Tweet/X Post Timestamp - By LeviathanThis script allows you to generate visual timestamps of X/Twitter posts directly on your chart, highlighting the precise moment an X post/tweet was made. All you have to do is copy and paste the post URL.
◽️ Use Cases:
- News Trading: Traders can use this indicator to visually align market price actions with news or announcements made on X (formerly Twitter), aiding in the analysis of news impact on market volatility.
- Behavioral Analysis: Traders studying the influence of social media on price can use the timestamps to track correlations between specific posts and market reactions.
- Proof of Predictions: Traders can use this indicator to timestamp their market forecasts shared on X (formerly Twitter), providing a visual record of their predictions relative to actual market movements. This feature allows for transparent verification of the timing and accuracy of their analyses
◽️ Process of Timestamp Calculation
The calculation of the timestamp from a tweet ID involves the following steps:
Extracting the Post ID:
The script first parses the input URL provided by the user to extract the unique ID of the tweet or X post. This ID is embedded in the URL and is crucial for determining the exact posting time.
Calculating the Timestamp:
The post ID undergoes a mathematical transformation known as a right shift by 22 bits. This operation aligns the ID's timestamp to a base reference time used by the platform.
Adding Base Offset:
The result from the right shift is then added to a base offset timestamp (1288834974657 ms, the epoch used by Twitter/X). This converts the processed ID into a UNIX timestamp reflecting the exact moment the post was made.
Date-Time Conversion:
The UNIX timestamp is further broken down into conventional date and time components (year, month, day, hour, minute, second) using calculations that account for leap years and varying days per month.
Label Placement:
Based on user settings, labels displaying the timestamp, username, and other optional information such as price changes or pivot points are dynamically placed on the chart at the bar corresponding to the timestamp.
Forecasting
Psychosis - BitcoinPsychosis - Bitcoin is a cutting-edge TradingView indicator inspired by the "spiderline" methodology, which has gained acclaim for its precision in marking critical market junctures. Our script uniquely adapts this method by plotting key lines from significant price movements between 2018 and the peak of 2019. Remarkably, these lines have not been updated since 2019, yet the market continues to respect them, highlighting their continued relevance and effectiveness.
Key Features:
Persistent Historical Lines: This indicator leverages lines established from 2018 to 2019, which continue to be pivotal in market analysis, demonstrating the enduring influence of these historical levels on current price action.
Dynamic Customization: Users can tailor the visibility, color, and width of the lines to match their trading preferences, ensuring a seamless integration into personal trading strategies.
Strategic Trade Boxes: Based on the proximity of current prices to these historical lines, our script automatically plots color-coded 'Buy' and 'Sell' boxes, simplifying the decision-making process by providing clear visual cues for potential trades.
Benefits:
Enhances technical analysis by using time-tested support and resistance lines that remain pertinent, providing traders with a reliable foundation for predicting price movements.
Adaptable to multiple markets, proving the method's robustness and wide-ranging applicability beyond just the cryptocurrency sector.
Intuitive visual aids and customization features make it easy for traders to adapt their strategies quickly, especially useful in the fast-paced cryptocurrency market.
Usage Guide:
To utilize this indicator, add it to your Bitcoin chart from the TradingView library. Configure the settings as desired, and employ the historical spiderlines along with the buy/sell boxes to pinpoint strategic trading opportunities. These lines serve as a guide for potential market responses, aiding in more informed trading decisions.
Originality and Utility:
"Psychosis - Bitcoin" revitalizes the traditional spiderline approach by focusing on historically significant lines that have proven their value over time, without the need for constant updates. This enduring relevance makes our script an indispensable tool for traders looking to leverage historical data for future gains.
Yield Curve SpaghettiDisplays the difference in yield between multiple bond pairs for a given country.
Currently supports US, DE, and GB bonds
Edge AI Forecast [Edge Terminal]This indicator inputs the previous 150 closing prices in a simple two-layer neural network, normalizes the network inputs using a sigmoid function, uses a feedforward calculation to send it to the second layer, shows the MSE loss curve and uses both automatic and manual backpropagation (user input) to find the most likely forecast values and uses the analog forecasting algorithm to adjust and optimize the data furthermore to display potential prices on the chart.
Here's how it works:
The idea behind this script is to train a simple neural network to predict the future x values based on the sample data. For this, we use 2 types of data, Price and Volume.
The thinking behind this is that price alone can’t be used in this case because it doesn’t provide enough meaningful pattern data for the network but price and volume together can change the game. We’re planning to use more different data sets and expand on this in the future.
To avoid a bad mix of results, we technically have two neural networks, each processing a different data type, one for volume data and one for price data.
The actual prediction is decided by the way price and volume of the closing price relate to each other. Basically, the network passes the price and volume and finds the best relation between the two data set outputs and predicts where the price could be based on the upcoming volume of the latest candle.
The network adjusts the weights and biases using optimization algorithms like gradient descent to minimize the difference between the predicted and actual stock prices, typically measured by a loss function, (in this case, mean squared error) which you can see using the error rate bubble.
This is a good measure to see how well the network is performing and the idea is to adjust the settings inputs such as learning rate, epochs and data source to get the lowest possible error rate. That’s when you’re getting the most accurate prediction results.
For each data set, we use a multi-layer network. In a multi-layer neural network, the outputs of neurons in one layer serve as inputs to neurons in the next layer. Initially, the input layer of the neural network receives the historical data. Each input neuron represents a feature, such as previous stock prices and trading volumes over a specific period.
The hidden layers perform feature extraction and transformation through a series of weighted connections and activation functions. Each neuron in a hidden layer computes a weighted sum of the inputs from the previous layer, applies an activation function to the sum, and passes the result to the next layer using the feedforward (activation) function.
For extraction, we use a normalization function. This function takes a value or data (such as bar price) and divides it up by max scale which is the highest possible value of the bar. The idea is to take a normalized number, which is either below 1 or under 2 for simple use in the neural network layers.
For the activation, after computing the weighted sum, the neuron applies an activation function a(x). To introduce non-linearity into the model to pass it to the next layer. We use sigmoid activation functions in this case. The main reason we use sigmoid function is because the resulting number is between 0 to 1 and is better for models where we have to predict the probability as an output.
The final output of the network is passed as an input to the analog forecasting function. This is an algorithm commonly used in weather prediction systems. In this case, this is used to make predictions by comparing current values and assuming the patterns might repeat in the future.
There are many different ways to build an analog forecasting function but in our case, we’re used similarity measurement model:
X, as the current situation or set of current variables.
Y, as the outcome or variable of interest.
Si as the historical situations or patterns, where i ranges from 1 to n.
Vi as the vector of variables describing historical situation Si.
Oi as the outcome associated with historical situation Si.
First, we define a similarity measure sim(X,Vi) that quantifies the similarity between the current situation X and historical situation Si based on their respective variables Vi.
Then we select the K most similar historical situations (KNN Machine learning) based on the similarity measure sim(X,Vi). We denote the rest of the selected historical situations as {Si1, Si2,...Sik).
Then we examine the outcomes associated with the selected historical situations {Oi1, Oi2,...,Oik}.
Then we use the outcomes of the selected historical situations to forecast the future outcome Y^ using weighted averaging.
Finally, the output value of the analog forecasting is standardized using a standardization function which is the opposite of the normalization function. This function takes a normalized number and turns it back to its original value by multiplying it by the max scale (highest value of the bar). This function is used when the final number is produced by the network output at the end of the analog forecasting to turn the final value back into a price so it can be displayed on the chart with PineScript.
Settings:
Data source: Source of the neural network's input data.
Sample Bars: How many historical bars do you want to input into the neural network
Prediction Bars: How many bars you want the script to forecast
Show Training Rate: This shows the neural network's error rate for the optimization phase
Learning Rate: how many times you want the script to change the model in response to the estimated error (automatic)
Epochs: the network cycle or how many times you want to run the data through the network from the first layer to the last one.
Usage:
The sample bars input determines the number of historical bars to be used as a reference for the network. You need to change the Epochs and Learning Rate inputs for each asset and chart timeframe to get the lowest error rate.
On the surface, the highest possible epoch and learning rate should produce the most effective results but that's not always the case.
