shares are bought on a specific day, providing a count of all buying actions taken.
Interpretation: The number of buys indicates the frequency of investment opportunities. A higher count could mean more consistent opportunities for investment, but it's important to consider this in conjunction with the average price and the total shares acquired to assess overall strategy effectiveness.
⚪ Most Shares
Definition: Identifies the day of the month on which the highest number of shares were bought, highlighting the specific day and the total shares acquired.
How It Works: After simulating purchases across all days of the month, the script identifies which day resulted in the highest total number of shares bought.
Interpretation: This metric points out the most opportune day for volume buying. It suggests that historically, this day provided conditions that allowed for maximizing the quantity of shares purchased, potentially due to lower prices or other factors.
⚪ Best Price
Definition: Highlights the day of the month that offered the lowest average price per share, indicating both the day and the price.
How It Works: The script calculates the average price per share for each day and identifies the day with the lowest average.
Interpretation: This metric is key for investors looking to minimize costs. The best price day suggests that historically, buying on this day led to acquiring shares at a more favorable average price, potentially maximizing long-term investment returns.
⚪ Randomized Shares
Definition: This metric represents the total number of shares acquired on a randomly selected day of the month, simulated across the entire period.
How It Works: At the beginning of each month within the simulation, the script selects a random day when the market is open and calculates how many shares can be purchased with the available capital or monthly contribution at that day's opening price. This process is repeated each month, and the total number of shares acquired through these random purchases is tallied.
Interpretation: Randomized shares offer a comparison point to systematic buying strategies. By comparing the total shares acquired through random selection against those bought on the best or worst days, investors can gauge the impact of timing and market fluctuations on their investment strategy. A higher total in randomized shares might indicate that over the long term, the specific days chosen for investment might matter less than consistent market participation. Conversely, if systematic strategies yield significantly more shares, it suggests that timing could indeed play a crucial role in maximizing investment returns.
⚪ Randomized Price
Definition: The average price paid per share for the shares acquired on the randomly selected days throughout the simulation period.
How It Works: Each time shares are bought on a randomly chosen day, the script calculates the average price paid for all shares bought through this randomized strategy. This average price is updated as the simulation progresses, reflecting the cost efficiency of random buying decisions.
Interpretation: The randomized price metric helps investors understand the cost implications of a non-systematic, random investment approach. Comparing this average price to those achieved through more deliberate, systematic strategies can reveal whether consistent investment timing strategies outperform random investment actions in terms of cost efficiency. A lower randomized price suggests that random buying might not necessarily result in higher costs, while a higher average price indicates that systematic strategies might provide better control over investment costs.
█ How to Use
Traders can use this tool to analyze historical data and simulate different investment strategies. By inputting their initial capital, regular contribution amount, and start year, they can visually assess which days might have been more advantageous for buying, based on historical price actions. This can inform future investment decisions, especially for those employing dollar-cost averaging strategies or looking to optimize entry points.
█ Settings
StartYear: This setting allows the user to specify the starting year for the investment simulation. Changing this value will either extend or shorten the period over which the simulation is run. If a user increases the value, the simulation begins later and covers a shorter historical period; decreasing the value starts the simulation earlier, encompassing a longer time frame.
Capital: Determines the initial amount of capital with which the simulation begins. Increasing this value simulates starting with more capital, which can affect the number of shares that can be initially bought. Decreasing this value simulates starting with less capital.
Contribution: Sets the monthly financial contribution added to the investment within the simulation. A higher contribution increases the investment each month and could lead to more shares being purchased over time. Lowering the contribution decreases the monthly investment amount.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Timely Opening Range Breakout Strategy [TORB] (Zeiierman)█ Overview
The Timely Opening Range Breakout (TORB) indicator builds upon the classic Open Range Breakout (ORB) concept. The ORB strategy is a popular trading setup used to identify trades around the opening range of an asset. It's based on the idea that the first few minutes (15-60 minutes) of trading often set the tone for the rest of the day, with breakouts above or below the opening range signifying potential trends.
TORB refines the concept by stating that a trade is only valid if there is sufficient market activity. This means a breakout beyond the upper or lower range is only of interest during the most active trading hours, as defined by PMMV (Per-Minute Mean Volume)
█ How It Works
ORB
The indicator works by first defining a session's opening range based on user-specified settings, including the session's start and end times and the applicable time zone. During this session, it calculates the high and low price points, which form the basis for identifying potential breakout levels.
PMMV
PMMV (Per-Minute Mean Volume) provides a snapshot of the market's activity level at each minute of the trading day. PMMV is calculated by averaging the trading volume in a one-minute interval over a specified number of trading days. This script uses the average volume over the last N periods to determine the PMMV value. This average volume provides a smoother representation of volume activity compared to using a single volume value. It considers the volume over a broader timeframe, filtering out short-term fluctuations and potentially offering a more reliable indicator of underlying market activity.
TORB
TORB works by integrating the Opening Range Breakout (ORB) highs and lows with the Per-Minute Mean Volume (PMMV) metric to assess the validity of breakouts. The objective is to identify breakouts from the opening high and low levels during periods of heightened market activity, as indicated by PMMV.
█ How to Use
To effectively utilize the Timely Opening Range Breakout (TORB) strategy, follow these steps:
Identify Active Hours: Employ PMMV to pinpoint periods of peak activity within the trading day.
Apply Basic ORB Rules: If the price surpasses the upper range (resistance), buy; if it breaches the lower range (support), sell.
Breakouts
The TORB strategy identifies breakout signals when the price moves beyond the established range, supported by volume exceeding a set threshold. This technique aims to eliminate false signals, focusing on price movements during high market activity.
█ Settings
Session
Trading Session: Customize the trading session's start and end times.
Volume
Volume analysis is integral to the TORB strategy, as it uses volume data to confirm the strength and validity of breakout signals.
Period: Sets the number of periods (or bars) to calculate the average volume, which is then used to assess market activity level.
Sensitivity and Significance: Adjusts how responsive the volume analysis is to changes in trading volume. By adjusting the sensitivity, traders can decide how much emphasis to place on volume spikes, potentially reducing false breakouts and focusing on those supported by significant trading activity.
Breakout Threshold
This setting establishes a criterion to identify when the price movement is significant enough.
Threshold: Traders set a threshold level to identify high market activity. If the PMMV is greater than or equal to this threshold, it indicates significant market activity.
Setting the correct threshold is key to balancing sensitivity and specificity. Too low of a threshold may lead to many false positives, while too high of a threshold might filter out potentially profitable breakouts. This setting helps in pinpointing when market activity indicates a strong move, thereby aligning trade entries with moments of heightened market momentum.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
