Quatro SMA Strategy [4h]Hello, I would like to present to you The "Quatro SMA" strategy
Strategy is based on four simple moving averages of different lengths and monitoring trading volume. The key idea is to identify strong market trends by comparing short-term moving averages with the long-term SMA. The strategy generates buy signals when all short-term SMAs are above the SMA(200) and the volume confirms the strength of the move. Similarly, sell signals are generated when all short-term SMAs are below the SMA(200), and the volume is sufficiently high.
The strategy manages risk by applying a stop loss and three different Take Profit levels (TP1, TP2, TP3), with varying percentages of the position closed at each level.
Each Take Profit level is triggered at a specific percentage gain, with the position being closed gradually depending on the achieved targets. The percentage of the position closed at each TP level is also defined by the user.
Indicators and Parameters:
Simple Moving Averages (SMA):
The script utilizes four simple moving averages with different lengths (4, 16, 32, 200). The first three SMAs (SMA1, SMA2, SMA3) are used to determine the trend direction, while the fourth SMA (with a length of 200) serves as a support/resistance line.
Volume:
The script monitors trading volume and checks if the current volume exceeds 2.5 times the average volume of the last 40 candles. High volume is considered as confirmation of trend strength.
Entry Conditions:
- Long Position: Triggered when SMA1 > SMA2 > SMA3, the closing price is above SMA(200), and the volume condition is met.
- Short Position: Triggered when SMA1 < SMA2 < SMA3, the closing price is below SMA(200), and the volume condition is met.
Exit Conditions:
- Long Position: Closed when SMA1 < SMA2 < SMA3 and the closing price is above SMA(200).
- Short Position: Closed when SMA1 > SMA2 > SMA3 and the closing price is below SMA(200).
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
I hope the strategy will be helpful, as always, best regards and safe trades
;)
1-BTCUSD
NOVO ALGO - Starry SkyGeneral Description:
This indicator provides the possible buy and sell entry with the estimated risk and its corresponding Stop Loss (SL) value.
It has originally developed for 1-min chart and works the best on this time-frame. It may work on the other time-frames, but its profitability has not been checked. So, I would rather recommend to use and apply it only on 1-min chart.
Novelty of the indicator:
Trading in 1-min chart consists of dealing with so many small swings and price variations which are very local and does not affect the general trend even in the 5-min time frame.
We call these small price variations and swings 'Noise'.
The novelty of the indicator is in a parameter which we call the Noise Level and filtering length.
It has been widely used in the Fluid Dynamics and in the Large Eddy Simulations where small noises of flow is removed by a dynamic filter.
In this indicator, we have tried to incorporate the same idea but in the price trend detection.
For the current version, we have used a less tolerance for noise level which results in much less signals compared to the full capacity of the indicator. It roughly sends out around 10-15% of the total confirmed positions.
How it detects the entry positions
To define the entry point, 5 main properties are considered and checked at 3 main time frames including 1-min, 5-min, and 15-min.
These time-frames are selected based on the fact that the target chart is in 1-min.
The 5 properties evaluated are:
1- Smooth Moving Average
2- Bollinger Band
3- Price Regression
4- Candle Pattern
5- Volume
Detailed Description:
Detect a possible entry by Smooth Moving Average:
- At each time frame, 3 lengths are considered to calculate the price moving average values; i.e. short, medium and long lengths.
- The interaction of these MAs, of course, defines the local trend of the price generally. It also provides an idea about the strength of the trend.
- The information calculated at 1-min time frame triggers the possible buy/sell. However, it waits until getting confirmation from the upper time frame (5-min).
- We use the MAs of 15-min time frame to define the general dominant price trend and stop reverse signals when the trend is fully dominant in one direction.
When a possible entry position is triggered by the MAs, at that very price bar we calculate the noise level.
If the noise level is higher than a certain predefined value, then the signal is rejected. Otherwise the signal gets out.
The threshold we use to define if a signal is noisy or not is normalized so it can be used without any concern at different markets.
We believe the calculations and ideas behind the Noise Level is what makes this indicator unique and practical.