If the epochs rate is too high, there is a chance we face overfitting. Essentially, you might be over processing good data which can make it useless.
On the other hand, if the learning rate is too high, the network may overshoot the optimal solution and diverge. This is almost like the same issue I mentioned above with a high epoch rate.
Access:
It took over 4 months to develop this script and we’re constantly improving it so it took a lot of manpower to develop this script. Also when it comes to neural networks, Pine Script isn’t the most optimal language to build a neural network in, so we had to resort to a few proprietary mathematical formulas to ensure this runs smoothly without giving out an error for overprocessing, specially when you have multiple neural networks with many layers.
The optimization done to make this script run on Pine Script is basically state of the art and because of this, we would like to keep the code closed source at the moment.
On the other hand we don’t want to publish the code publicly as we want to keep the trading edge this script gives us in a closed loop, for our own small group of members so we have to keep the code closed. We only accept invites from expert traders who understand how this script and algo trading works and the type of edge it provides.
Additionally, at the moment we don’t want to share the code as some of the parts of this network, specifically the way we hand the data from neural network output into the analog method formula are proprietary code and we’d like to keep it that way.
You can contact us for access and if we believe this works for your trading case, we will provide you with access.
Nadaraya-Watson Probability [Yosiet]The script calculates and displays probability bands around price movements, offering insights into potential market trends.
Setting Up the Script
Window Size: Determines the length of the window for the Nadaraya-Watson estimation. A larger window smooths the data more but might lag current market conditions.
Bandwidth: Controls the bandwidth for the kernel regression, affecting the smoothness of the probability bands.
Reading the Data Table
The script dynamically updates a table positioned at the bottom right of your chart, providing real-time insights into market probabilities. Here's how to interpret the table:
Table Columns: The table is organized into three columns:
Up: Indicates the probability or relative change percentage for the upper band.
Down: Indicates the probability or relative change percentage for the lower band.
Table Rows: There are two main rows of interest:
P%: Shows the price change percentage difference between the bands and the closing price. A positive value in the "Up" column suggests the upper band is above the current close, indicating potential upward momentum. Conversely, a negative value in the "Down" column suggests downward momentum.
R%: Displays the relative inner change percentage difference between the bands, offering a measure of the market's volatility or stability within the bands.
Utilizing the Insights
Market Trends: A widening gap between the "Up" and "Down" percentages in the "P%" row might indicate increasing market volatility. Traders can use this information to adjust their risk management strategies accordingly.
Entry and Exit Points: The "R%" row provides insights into the relative position of the current price within the probability bands. Traders might consider positions closer to the lower band as potential entry points and positions near the upper band as exit points or take-profit levels.
Conclusion
The Nadaraya-Watson Probability script offers a sophisticated tool for traders looking to incorporate statistical analysis into their trading strategy. By understanding and utilizing the data presented in the script's table, traders can gain insights into market trends and volatility, aiding in decision-making processes. Remember, no indicator is foolproof; always consider multiple data sources and analyses when making trading decisions.
Mag7 IndexThis is an indicator index based on cumulative market value of the Magnificent 7 (AAPL, MSFT, NVDA, TSLA, META, AMZN, GOOG). Such an indicator for the famous Mag 7, against which your main security can be benchmarked, was missing from the TradingView user library.
The index bar values are calculated by taking the weighted average of the 7 stocks, relative to their market cap. Explicitly, we are multiplying each bar period's total outstanding stock amount by the OHLC of that period for each stock and dividing that value by the combined sum of outstanding stock for the 7 corporations. OHLC is taken for the extended trading session.
The index dynamically adjusts with respect to the chosen main security and the bars/line visible in the chart window; that is, the first close value is normalized to the main security's first close value. It provides recalculation of the performance in that chart window as you scroll (this isn't apparent in the demo chart above this description).
It can be useful for checking market breadth, or benchmarking price performance of the individual stock components that comprise the Magnificent 7. I prefer comparing the indicator to the Nasdaq Composite Index (IXIC) or S&P500 (SPX), but of course you can make comparisons to any security or commodity.
Settings Input Options:
1) Bar vs. Line - view as OHLC colored bars or line chart. Line chart color based on close above or below the previous period close as green or red line respectively.
2) % vs Regular - the final value for the window period as % return for that window or index value
3) Turn on/off - bottom right tile displaying window-period performance
Inspired by the simpler NQ 7 Index script by @RaenonX but with normalization to main security at start of window and additional settings input options.
Please provide feedback for additional features, e.g., if a regular/extended session option is useful.
US CPIIntroducing "US CPI" Indicator
The "US CPI" indicator, based on the Consumer Price Index (CPI) of the United States, is a valuable tool for analyzing inflation trends in the U.S. economy. This indicator is derived from official data provided by the U.S. Bureau of Labor Statistics (BLS) and is widely recognized as a key measure of inflationary pressures.
What is CPI?
The Consumer Price Index (CPI) is a measure that examines the average change in prices paid by consumers for a basket of goods and services over time. It is an essential economic indicator used to gauge inflationary trends and assess changes in the cost of living.
How is "US CPI" Calculated?
The "US CPI" indicator in this script retrieves CPI data from the Federal Reserve Economic Data (FRED) using the FRED:CPIAUCSL symbol. It calculates the rate of change in CPI over a specified period (typically 12 months) and applies technical analysis tools like moving averages (SMA and EMA) for trend analysis and smoothing.
Why Use "US CPI" Indicator?
1. Inflation Analysis: Monitoring CPI trends provides insights into the rate of inflation, which is crucial for understanding the overall economic health and potential impact on monetary policy.
2. Policy Implications: Changes in CPI influence decisions by policymakers, central banks, and investors regarding interest rates, fiscal policies, and asset allocation.
3. Market Sentiment: CPI data often impacts market sentiment, influencing trading strategies across various asset classes including currencies, bonds, and equities.
Key Features:
1. Customizable Smoothing: The indicator allows users to apply exponential moving average (EMA) smoothing to CPI data for clearer trend identification.
2. Visual Representation: The plotted line visually represents the inflation rate based on CPI data, helping traders and analysts assess inflationary pressures at a glance.
Sources and Data Integrity:
The CPI data used in this indicator is sourced directly from FRED, ensuring reliability and accuracy. The script incorporates robust security protocols to handle data requests and maintain data integrity in a trading environment.
In conclusion, the "US CPI" indicator offers a comprehensive view of inflation dynamics in the U.S. economy, providing traders, economists, and policymakers with valuable insights for informed decision-making and risk management.
Disclaimer: This indicator and accompanying analysis are for informational purposes only and should not be construed as financial advice. Users are encouraged to conduct their own research and consult with professional advisors before making investment decisions.
Evolving RThe "Evolving R" script is a script that allows to calculate a dynamic reward-to-risk ratio at any given point of time during the trade. Its fundamentals are based on Tom Dante's concept of an evolving reward-to-risk. The script requires a user to input their preferred stop loss price and the target price for a specific asset, and calculates the ratio between two differences: (a) the absolute difference between the target price and the current price and (b) the absolute difference between the stop loss price and the current price.
The output of the script displays the ratio discussed as a value called "Evolving R" in the table. In order to use it successfully, the user of the script has to input:
(a) Stop loss price for the asset
(b) Target price for the asset
Theoretically, as long as the evolving R value holds above or equal to 0.25, the trade is worth holding. However, if the evolving R value drops below 0.25, the table turns red and signifies that such a trade possesses more risk than there is a reward remaining: this alerts the user to possibly take profits prematurely without risking their unrealized gains for a minor amount of additional gain.
The graphics of the script are represented by green and red areas: the green area indicates the area between the current price and the target price, while the red area shows the distance between the current price and the stop loss price. This visual representation allows users to understand the relative reward-to-risk ratio graphically in addition to the given evolving R value output.
The script is used for any type of trading: whether trend-trading or in a ranging market, it doesn't suggest a user which market conditions they should use.
STIC bullish and bearish hunter with FVGSmart Trading and Investment Companion (STIC) is a sophisticated tool designed to identify and visualize inducement, market structure, market trends, track liquidity, and project and forecast price action for all applicable assets. it has been tested to work on all timeframes and has been traded on stock, forex, and crypto assets.
This script is an upgraded version of previous STIC indicator, which you can use in addition to it or separately as you deem fit
Traders/ investor that are familiar with market structure, inducement, candlestick psychology, trend-following indicatorsand Fair Value Gap FVG will find it easy to adopt this trading and investment companion. As stated below, this is how it works.
Features and how to use
1st of all, after adding the indicator to yoursuperchart, you want to endusre to set your to so as to enable you see the text labeling clearly. to do that, after adding the indicator to your chart, right click it on the list, you will se the Visual order option.
Special Extreme Alert!
By analyzing the trends and dimensions, we are able to predict market extremes conditions, especially in pump and dump scenarios. (the bullish or bearish P/D extreme alerts).
Market flip arrow
The arrows trigger to indicate when the market flips to bullish (green) or bearish (red) conditions. note that this arrow is just a market flip confirmation and it it triggered by market trends, it does not come one time and sometimes later after market trigger conditions had been met.
circled in white.
Buy or sell potential {The tiny yelow(sell) and blue(buy) triangle}
By analyzing market extreme conditions, market sentiment, and liquidity, the buy/sell potential alert trigger is able to determine the state of the market, This can and should be used in combination with the market flip line (MFL) [the yellow line from , market flip trigger (MFT) (purple line), and market support/resistance line (MSR)(blue line) .
Market flip Line (Blue line) (MFL): the MFL is useful to also understand the market phase; a candle close above the MFL is bullish, while a candle close Below, the MFL is bearish. You are, however, expected to experience market retests and rejections coupled with support and resistance to follow through with the predicted direction. Patience is a valuable virtue in trading.
Extended sell or buy hunt (Red and Green Triangle)
this is real-time triangles indicator just like every other indicator on theis chart that indicates the market direction labeled with buy and sell. Note that the market-extended extreme can occur multiple times in the same direction. Hence, we'll advise having multiple trade entries.
The flip support line
Market Flip Trigger Line (MFTL) (Magenta): When the market crosses and closes below or above the Market Flip Trigger Line, you should wait for a confirmation. a confirmation is usually a retest or rejection of the line. A candle close and reject indicates the market as flip direction and it is going for a correction or major reversal. it is applicable on all timeframe.
As mentioned earlier, if you understand market structure and sentiment, using the uFVG, iFVG, upLQTY, downLQTY and BOS will be easy. however, this is how it works, you may need tohave and expanded readbout market structure for additional knowledge.
upLQTY (Bullish liquidity inducement)
The indicator appear at the close and confirmation on the 3rd candle and it is extended to only appear on 200 bars applicable on all timeframes.
This is a bullish sentiment and liquidty inducement order block that occurs, leading to the break of trend structure and change of character. Meaning the market sentiment as change which is backed up by liquidity in that region, which mostly gets filled, especially on lower timeframes before the price action continues. If price revese breaks and hold above this region, it invalidates the order block. This will always appear when there is a confirmed change of character CHoCH to the bullish side.
downLQTY (Bearish liquidity inducement) The indicator appear at the close and confirmation on the 3rd candle and it is extended to only appear on 200 bars applicable on all timeframes. It is and inverse of the upLQTY.
like order block, these are supply and demand zones that has the potential to change the direction of a trade. This is a bearish order block that occurs, leading to the break of structure and change of character. Meaning there is bearish liquidity yet to be accounted for in the region, which mostly gets filled, especially on lower timeframes before the price action continues. If broken, it invalidates the order block. This will always appear when there is a confirmed change of character from CHoCH to the bearish side.
Fair Value Gap
From general knowledge, FVG also know as Fair value gaps are inbalnace created by a 3 candlestick pattern where the top of the bottom candles doesn't cross the bottom of the top candle. like order block, these are supply and demand zones that has the potential to change the direction of a trade. This mostly indicate the presense of big plays in the market. for STIC indicator, FVG are labeled as listed below;
UFVG, also FVGup, {Colour green box} = bullish imbalance fair value gap
IFVG, aka FVGdown, {Red box} = bearish imbalance fair value gap
OIFVG, {Yellow box, no label} = other imbalances fair value gab
You should not that FG has upper, lower and middle band, any of the this area can be induced and filled by price.
Alert Conditions!
Buy alert conditions
- Any bullish buy alert
- Bullish hunt
- Re-entry Buy
- Sharp Market Sell rejection
- Buy potential
- upLQTY
Long position Exit conditions
- ExtremeB
- Profit
- Sell hunt
The Entry, exit and trail profit alert trigger should be used as position exit conditions either for a Long (Buy) or Short (Sell) situation and should be set as OPB (Once Per Bar). Using it as entry for exit or vice versa as shown not to be very profitable. hence the need to combine with other order entry alerts like the Any bullish or Bearish alerts
Sell alert conditions ( NOTE: All Sell alert are not yet included in this current version as this is targeted towards bullrun.)
- Sell potential
- Sell triangle (Sell hunt)
- downLQTY
and any trail profit alert, this alert put into consideration all the conditions required to trail profit.
Risk management advice
Patience and a good risk management strategy are required to be profitable trader using this tool. You need to ensure not to overleverage, and you should have multiple entries in case the buy coditions/alert shows again below the previous buy alert before a sell condition/alert occurs.
Spot Martingale KuCoin - The Quant ScienceINTRODUCTION
Backtesting software of the Spot Martingale algorithm offered by the KuCoin exchange.
This script replicates the logic used by the KuCoin bot and is useful for analyzing strategy on any cryptocurrency historical series.
It's not intended as an automatic trading algorithm and does not offer the possibility of automatic order execution.
The trader will use this software exclusively to research the best parameters with which to work on KuCoin.
LOGIC OF EXECUTION
The execution of orders is composed as follows:
1) Start Martingale: initial order
2) Martingale-Number: orders following Start Martingale
(A) The software is designed and developed to replicate trading without taking into account technical indicators or particular market conditions. The Initial Order (Start Martingale) will be executed immediately the close of the previous Martingale when the balance of market orders is zero. It will use the capital set in the Properties section for the initial order.
(B) After the first order, the software will open new orders as the price decreases. For orders following Start Martingale, the initial capital, multiplier, and number of orders in the exponential growth context are considered. The multiplier is the factor that determines the proportional increase in capital with each new order. The number of orders, indicates how many times the multiplier is applied to increase the investment.
Example
To find out the capital used in Martingale order number 5, with a Multiple For Position Increase equal to 2 and a starting capital of $100, the formula will be as follows:
Martingale Order = ($100 * (2 * 2 * 2 * 2 * 2)) = $100 * 32 = $3.200
(C) A multiplier is used for each new order that will increase the quantity purchased.
(D) All previously open orders are closed once the take profit is reached.
USER MANUAL
The user interface consists of two main sections:
1. Settings
Percentage Drop for Position Increase (0.1-15%) : percentage distance between Martingale orders. For example, if you set 5% each new order will be opened after a 5% price decrease from the previous one.
Max Position Increases (1-15) : number of Martingale orders to be executed after Start Martingale. For example, if you set 10, up to10 orders will be opened after Start Martingale.
Multiple For Position Increase (1-2x) : capital multiplier. For example, if you set 2 each for each new order, the capital involved will be doubled, order by order.
Take Profit Percentage (0.5-1000%) : percentage take profit, calculated on the average entry price.
2. Date Range Backtesting
The Date Range Backtesting section adjusts the analysis period. The user can easily adjust the UI parameters, and automatically the software will update the data.
LIMITATIONS OF THE MODEL
Although the Martingale model is widely used in position management, even this model has limitations and is subject to real risks during particular market conditions. Knowing these conditions will help you understand which asset is best to use the strategy on.
The main risks in adopting this automatic strategy are 2:
1) The price falls below our last order.
It happens during periods of strong bear-market in which the price collapses abruptly without experiencing any pullback. In this case the algorithm will enter a drawdown phase and the strategy will become a loser. The trader will then have to consider whether to wait for a price recovery or to incur a loss by manually closing the algorithm.
2) The price increases quickly.
It happens during periods of strong bull-market in which the price rises abruptly without experiencing any pullback. In this case the algorithm will not optimize order execution, working only with Start Martingale in the vast majority of trades. Given the exponential nature of the investment, the algorithm will in this case generate a profit that is always less than that of the reference market.