We define the noise level parameter based on the following properties:
1- Smooth Moving Average at upper time frame (basically 15-min):
If a possible signal is against the trend of the upper time-frame, the noise level is increased.
If it is in the direction of the upper time-frame trend, then the noise level is untouched.
As already mentioned, different lengths are used. So, as the length of MA is larger its impact on the noise level is considered higher.
2- Bollinger Band of upper time frames (5-min and 15-min)
We employ bollinger bands to define 4 regions.
1. Above the upper band
2. Between middle and upper band
3. Between Lower and middle bands
4. Below the lower band
Then use these 4 regions along with the candle position and price regression.
For example, if the price regression line and candle position are on the same region of BB, then we assume less possibility for reverse or strong trend.
Consequently, we increase the noise level parameter. On the other hand, if they belong to two different region, we assume more possibility for big price change, and so we lower the noise level.
3- Price Regression
We use average price regression line to filter out very small swings in the price. We have also set a criterion of continuity for the regression line that ensures small price variation and swings are left out and filtered.
This will come with the sot of delay in the confirmation of signal, but we found it very important to remove very small swings of price that, for example, consists of only few bars in 1-min chart.
We have also used the position of the regression line along with the regions defied by BBs to evaluate the strength of a newly detected trend.
As candles will always reach to the regression at some point, if a possible entry is detected and the regression line and candles belong to two different region, we assume a strong price change. But if they belong to the same region, we increase the noise level and will assume that it might be a small swing.
4- Candle Pattern
We assumed several rules for candles shape and prices to define if a price movement is strong or it is just a small swing. For example we expect the price to be increase in the last 2-3 candles if we should call a entry for long position.
These set of self-made rules have been extracted by using the visual inspections of the price movement. This has been done much more advanced for long entry position which has resulted in more long signals by the indicator.
5- Volume
We use volume of trades in 1-min, 5-min, and 15-min to evaluate the strength of the trend. We use both absolute and what we call directional volume! The directional volume is the volume with the sign of the candle. This helps us to know if the reverse trend supported by enough volume or it is just a small swing.
For example, if the directional volume of 1-min can surpass the 5-min directional volume, this indicates to us that the importance of 5-min data and its validity is less. So, more focus will be put on the 1-min volume data and the direction it indicates.
Money Management:
Profit calculation: the profit is calculated based on the user defined leverage (default 100x). The user has the option to change the buy/sell leverages to the desired values.
Risk assessment: The user has the option to adjust the risk of the trades. Then the SL value will be calculated for each trade according to the defined risk value.
If a value of zero is set for the risk, then the indicator will define the local SL of each trade based on the pivot point.
As in 1-min trading, the prices are noise and include several small swings and consequently several minor pivot points, we filtered the pivot points that belong to the super small swings detected by our noise level indicator.
Suggestion
I found it more profitable to make the trades risk-free when their profits passes 10% (with leverage 100x). Then, readjust the TP of trades if the trend is in the direction of the position.
I would recommend to observe the performance of the indicator for a day or two, before actually trading with its signals. This will help to have a better understanding of the leverage and risk you may apply.
BTC outperform atrategy### Code Description
This Pine Script™ code implements a simple trading strategy based on the relative prices of Bitcoin (BTC) on a weekly and a three-month basis. The script plots the weekly and three-month closing prices of Bitcoin on the chart and generates trading signals based on the comparison of these prices. The code can also be applied to Ethereum (ETH) with similar effectiveness.
### Explanation
1. **Inputs and Variables**:
- The user selects the trading symbol (default is "BINANCE:BTCUSDT").
- `weeklyPrice` retrieves the closing price of the selected symbol on a weekly interval.
- `monthlyPrice` retrieves the closing price of the selected symbol on a three-month interval.
2. **Plotting Data**:
- The weekly price is plotted in blue.
- The three-month price is plotted in red.
3. **Trading Conditions**:
- A long position is suggested if the weekly price is greater than the three-month price.
- A short position is suggested if the three-month price is greater than the weekly price.
4. **Strategy Execution**:
- If the long condition is met, the strategy enters a long position.