The best market conditions to use this strategy are characterized by high volatility such as correction phases during a bull run and/or markets that exhibit sideways price trends (such as areas of accumulation or congestion where price will generate many false signals).
FEATURES
This script was developed by including features to optimize the user experience.
Includes a dashboard at launch that allows the user to intuitively enter backtesting parameters.
Includes graphical indicator that helps the user analyze the behavior of the strategy.
Includes a date period backtesting feature that allows the user to adjust and choose custom historical periods.
DISCLAIMER
This script was released using parameters researched solely for the BTC/USDT pair, 4H timeframe, traded on the KuCoin Exchange (2017-present). Do not consider this combination of parameters as universal and usable on all assets and timeframes.
[KVA] ICT Dealing rangesNaive aproach of Dynamic Detection of Dealing Ranges:
The script dynamically identifies dealing ranges based on sequences of upward or downward price movements. It uses arrays to track the highest highs and lowest lows after detecting two consecutive up or down bars, a fundamental step towards understanding market structure and potential shifts in momentum.
ICT Concept: Order Blocks & Fair Value Gaps. This aspect can be linked to the identification of order blocks (bullish or bearish) and fair value gaps. Order blocks are essentially the last bearish or bullish candle before a significant price move, which this script could approximate by identifying the highs and lows of potential reversal zones.
Red and Green Ranges for Bullish and Bearish Movements:
The script separates these movements into red (bearish) and green (bullish) ranges, effectively categorizing potential areas of selling and buying pressure.
ICT Concept: Liquidity Pools. Red ranges could be indicative of areas where selling might occur, potentially leading to liquidity pools below these ranges. Conversely, green ranges might indicate potential buying pressure, with liquidity pools above. These areas are critical for ICT traders, as they often represent zones where price may return to "hunt" for liquidity.
Horizontal Lines for High and Low Points:
The indicator draws horizontal lines at the high and low points of these ranges, offering visual cues for significant levels.
ICT Concept: Breaker Blocks & Mitigation Sequences. The high and low points of these ranges can be seen as potential breaker blocks or areas for future mitigation sequences. In ICT terms, breaker blocks are areas where institutional orders have overwhelmed retail stop clusters, creating potential entry points for trend continuation or reversal. The high and low points marked by the indicator could serve as references for these sequences, where price might return to retest these levels.
Customizability and Historical Depth:
With inputs like rangePlot and maxBarsBack, the indicator allows for customization of the number of ranges to display and how far back in the chart history it looks to identify these ranges. This flexibility is crucial for tailoring the analysis to different trading strategies and timeframes.
ICT Concept: Market Structure Analysis. The ability to adjust the depth and number of ranges plotted caters to a detailed market structure analysis, an essential component of ICT methodology. Traders can adjust these parameters to better understand the distribution of buying and selling pressure over time and how actions have shaped price movements.
ATR Price Targets w/POC
ATR Price Targets with Point of Control (POC):
This script is designed to help traders identify key price target levels based on configurable multipliers of the the Average True Range (ATR) and the volume based Point of Control (POC). It is intended for intraday traders looking to capture significant price movements.
Features:
ATR Price Targets: The script calculates three levels of price targets above and below the first bar of the day, based on the ATR of the last 22 days (assuming 5-minute candles). These targets are adjustable through the settings, allowing traders to set their own ATR multipliers.
Point of Control (POC): The POC is determined as the price level of the highest volume bar since the start time, providing an indication of the most traded price within the specified period.
Customizable Start Time: Traders can set their desired start time for the calculation of price targets and POC, allowing for flexibility in aligning the indicator with their trading strategy.
Plot Lines: The ATR price targets are plotted as lines for easy visualization on the chart.
Usage:
The ATR price targets can be used as potential take-profit or stop-loss levels.
The POC can serve as a key level for assessing market sentiment and potential reversals.
Traders can adjust the ATR multipliers and start time based on their specific trading style and market conditions.
Settings:
ATR Price Targets 1, 2, 3: Adjust the multipliers for the ATR price targets. By default, these are set to 1*ATR for T1+/T1-, 3*ATR for T2+/T2- and ATR*6 for T3+/T3-. Adjust with caution as the price targets found in defaults have proven to be more accurate over intraday cycles for volatile stocks.
Start Hour & Start Minute: Set the starting hour and minute for the calculations. By default, these are set to the opening 5 minute intraday bar, but can also be set to the opening bar of pre-market hours.
WinningWave - Devrim - By [Sercan.B]WinningWave - Devrim is an extremely advanced technical analysis tool designed to understand fluctuations in the financial markets and provide investors with reliable buying and selling decisions based on this information. This tool integrates various analysis methods to detect market trends and potential reversal points.
Fundamentally, WinningWave - Devrim deeply examines market movements using ZigZag analyses, harmonic pattern recognition, and various indicators such as RSI. ZigZag analyses filter out the noise of short-term price movements, offering a cleaner view of market trends and identifying significant peaks and troughs. The harmonic pattern recognition feature utilizes the recurring nature of specific price patterns to indicate potential buying and selling areas. These patterns provide clues about the possible future directions of price movements.
The strength of WinningWave - Devrim lies not only in identifying specific patterns and trends but also in presenting this information in a way that can be integrated into investors' strategies. Investors can clearly see when to enter or exit the market, thanks to the visual signals and patterns provided by the indicator.
Moreover, WinningWave - Devrim offers a set of customizable settings according to user preferences. This feature is critical for adapting to different market conditions and investment strategies. For example, an investor can adjust the ATR period, which measures volatility, to receive the most suitable signals for the current market condition.
Thanks to the specially tailored artificial intelligence coding for pattern finding for each time period, it alerts the user as a formation by analyzing the possible start and end areas of Trends specific to time periods. Additionally, a buy and sell signal compatible with harmonic pattern-based trend scanning technique accompanies harmonic formations. The buy or sell signal that comes immediately after the formation is created provides detailed awareness for the user to enter or exit the game.
The option to set separate alerts for the formation of each pattern and for every buy-sell signal frees users from the necessity of monitoring the screen constantly.
Lastly, WinningWave - Devrim offers investors a broad perspective for market analysis. With this tool, investors can identify market trends, potential reversal points, and buying and selling opportunities, optimize risk management, and apply their investment strategies more consciously.
Note: In line with my principle of personal neutrality, the description and usage of the indicator have been written by analyzing the codes through ChatGPT.
- Adhering to buy and sell signals is crucial for securing transactions at points where harmonic patterns form. This importance stems from the fact that the legs of harmonic formations can extend according to Fibonacci values. In other words, a harmonic formation signal does not have to occur immediately when it is received. Therefore, buy and sell signal labels, transformed into signals with settings compatible with formations and based on ATR, aim to minimize the margin of error in transactions.
- Harmonic formations are an analysis method in financial markets that is based on specific mathematical properties and ratios of price movements. These formations rely on mathematical concepts such as the Fibonacci number sequence and are used to predict how price movements may behave in the future. The idea behind harmonic formations is that certain patterns tend to repeat in market price movements. These patterns are used to identify potential buying and selling points.
- Paintings are representative. It was drawn for those who cannot see that zigzag lines and formation labels create mathematics and a formation.
- The Super Trend ATR (Average True Range) is a popular trend-following indicator used in financial markets. This indicator creates a line that moves above or below the price as a function of the Average True Range (ATR), indicating the direction of the trend. The Super Trend is used both to determine the direction of the trend and to identify potential entry and exit points.
The Super Trend indicator is based on two main parameters: a period of the ATR and a multiplier. The indicator measures the volatility of price movements over the specified ATR period and applies a multiplier based on this volatility. Then, this calculated value is placed above or below the price to determine the direction of the trend. If the price is above the line, the market is considered to be in an uptrend, and if below, in a downtrend.
Buy and Sell signals were written in the most compatible way with harmonic formations for the Super Trend ATR and adjusted according to the most accurate areas of Fibonacci values. Thus, if the signal following the formation of harmonic formations is entering an uptrend or downtrend, it helps us find the most suitable entry and exit points.
- Zigzag Indicator
The Zigzag indicator is a tool that filters out minor price fluctuations and noise to better see the direction of price movements. This indicator ignores price movements until they reach a specified percentage change and only connects the movements that exceed this change with a line. As a result, investors can more easily identify the main trends and potential reversal points in the market.
The Zigzag indicator is particularly effective in identifying the maximum and minimum points in the market and when used in conjunction with other technical analysis tools like Fibonacci retracement.
Pivot Points
Pivot points are a type of indicator used to determine the general trend of the market. This calculation is made using the high, low, and closing prices of the previous period. The basic pivot point is calculated by taking the average of these three values. Around this basic pivot point, resistance and support levels are also calculated. Resistance levels represent potential obstacles that the price may encounter moving upwards, while support levels represent potential "floor" areas when the price is moving downwards.
Pivot points are especially useful for daily trading activities because traders can use these points to predict the likely direction of market movements within the day. These points can also serve as potential buying and selling areas.
Both indicators assist investors and traders in analyzing market movements and making decisions, but it is always recommended to use them in conjunction with other analysis methods and consider market conditions.
Buffett IndicatorThis is an open-source version of the Buffett indicator. The old version was code-protected and broken, so I created another version.
It's computed simply as the entire SPX 500 capitalization divided by the US GDP. Since TradingView does not have data for the SPX 500 capitalization, I used quarterly values of SPX devisors as a proxy.
I tried to create another version of the Buffett indicator for other countries/indexes, but I can't find the data. If you can help me find data for index divisors, I can add more choices to this indicator.
It's interesting to see how this indicator's behavior has changed in the last few years. Levels that looked crazy are not so crazy anymore.
Disclaimer
Please remember that past performance may not be indicative of future results.
Due to various factors, including changing market conditions, the strategy may no longer perform as well as in historical backtesting.
This post and the script don’t provide any financial advice.
Murrey Math
The Murrey Math indicator is a set of horizontal price levels, calculated from an algorithm developed by stock trader T.J. Murray.
The main concept behind Murrey Math is that prices tend to react and rotate at specific price levels. These levels are calculated by dividing the price range into fixed segments called "ranges", usually using a number of 8, 16, 32, 64, 128 or 256.
Murrey Math levels are calculated as follows:
1. A particular price range is taken, for example, 128.
2. Divide the current price by the range (128 in this example).
3. The result is rounded to the nearest whole number.
4. Multiply that whole number by the original range (128).
This results in the Murrey Math level closest to the current price. More Murrey levels are calculated and drawn by adding and subtracting multiples of the range to the initially calculated level.
Traders use Murrey Math levels as areas of possible support and resistance as it is believed that prices tend to react and pivot at these levels. They are also used to identify price patterns and possible entry and exit points in trading.
The Murrey Math indicator itself simply calculates and draws these horizontal levels on the price chart, allowing traders to easily visualize them and use them in their technical analysis.
HOW TO USE THIS INDICATOR?
To use the Murrey Math indicator effectively, here are some tips:
1. Choose the appropriate Murrey Math range : The Murrey Math range input (128 by default in the provided code) determines the spacing between the levels. Common ranges used are 8, 16, 32, 64, 128, and 256. A smaller range will give you more levels, while a larger range will give you fewer levels. Choose a range that suits the volatility and trading timeframe you're working with.
2. Identify potential support and resistance levels: The horizontal lines drawn by the indicator represent potential support and resistance levels based on the Murrey Math calculation. Prices often react or reverse at these levels, so they can be used to spot areas of interest for entries and exits.
3. Look for price reactions at the levels: Watch for price action like rejections, bounces, or breakouts at the Murrey Math levels. These reactions can signal potential trend continuation or reversal setups.
4. Trail stop-loss orders: You can place stop-loss orders just below/above the nearest Murrey Math level to manage risk if the price moves against your trade.
5. Set targets at future levels: Project potential profit targets by looking at upcoming Murrey Math levels in the direction of the trend.
7. Adjust range as needed: If prices are consistently breaking through levels without reacting, try adjusting the range input to a different value to see if it provides better levels.
In which asset can this indicator perform better?
The Murrey Math indicator can potentially perform well on any liquid financial asset that exhibits some degree of mean-reversion or trading range behavior. However, it may be more suitable for certain asset classes or trading timeframes than others.
Here are some assets and scenarios where the Murrey Math indicator can potentially perform better:
1. Forex Markets: The foreign exchange market is known for its ranging and mean-reverting nature, especially on higher timeframes like the daily or weekly charts. The Murrey Math levels can help identify potential support and resistance levels within these trading ranges.
2. Futures Markets: Futures contracts, such as those for commodities (e.g., crude oil, gold, etc.) or equity indices, often exhibit trading ranges and mean-reversion trends. The Murrey Math indicator can be useful in identifying potential turning points within these ranges.
3. Stocks with Range-bound Behavior: Some stocks, particularly those of large-cap companies, can trade within well-defined ranges for extended periods. The Murrey Math levels can help identify the boundaries of these ranges and potential reversal points.
4. I ntraday Trading: The Murrey Math indicator may be more effective on lower timeframes (e.g., 1-hour, 30-minute, 15-minute) for intraday trading, as prices tend to respect support and resistance levels more closely within shorter time periods.
5. Trending Markets: While the Murrey Math indicator is primarily designed for range-bound markets, it can also be used in trending markets to identify potential pullback or continuation levels.
Momentum Ghost Machine [ChartPrime]Momentum Ghost Machine (ChartPrime) is designed to be the next generation in momentum/rate of change analysis. This indicator utilizes the properties of one of our favorite filters to create a more accurate and stable momentum oscillator by using a high quality filtered delayed signal to do the momentum comparison.
Traditional momentum/roc uses the raw price data to compare current price to previous price to generate a directional oscillator. This leaves the oscillator prone to false readings and noisy outputs that leave traders unsure of the real likelihood of a future movement. One way to mitigate this issue would be to use some sort of moving average. Unfortunately, this can only go so far because simple moving average algorithms result in a poor reconstruction of the actual shape of the underlying signal.
The windowed sinc low pass filter is a linear phase filter, meaning that it doesn't change the shape or size of the original signal when applied. This results in a faithful reconstruction of the original signal, but without the "high frequency noise". Just like any filter, the process of applying it requires that we have "future" samples resulting in a time delay for real time applications. Fortunately this is a great thing in the context of a momentum oscillator because we need some representation of past price data to compare the current price data to. By using an ideal low pass filter to generate this delayed signal we can super charge the momentum oscillator and fix the majority of issues its predecessors had.
This indicator has a few extra features that other momentum/roc indicators dont have. One major yet simple improvement is the inclusion of a moving average to help gauge the rate of change of this indicator. Since we included a moving average, we thought it would only be appropriate to add a histogram to help visualize the relationship between the signal and its average. To go further with this we have also included linear extrapolation to further help you predict the momentum and direction of this oscillator. Included with this extrapolation we have also added the histogram in the extrapolation to further enhance its visual interpretation. Finally, the inclusion of a candle coloring feature really drives how the utility of the Momentum Machine .
There are three distinct options when using the candle coloring feature: Direct, MA, and Both. With direct the candles will be colored based on the indicators direction and polarity. When it is above zero and moving up, it displays a green color. When it is above zero and moving down it will display a light green color. Conversely, when the indicator is below zero and moving down it displays a red color, and when it it moving up and below zero it will display a light red color. MA coloring will color the candles just like a MACD. If the signal is above its MA and moving up it will display a green color, and when it is above its MA and moving down it will display a light green color.
When the signal is below its MA and moving down it will display a red color, and when its below its ma and moving up it will display a light red color. Both combines the two into a single color scheme providing you with the best of both worlds. If the indicator is above zero it will display the MA colors with a slight twist. When the indicator is moving down and is below its MA it will display a lighter color than before, and when it is below zero and is above its MA it will display a darker color color.