- If the short condition is met, the strategy enters a short position.
This script works equally well for Ethereum (ETH) by changing the symbol input to "BINANCE:ETHUSDT" or any other desired Ethereum trading pair.
Bitcoin Futures vs. Spot Tri-Frame - Strategy [presentTrading]Prove idea with a backtest is always true for trading.
I developed and open-sourced it as an educational material for crypto traders to understand that the futures and spot spread may be effective but not be as effective as they might think. It serves as an indicator of sentiment rather than a reliable predictor of market trends over certain periods. It is better suited for specific trading environments, which require further research.
█ Introduction and How it is Different
The "Bitcoin Futures vs. Spot Tri-Frame Strategy" utilizes three different timeframes to calculate the Z-Score of the spread between BTC futures and spot prices on Binance and OKX exchanges. The strategy executes long or short trades based on composite Z-Score conditions across the three timeframes.
The spread refers to the difference in price between BTC futures and BTC spot prices, calculated by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges.
BTCUSD 1D L/S Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Spread
The spread is the difference in price between BTC futures and BTC spot prices. The strategy calculates the spread by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges. This spread serves as the primary metric for identifying trading opportunities.
Spread = Weighted Average Futures Price - Weighted Average Spot Price
🔶 Z-Score Calculation
The Z-Score measures how many standard deviations the current spread is from its historical mean. This is calculated for each timeframe as follows:
Spread Mean_tf = SMA(Spread_tf, longTermSMA)
Spread StdDev_tf = STDEV(Spread_tf, longTermSMA)
Z-Score_tf = (Spread_tf - Spread Mean_tf) / Spread StdDev_tf
Local performance
🔶 Composite Entry Conditions
The strategy triggers long and short entries based on composite Z-Score conditions across all three timeframes:
- Long Condition: All three Z-Scores must be greater than the long entry threshold.
Long Condition = (Z-Score_tf1 > zScoreLongEntryThreshold) and (Z-Score_tf2 > zScoreLongEntryThreshold) and (Z-Score_tf3 > zScoreLongEntryThreshold)
- Short Condition: All three Z-Scores must be less than the short entry threshold.
Short Condition = (Z-Score_tf1 < zScoreShortEntryThreshold) and (Z-Score_tf2 < zScoreShortEntryThreshold) and (Z-Score_tf3 < zScoreShortEntryThreshold)
█ Trade Direction
The strategy allows the user to specify the trading direction:
- Long: Only long trades are executed.
- Short: Only short trades are executed.
- Both: Both long and short trades are executed based on the Z-Score conditions.
█ Usage
The strategy can be applied to BTC or Crypto trading on major exchanges like Binance and OKX. By leveraging discrepancies between futures and spot prices, traders can exploit market inefficiencies. This strategy is suitable for traders who prefer a statistical approach and want to diversify their timeframes to validate signals.
█ Default Settings
- Input TF 1 (60 minutes): Sets the first timeframe for Z-Score calculation.
- Input TF 2 (120 minutes): Sets the second timeframe for Z-Score calculation.
- Input TF 3 (180 minutes): Sets the third timeframe for Z-Score calculation.
- Long Entry Z-Score Threshold (3): Defines the threshold above which a long trade is triggered.
- Short Entry Z-Score Threshold (-3): Defines the threshold below which a short trade is triggered.
- Long-Term SMA Period (100): The period used to calculate the simple moving average for the spread.
- Use Hold Days (true): Enables holding trades for a specified number of days.
- Hold Days (5): Number of days to hold the trade before exiting.
- TPSL Condition (None): Defines the conditions for taking profit and stop loss.
- Take Profit (%) (30.0): The percentage at which the trade will take profit.
- Stop Loss (%) (20.0): The percentage at which the trade will stop loss.
By fine-tuning these settings, traders can optimize the strategy to suit their risk tolerance and trading style, enhancing overall performance.
Wall Street Cheat Sheet IndicatorThe Wall Street Cheat Sheet Indicator is a unique tool designed to help traders identify the psychological stages of the market cycle based on the well-known Wall Street Cheat Sheet. This indicator integrates moving averages and RSI to dynamically label market stages, providing clear visual cues on the chart.