Length of 50 with a smoothing of 100
Length of 50 with a smoothing of 25
By default, the indicator is set to a momentum length of 50, with a post smoothing of 2. We have chosen the longer period for the momentum length to highlight the performance of this indicator compared to its ancestors. A major point to consider with this indicator is that you can only achieve so much smoothing for a chosen delay. This is because more data is required to produce a smoother signal at a specified length. Once you have selected your desired momentum length you can then select your desired momentum smoothing . This is made possible by the use of the windowed sinc low pass algorithm because it includes a frequency cutoff argument. This means that you can have as little or as much smoothing as you please without impacting the period of the indicator. In the provided examples above this paragraph is a visual representation of what is going on under the hood of this indicator. The blue line is the filtered signal being compared to the current closing price. As you can see, the filtered signal is very smooth and accurately represents the underlying price action without noise.
We hope that users can find the same utility as we did in this indicator and that it levels up your analysis utilizing the momentum oscillator or rate of change.
Enjoy
Price Action Fractal Forecasts [AlgoAlpha]🔮 Price Action Fractal Forecasts - Unleash the Power of Historical Patterns! 🌌✨
Dive into the future with AlgoAlpha's Price Action Fractal Forecasts ! This innovative indicator utilizes the mesmerizing complexity of fractals to predict future price movements, offering traders a unique edge in the market. By analyzing historical price action and identifying repeating patterns, this tool forecasts future price trends, providing visually engaging and actionable insights.
Key Features:
🔄 Flexible Data Series Selection: Choose your preferred data series for precise analysis.
🕰 Flexible Training and Reference Data Windows: Customize the length of training data and reference periods to match your trading style.
📈 Custom Forecast Length: Adjust the forecast horizon to suit your strategic objectives.
🌈 Customizable Visual Elements: Tailor the colors of forecast deviation cones, data reference areas, and more for optimal chart readability.
🔄 Anticipatory and Repetitive Forecast Modes: Select between anticipating future trends or identifying repetitive patterns for forecasts.
🔎 Enhanced Similarity Search: Leverages correlation metrics to find the most similar historical data segments.
📊 Forecast Deviation Cone: Visualize potential price range deviations with adjustable multipliers.
🚀 Quick Guide to Maximizing Your Trading with Price Action Fractal Forecasts:
🛠 Add the Indicator: Search for "Price Action Fractal Forecasts" in TradingView's Indicators & Strategies. Customize settings according to your trading strategy.
📊 Strategic Forecasting: Monitor the forecast deviation cone and forecast directional changes for insights into potential future price movements.
🔔 Alerts for Swift Action: Set up notifications based on forecast changes to stay ahead of market movements without constant monitoring.
Behind the Magic: How It Works
The core of the Price Action Fractal Forecasts lies in its ability to compare current market behavior with historical data to unearth similar patterns. It first establishes a training data window to analyze historical prices. Within this window, it then defines a reference length to identify the most recent price action that will serve as the basis for comparison. The indicator searches through the historical data within the training window to find segments that closely match the recent price action in the reference period.
Depending on whether you choose the anticipatory or repetitive forecast mode, the indicator either looks ahead to predict future prices based on past outcomes following similar patterns or focuses on the repeating patterns within the reference period itself for forecasts. The forecast's direction can be configured to reflect the mean average of forecasted prices or the end-point relative to the start-point of the forecast, offering flexibility in how forecasts are interpreted.
To enhance the comprehensiveness and visualization, the indicator features a forecast deviation cone. This cone represents the potential range of price movements, providing a visual cue for volatility and uncertainty in the forecasted prices. The intensity of this cone can be adjusted to suit individual preferences, offering a visual guide to the level of risk and uncertainty associated with the forecasted price path.
Embrace the fractal magic of markets with AlgoAlpha's Price Action Fractal Forecasts and transform your trading today! 🌟🚀
Daily Close GAP Detector [Yosiet]User Manual for "Daily Close GAP Detector "
Overview
This script is designed to help traders identify and react to significant gaps in daily market prices. It plots daily open and close prices and highlights significant gaps with a cross. The script is particularly useful for identifying potential breakouts or reversals based on these gaps.
Configuration
GAP Close Threshold: This input allows you to set a threshold for the gap size that you consider significant. The default value is 0.001.
Timeframe Seeker: This input lets you choose the timeframe for the gap detection. The default is 'D' for daily.
Features
Daily Open and Close Lines: The script plots daily open and close prices. If the close price is lower than the open price, the line is colored red; otherwise, it's green.
Gap Detection: It calculates the difference between the current day's close and the previous day's close, both adjusted for the selected timeframe. If this difference exceeds the threshold, it's considered a significant gap.
Significant Gap Indicator: A cross is plotted on the chart to indicate significant gaps. The color of the cross indicates whether the gap is a short or long gap: red for short gaps and green for long gaps.
Alert Conditions: The script sets up alert conditions for short and long gap breakouts. You can customize the alert messages to include details like the ticker symbol, interval, price, and exchange.
How to Use
Add the Script to Your Chart: Copy the script into the Pine Script editor on TradingView and add it to your chart.
Configure Inputs: Adjust the "GAP Close Threshold" and "Timeframe Seeker" inputs as needed.
Review the Chart: The script will overlay daily open and close prices on your chart, along with crosses indicating significant gaps.
Set Alerts: Use the script's alert conditions to set up alerts for short and long gap breakouts. You can customize the alert messages to suit your trading strategy.
Extending the Code
To extend this script, you can modify the gap detection logic, add more indicators, or integrate it with other scripts for a more comprehensive trading strategy. Remember to test any changes thoroughly before using them in live trading.
Pi Cycle Indicator Low and High
The Pi Cycle Indicator is a technical analysis tool used in finance, particularly within cryptocurrency markets, to identify potential market tops or bottoms. It is based on two moving averages: the 111-day moving average and the 350-day moving average of Bitcoin's price. The indicator suggests that when these two moving averages converge or cross each other, it may signal significant market turning points. The name "Pi Cycle" comes from the mathematical relationship between these two moving averages, roughly equivalent to the mathematical constant Pi (3.14). Traders and analysts use this indicator to gauge potential trend reversals and make informed decisions regarding their trading strategies. However, like any technical analysis tool, it should be used in conjunction with other indicators and fundamental analysis for a comprehensive understanding of market conditions.
Bitcoin 5A Strategy@LilibtcIn our long-term strategy, we have deeply explored the key factors influencing the price of Bitcoin. By precisely calculating the correlation between these factors and the price of Bitcoin, we found that they are closely linked to the value of Bitcoin. To more effectively predict the fair price of Bitcoin, we have built a predictive model and adjusted our investment strategy accordingly based on this model. In practice, the prediction results of this model correspond quite high with actual values, fully demonstrating its reliability in predicting price fluctuations.
When the future is uncertain and the outlook is unclear, people often choose to hold back and avoid risks, or even abandon their original plans. However, the prediction of Bitcoin is full of challenges, but we have taken the first step in exploring.
Table of contents:
Usage Guide
Step 1: Identify the factors that have the greatest impact on Bitcoin price
Step 2: Build a Bitcoin price prediction model
Step 3: Find indicators for warning of bear market bottoms and bull market tops
Step 4: Predict Bitcoin Price in 2025
Step 5: Develop a Bitcoin 5A strategy
Step 6: Verify the performance of the Bitcoin 5A strategy
Usage Restrictions
🦮Usage Guide:
1. On the main interface, modify the code, find the BTCUSD trading pair, and select the BITSTAMP exchange for trading.
2. Set the time period to the daily chart.
3. Select a logarithmic chart in the chart type to better identify price trends.
4. In the strategy settings, adjust the options according to personal needs, including language, display indicators, display strategies, display performance, display optimizations, sell alerts, buy prompts, opening days, backtesting start year, backtesting start month, and backtesting start date.
🏃Step 1: Identify the factors that have the greatest impact on Bitcoin price
📖Correlation Coefficient: A mathematical concept for measuring influence
In order to predict the price trend of Bitcoin, we need to delve into the factors that have the greatest impact on its price. These factors or variables can be expressed in mathematical or statistical correlation coefficients. The correlation coefficient is an indicator of the degree of association between two variables, ranging from -1 to 1. A value of 1 indicates a perfect positive correlation, while a value of -1 indicates a perfect negative correlation.
For example, if the price of corn rises, the price of live pigs usually rises accordingly, because corn is the main feed source for pig breeding. In this case, the correlation coefficient between corn and live pig prices is approximately 0.3. This means that corn is a factor affecting the price of live pigs. On the other hand, if a shooter's performance improves while another shooter's performance deteriorates due to increased psychological pressure, we can say that the former is a factor affecting the latter's performance.
Therefore, in order to identify the factors that have the greatest impact on the price of Bitcoin, we need to find the factors with the highest correlation coefficients with the price of Bitcoin. If, through the analysis of the correlation between the price of Bitcoin and the data on the chain, we find that a certain data factor on the chain has the highest correlation coefficient with the price of Bitcoin, then this data factor on the chain can be identified as the factor that has the greatest impact on the price of Bitcoin. Through calculation, we found that the 🔵number of Bitcoin blocks is one of the factors that has the greatest impact on the price of Bitcoin. From historical data, it can be clearly seen that the growth rate of the 🔵number of Bitcoin blocks is basically consistent with the movement direction of the price of Bitcoin. By analyzing the past ten years of data, we obtained a daily correlation coefficient of 0.93 between the number of Bitcoin blocks and the price of Bitcoin.
🏃Step 2: Build a Bitcoin price prediction model
📖Predictive Model: What formula is used to predict the price of Bitcoin?
Among various prediction models, the linear function is the preferred model due to its high accuracy. Take the standard weight as an example, its linear function graph is a straight line, which is why we choose the linear function model. However, the growth rate of the price of Bitcoin and the number of blocks is extremely fast, which does not conform to the characteristics of the linear function. Therefore, in order to make them more in line with the characteristics of the linear function, we first take the logarithm of both. By observing the logarithmic graph of the price of Bitcoin and the number of blocks, we can find that after the logarithm transformation, the two are more in line with the characteristics of the linear function. Based on this feature, we choose the linear regression model to establish the prediction model.
From the graph below, we can see that the actual red and green K-line fluctuates around the predicted blue and 🟢green line. These predicted values are based on fundamental factors of Bitcoin, which support its value and reflect its reasonable value. This picture is consistent with the theory proposed by Marx in "Das Kapital" that "prices fluctuate around values."
The predicted logarithm of the market cap of Bitcoin is calculated through the model. The specific calculation formula of the Bitcoin price prediction value is as follows:
btc_predicted_marketcap = math.exp(btc_predicted_marketcap_log)
btc_predicted_price = btc_predicted_marketcap / btc_supply
🏃Step 3: Find indicators for early warning of bear market bottoms and bull market tops
📖Warning Indicator: How to Determine Whether the Bitcoin Price has Reached the Bear Market Bottom or the Bull Market Top?
By observing the Bitcoin price logarithmic prediction chart mentioned above, we notice that the actual price often falls below the predicted value at the bottom of a bear market; during the peak of a bull market, the actual price exceeds the predicted price. This pattern indicates that the deviation between the actual price and the predicted price can serve as an early warning signal. When the 🔴 Bitcoin price deviation is very low, as shown by the chart with 🟩green background, it usually means that we are at the bottom of the bear market; Conversely, when the 🔴 Bitcoin price deviation is very high, the chart with a 🟥red background indicates that we are at the peak of the bull market.
This pattern has been validated through six bull and bear markets, and the deviation value indeed serves as an early warning signal, which can be used as an important reference for us to judge market trends.
🏃Step 4:Predict Bitcoin Price in 2025
📖Price Upper Limit
According to the data calculated on February 25, 2024, the 🟠upper limit of the Bitcoin price is $194,287, which is the price ceiling of this bull market. The peak of the last bull market was on November 9, 2021, at $68,664. The bull-bear market cycle is 4 years, so the highest point of this bull market is expected in 2025. That is where you should sell the Bitcoin. and the upper limit of the Bitcoin price will exceed $190,000. The closing price of Bitcoin on February 25, 2024, was $51,729, with an expected increase of 2.7 times.
🏃Step 5: Bitcoin 5A Strategy Formulation
📖Strategy: When to buy or sell, and how many to choose?
We introduce the Bitcoin 5A strategy. This strategy requires us to generate trading signals based on the critical values of the warning indicators, simulate the trades, and collect performance data for evaluation. In the Bitcoin 5A strategy, there are three key parameters: buying warning indicator, batch trading days, and selling warning indicator. Batch trading days are set to ensure that we can make purchases in batches after the trading signal is sent, thus buying at a lower price, selling at a higher price, and reducing the trading impact cost.
In order to find the optimal warning indicator critical value and batch trading days, we need to adjust these parameters repeatedly and perform backtesting. Backtesting is a method established by observing historical data, which can help us better understand market trends and trading opportunities.
Specifically, we can find the key trading points by watching the Bitcoin price log and the Bitcoin price deviation chart. For example, on August 25, 2015, the 🔴 Bitcoin price deviation was at its lowest value of -1.11; on December 17, 2017, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.69; on March 16, 2020, the 🔴 Bitcoin price deviation was at its lowest value at the time, -0.91; on March 13, 2021, the 🔴 Bitcoin price deviation was at its highest value at the time, 1.1; on December 31, 2022, the 🔴 Bitcoin price deviation was at its lowest value at the time, -1.
To ensure that all five key trading points generate trading signals, we set the warning indicator Bitcoin price deviation to the larger of the three lowest values, -0.9, and the smallest of the two highest values, 1. Then, we buy when the warning indicator Bitcoin price deviation is below -0.9, and sell when it is above 1.
In addition, we set the batch trading days as 25 days to implement a strategy that averages purchases and sales. Within these 25 days, we will invest all funds into the market evenly, buying once a day. At the same time, we also sell positions at the same pace, selling once a day.
📖Adjusting the threshold: a key step to optimizing trading strategy
Adjusting the threshold is an indispensable step for better performance. Here are some suggestions for adjusting the batch trading days and critical values of warning indicators:
• Batch trading days: Try different days like 25 to see how it affects overall performance.
• Buy and sell critical values for warning indicators: iteratively fine-tune the buy threshold value of -0.9 and the sell threshold value of 1 exhaustively to find the best combination of threshold values.
Through such careful adjustments, we may find an optimized approach with a lower maximum drawdown rate (e.g., 11%) and a higher cumulative return rate for closed trades (e.g., 474 times). The chart below is a backtest optimization chart for the Bitcoin 5A strategy, providing an intuitive display of strategy adjustments and optimizations.
In this way, we can better grasp market trends and trading opportunities, thereby achieving a more robust and efficient trading strategy.
🏃Step 6: Validating the performance of the Bitcoin 5A Strategy
📖Model interpretability validation: How to explain the Bitcoin price model?
The interpretability of the model is represented by the coefficient of determination R squared, which reflects the degree of match between the predicted value and the actual value. I divided all the historical data from August 18, 2015 into two groups, and used the data from August 18, 2011 to August 18, 2015 as training data to generate the model. The calculation result shows that the coefficient of determination R squared during the 2011-2015 training period is as high as 0.81, which shows that the interpretability of this model is quite high. From the Bitcoin price logarithmic prediction chart in the figure below, we can see that the deviation between the predicted value and the actual value is not far, which means that most of the predicted values can explain the actual value well.
The calculation formula for the coefficient of determination R squared is as follows:
residual = btc_close_log - btc_predicted_price_log
residual_square = residual * residual
train_residual_square_sum = math.sum(residual_square, train_days)
train_mse = train_residual_square_sum / train_days
train_r2 = 1 - train_mse / ta.variance(btc_close_log, train_days)
📖Model stability verification: How to affirm the stability of the Bitcoin price model when new data is available?
Model stability is achieved through model verification. I set the last day of the training period to February 2, 2024 as the "verification group" and used it as verification data to verify the stability of the model. This means that after generating the model if there is new data, I will use these new data together with the model for prediction, and then evaluate the interpretability of the model. If the coefficient of determination when using verification data is close to the previous training one and both remain at a high level, then we can consider this model as stability. The coefficient of determination calculated from the validation period data and model prediction results is as high as 0.83, which is close to the previous 0.81, further proving the stability of this model.
📖Performance evaluation: How to accurately evaluate historical backtesting results?