Key Features:
Dynamic Stage Identification: The indicator automatically detects and labels market stages such as Disbelief, Hope, Optimism, Belief, Thrill, Euphoria, Complacency, Anxiety, Denial, Panic, Capitulation, Anger, and Depression. These stages are derived from the emotional phases of market participants, helping traders anticipate market movements.
Technical Indicators: The script uses two key technical indicators:
200-day Simple Moving Average (SMA): Helps identify long-term market trends.
50-day Simple Moving Average (SMA): Aids in recognizing medium-term trends.
Relative Strength Index (RSI): Assesses the momentum and potential reversal points based on overbought and oversold conditions.
Clear Visual Labels: The current market stage is displayed directly on the chart, making it easy to spot trends and potential turning points.
Usefulness:
This indicator is not just a simple mashup of existing tools. It uniquely combines the concept of market psychology with practical technical analysis tools (moving averages and RSI). By labeling the psychological stages of the market cycle, it provides traders with a deeper understanding of market sentiment and potential future movements.
How It Works:
Disbelief: Detected when the price is below the 200-day SMA and RSI is in the oversold territory, indicating a potential bottom.
Hope: Triggered when the price crosses above the 50-day SMA, with RSI starting to rise but still below 50, suggesting an early uptrend.
Optimism: Occurs when the price is above the 50-day SMA and RSI is between 50 and 70, indicating a strengthening trend.
Belief: When the price is well above the 50-day SMA and RSI is between 70 and 80, showing strong bullish momentum.
Thrill and Euphoria: Identified when RSI exceeds 80, indicating overbought conditions and potential for a peak.
Complacency to Depression: These stages are identified based on price corrections and drops relative to moving averages and declining RSI values.
Best Practices:
High-Time Frame Focus: This indicator works best on high-time frame charts, specifically the 1-week Bitcoin (BTCUSDT) chart. The longer time frame provides a clearer picture of the overall market cycle and reduces noise.
Trend Confirmation: Use in conjunction with other technical analysis tools such as trendlines, Fibonacci retracement levels, and support/resistance zones for more robust trading strategies.
How to Use:
Add the Indicator: Apply the Wall Street Cheat Sheet Indicator to your TradingView chart.
Analyze Market Stages: Observe the dynamic labels indicating the current stage of the market cycle.
Make Informed Decisions: Use the insights from the indicator to time your entries and exits, aligning your trades with the market sentiment.
This indicator is a valuable tool for traders looking to understand market psychology and make informed trading decisions based on the stages of the market cycle.
Calculus Free Trend Strategy for Crypto & StocksObjective :
The Correlation Channel Trading Strategy is designed to identify potential entry points based on the relationship between price movements and a correlation channel. The strategy aims to capture trends within the channel while managing risk effectively.
Parameters :
Length: Determines the period for calculating moving averages and the true range, influencing the sensitivity of the strategy to price movements.
Multiplier: Adjusts the width of the correlation channel, providing flexibility to adapt to different market conditions.
Inputs :
Asset Symbol: Allows users to specify the financial instrument for analysis.
Timeframe: Defines the timeframe for data aggregation, enabling customization based on trading preferences.
Plot Correlation Channel: Optional input to visualize the correlation channel on the price chart.
Methodology :
Data Acquisition: The strategy fetches OHLC (Open, High, Low, Close) data for the specified asset and timeframe. In this case we use COINBASE:BTCUSD
Calculation of Correlation Channel: It computes the squared values for OHLC data, calculates the average value (x), and then calculates the square root of x to derive the source value. Additionally, it calculates the True Range as the difference between high and low prices.
Moving Averages: The strategy calculates moving averages (MA) for the source value and the True Range, which form the basis for defining the correlation channel.
Upper and Lower Bands: Using the MA and True Range, the strategy computes upper and lower bands of the correlation channel, with the width determined by the multiplier.
Entry Conditions: Long positions are initiated when the price crosses above the upper band, signaling potential overbought conditions. Short positions are initiated when the price crosses below the lower band, indicating potential oversold conditions.
Exit Conditions: Stop-loss mechanisms are incorporated directly into the entry conditions to manage risk. Long positions are exited if the price falls below a predefined stop-loss level, while short positions are exited if the price rises above the stop-loss level.
Strategy Approach: The strategy aims to capitalize on trends within the correlation channel, leveraging systematic entry signals while actively managing risk through stop-loss orders.
Backtest Details : For the purpose of this test I used the entire data available for BTCUSD Coinbase, with 10% of capital allocation and 0.1% comission for entry/exit(0.2% total). Can be also used with other both directly correlated with current settings of BTC or with new ones
Advantages :
Provides a systematic approach to trading based on quantifiable criteria.
Offers flexibility through customizable parameters to adapt to various market conditions.
Integrates risk management through predefined stop-loss mechanisms.
Limitations :
Relies on historical price data and technical indicators, which may not always accurately predict future price movements.
May generate false signals during periods of low volatility or erratic price behavior.
Requires continuous monitoring and adjustment of parameters to maintain effectiveness.
Conclusion :
The Correlation Channel Trading Strategy offers traders a structured framework for identifying potential entry points within a defined price channel. By leveraging moving averages and true range calculations, the strategy aims to capture trends while minimizing risk through stop-loss mechanisms. While no strategy can guarantee success in all market conditions, the Correlation Channel Trading Strategy provides a systematic approach to trading that can enhance decision-making and risk management for traders.
SeasonsThis code represents a seasonal indicator that has a number of unique functions to help traders better understand the market and make informed decisions. Let's take a closer look at each of them:
1. **Chart background shading for each season:** This function allows you to visually see seasonal changes in the market. You'll be able to easily track how the market changes in different seasons, thanks to the color labeling: blue for winter, green for summer, orange for autumn, and yellow for spring.
2. **Vertical markings for each month:** Additional markers on the chart help you orient yourself in time and better understand price dynamics throughout the year. This is especially useful when analyzing seasonal changes and identifying market cyclicality.
3. **Halving timers:** Connecting halving timers on the chart allows you to track important events, such as the reduction of bitcoin mining rewards. Knowing the timing of halving can be a key moment for decision-making and can affect asset prices.
These functions help traders better analyze the market, identify trends and cyclicality, and optimize their trading strategy. Use this indicator in your trading practice to unleash its full potential and reach new heights in your trading career. Don't miss the opportunity to improve your results - apply the seasonal indicator today!
The seasonal indicator is a powerful tool for traders, helping them analyze the market and make informed decisions based on seasonal and cyclical changes. Here are a few reasons why using this indicator can be advantageous:
1. **Identifying seasonal trends:** The seasonal indicator helps identify seasonal trends in the market, such as changes in activity during different seasons or months. For example, some markets may be more volatile or predictable at certain times of the year, and knowing these trends can help in making decisions about entering or exiting positions.
2. **Optimizing trading strategy:** Understanding seasonal changes in the market allows traders to optimize their trading strategy based on the time of year. For example, they may adjust their risk management approaches or choose specific types of trades according to the current season.
3. **Predicting market cyclicality:** The seasonal indicator can also help in predicting market cyclicality and identifying recurring price movement patterns. This enables traders to build their strategies based on past market behavior within specific time intervals.
How to use the seasonal indicator:
1. **Study seasonal changes:** Use the indicator to analyze how the market changes throughout the year. Pay attention to changes in volatility, trading volumes, and price directions depending on the season.
2. **Optimize trading strategy:** Use the data obtained to optimize your trading strategy. Consider entering or exiting positions within specific time intervals to account for seasonal factors.
3. **Predict cyclicality:** Analyze past market behavior using the seasonal indicator to identify cyclicality and recurring patterns. This will help you make more informed decisions based on expected price movements in the future.
Ultimately, using the seasonal indicator allows traders to better understand the market, adapt their strategies, and make more informed decisions based on seasonal and cyclical changes.