After detailed strategy testing, to ensure the accuracy and reliability of the results, we need to carry out a detailed performance evaluation on the backtest results. The key evaluation indices include:
• Net value curve: As shown in the rose line, it intuitively reflects the growth of the account net value. By observing the net value curve, we can understand the overall performance and profitability of the strategy.
The basic attributes of this strategy are as follows:
Trading range: 2015-8-19 to 2024-2-18, backtest range: 2011-8-18 to 2024-2-18
Initial capital: 1000USD, order size: 1 contract, pyramid: 50 orders, commission rate: 0.2%, slippage: 20 markers.
In the strategy tester overview chart, we also obtained the following key data:
• Net profit rate of closed trades: as high as 474 times, far exceeding the benchmark, as shown in the strategy tester performance summary chart, Bitcoin buys and holds 210 times.
• Number of closed trades and winning percentage: 100 trades were all profitable, showing the stability and reliability of the strategy.
• Drawdown rate & win-loose ratio: The maximum drawdown rate is only 11%, far lower than Bitcoin's 78%. Profit factor, or win-loose ratio, reached 500, further proving the advantage of the strategy.
Through these detailed evaluations, we can see clearly the excellent balance between risk and return of the Bitcoin 5A strategy.
⚠️Usage Restrictions: Strategy Application in Specific Situations
Please note that this strategy is designed specifically for Bitcoin and should not be applied to other assets or markets without authorization. In actual operations, we should make careful decisions according to our risk tolerance and investment goals.
Order-Block Detector ICT/SMT + FVG + SignalsOrderBlock-Finder
This script shows order-blocks (OB) and fair-value-gaps (FVG). Additionaly there are entry signals for OB and FVG. The Dist-Parameter tell how many candles should exist between the beginning of the OB or FVG and the pullback.
Order-Blocks
An order block in trading typically refers to a significant grouping of buy or sell orders at a particular price level within a financial market. These blocks of orders can influence price movement when they are executed. Here's a breakdown:
Buy Order Block: This occurs when there's a large concentration of buy orders at a specific price level. It indicates a significant interest among traders to purchase the asset if the price reaches that level.
Sell Order Block: Conversely, a sell order block happens when there's a notable accumulation of sell orders at a particular price level. This suggests that many traders are willing to sell the asset if the price reaches that level.
Impact on Price: Order blocks can influence price movement because when the market approaches these levels, the orders within the block may be triggered, leading to increased buying or selling pressure, depending on the type of block. This surge in trading activity can cause the price to either bounce off the level or break through it.
Support and Resistance: Order blocks are often associated with support and resistance levels. A buy order block may act as support, preventing the price from falling further, while a sell order block may serve as resistance, hindering upward price movement.
Fair-Value-Gap
The fair value gap in trading refers to the difference between the current market price of an asset and its calculated fair value. This concept is often used in financial markets, especially in the context of stocks and other securities. Here's a breakdown:
Market Price: The market price is the price at which an asset is currently trading in the market. It is determined by the interaction of supply and demand forces, as well as various other factors such as news, sentiment, and economic conditions.
Fair Value: Fair value represents the estimated intrinsic value of an asset based on fundamental analysis, which includes factors such as earnings, dividends, cash flow, growth prospects, and prevailing interest rates. It's essentially what an asset should be worth based on its fundamentals.
Fair Value Calculation: Analysts and investors use various methods to calculate the fair value of an asset. Common approaches include discounted cash flow (DCF) analysis, comparable company analysis (CCA), and dividend discount models (DDM), among others.
Fair Value Gap: The fair value gap is the numerical difference between the calculated fair value of an asset and its current market price. If the market price is higher than the fair value, it suggests that the asset may be overvalued. Conversely, if the market price is lower than the fair value, it indicates that the asset may be undervalued.
Trading Implications: Traders and investors often pay attention to the fair value gap to identify potential trading opportunities. If the market price deviates significantly from the fair value, it may present opportunities to buy or sell the asset with the expectation that the market price will eventually converge towards its fair value.
CVD Divergence Strategy.1.mmThis is the matching Strategy version of Indicator of the same name.
As a member of the K1m6a Lions discussion community we often use versions of the Cumulative Volume Delta indicator
as one of our primary tools along with RSI, RSI Divergences, Open interest, Volume Profile, TPO and Fibonacci levels.
We also discuss visual interpretations of CVD Divergences across multiple time frames much like RSI divergences.
RSI Divergences can be identified as possible Bullish reversal areas when the RSI is making higher low points while
the price is making lower low points.
RSI Divergences can be identified as possible Bearish reversal areas when the RSI is making lower high points while
the price is making higher high points.
CVD Divergences can also be identified the same way on any timeframe as possible reversal signals. As with RSI, these Divergences
often occur as a trend's momentum is giving way to lower volume and areas when profits are being taken signaling a possible reversal
of the current trending price movement.
Hidden Divergences are identified as calculations that may be signaling a continuation of the current trend.
Having not found any public domain versions of a CVD Divergence indicator I have combined some public code to create this
indicator and matching strategy. The calculations for the Cumulative Volume Delta keep a running total for the differences between
the positive changes in volume in relation to the negative changes in volume. A relative upward spike in CVD is created when
there is a large increase in buying vs a low amount of selling. A relative downward spike in CVD is created when
there is a large increase in selling vs a low amount of buying.
In the settings menu, the is a drop down to be used to view the results in alternate timeframes while the chart remains on current timeframe. The Lookback settings can be adjusted so that the divs show on a more local, spontaneous level if set at 1,1,60,1. For a deeper, wider view of the divs, they can be set higher like 7,7,60,7. Adjust them all to suit your view of the divs.
To create this indicator/strategy I used a portion of the code from "Cumulative Volume Delta" by @ contrerae which calculates
the CVD from aggregate volume of many top exchanges and plots the continuous changes on a non-overlay indicator.
For the identification and plotting of the Divergences, I used similar code from the Tradingview Technical "RSI Divergence Indicator"
This indicator should not be used as a stand-alone but as an additional tool to help identify Bullish and Bearish Divergences and
also Bullish and Bearish Hidden Divergences which, as opposed to regular divergences, may indicate a continuation.
CVD Divergence Indicator.1.mmAs a member of the K1m6a Lions discussion community we often use versions of the Cumulative Volume Delta indicator
as one of our primary tools along with RSI, RSI Divergences, Open interest, Volume Profile, TPO and Fibonacci levels.
We also discuss visual interpretations of CVD Divergences across multiple time frames much like RSI divergences.
RSI Divergences can be identified as possible Bullish reversal areas when the RSI is making higher low points while
the price is making lower low points.
RSI Divergences can be identified as possible Bearish reversal areas when the RSI is making lower high points while
the price is making higher high points.
CVD Divergences can also be identified the same way on any timeframe as possible reversal signals. As with RSI, these Divergences
often occur as a trend's momentum is giving way to lower volume and areas when profits are being taken signaling a possible reversal
of the current trending price movement.
Hidden Divergences are identified as calculations that may be signaling a continuation of the current trend.
Having not found any public domain versions of a CVD Divergence indicator I have combined some public code to create this
indicator and matching strategy. The calculations for the Cumulative Volume Delta keep a running total for the differences between
the positive changes in volume in relation to the negative changes in volume. A relative upward spike in CVD is created when
there is a large increase in buying vs a low amount of selling. A relative downward spike in CVD is created when
there is a large increase in selling vs a low amount of buying.
In the settings menu, the is a drop down to be used to view the results in alternate timeframes while the chart remains on current timeframe. The Lookback settings can be adjusted so that the divs show on a more local, spontaneous level if set at 1,1,60,1. For a deeper, wider view of the divs, they can be set higher like 7,7,60,7. Adjust them all to suit your view of the divs.
To create this indicator/strategy I used a portion of the code from "Cumulative Volume Delta" by @ contrerae which calculates
the CVD from aggregate volume of many top exchanges and plots the continuous changes on a non-overlay indicator.
For the identification and plotting of the Divergences, I used similar code from the Tradingview Technical "RSI Divergence Indicator"
This indicator should not be used as a stand-alone but as an additional tool to help identify Bullish and Bearish Divergences and
also Bullish and Bearish Hidden Divergences which, as opposed to regular divergences, may indicate a continuation.