All elements on the chart of a particular color will be attributed to the corresponding season. For example, trend lines or levels marked in blue will be associated with winter.
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Winter
Explanation of price movement during the winter season:
1. Number 1 and the blue line denote the maximum price of Bitcoin. Note that they always form at the peaks, which is consistent.
2. Number 2 and the blue line represent the minimum price specifically during the winter period. This is indeed the minimum price and the bottom point in the cycle.
3. Number 3 and the blue line indicate a local maximum after the breakthrough, after which the price starts to rise towards line number 1, which acts as global resistance.
4. Number 4 denotes the last winter cycle before the breakthrough of the global maximum. It should be noted that in 2017, the resistance was not broken immediately - first in spring, and then at the beginning of 2018, the maximum was set, and the asset growth occurred in winter.
Additionally, it's worth noting that numbers 1 form the maximum, numbers 2 form the minimum, and since the trend is descending, I have marked its line in blue.
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Summer
Now let's consider the price behavior chart for the summer. To make the situation clearer, I've left a descending trend in blue on the graph. I reiterate that the elements shown in green on the graph pertain specifically to the summer period.
1. Number 1 on the graph denotes the first summer period! The price during this period remains within a narrow range 90% of the time; however, it's worth noting that impulsive movements can occur at the beginning, middle, or end. Thus, 90% of the time the price is in a low volatility zone, while the remaining percentage is in a high volatility zone.
2. Number 2 on the graph represents the second summer period, where a pattern is observed: the price tends to rise at the beginning of the summer period and fall towards the end. Therefore, I've marked this time with an arc, and there's a pattern to it. It's worth noting that during the period of the descending trend from 2014 to 2016, the situation after the downward trend differs from the situation in 2018 and 2023, when changes in the arrangement of this situation occur after the breakout of the descending trend based on wave analysis and the price of the asset itself.
3. Number 3 represents the third summer period! During this period, the price movement direction is upward and then downward, forming a correction in the upward trend. It should be noted that in this movement, all lows gradually rise, while highs renew all previous local highs of the asset price. This period exhibits increased volatility and impulsive movements, with the asset price mostly staying within a range of minimal volatility, with volatility not exceeding 1-2% on some stretches.
4. Under number 4, the fourth summer period is indicated, which has an overall upward direction. In this period, the movement is aggressively upward. Starting from the first month until the middle of summer, the price moves downward, forming a correction in the upward trend. Then, during the next month, the price moves aggressively upward, renewing price highs. Volatility in this period is anomalously high, resembling a hot July summer.
Additionally, based on the price movement in the summer period, we can assume that fractals are evident here, which we can use to our advantage for profit.
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Shark Trading - We urge all traders to delve deeper into this indicator and incorporate it into their trading practices. It can become an invaluable aid in market analysis and help traders reach new heights in their trading endeavors.
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.
Bitcoin 5A Strategy - Price Upper & Lower Limit@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 March 10, 2023(If you want to check latest data, please contact with author), the 🟠upper limit of the Bitcoin price is $132,453, 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, and the 🟠upper limit of the Bitcoin price will exceed $130,000. The closing price of Bitcoin on March 10, 2024, was $68,515, with an expected increase of 90%.
🏃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 accuracy validation: How to judge the accuracy of the Bitcoin price model?
The accuracy of the model is represented by the coefficient of determination R square, 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 accuracy 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 square 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 reliability verification: How to affirm the reliability of the Bitcoin price model when new data is available?
Model reliability 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 reliability 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 accuracy 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 reliable. 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 reliability 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.
Trend Deviation strategy - BTC [IkkeOmar]Intro:
This is an example if anyone needs a push to get started with making strategies in pine script. This is an example on BTC, obviously it isn't a good strategy, and I wouldn't share my own good strategies because of alpha decay.
This strategy integrates several technical indicators to determine market trends and potential trade setups. These indicators include:
Directional Movement Index (DMI)
Bollinger Bands (BB)
Schaff Trend Cycle (STC)
Moving Average Convergence Divergence (MACD)
Momentum Indicator
Aroon Indicator
Supertrend Indicator
Relative Strength Index (RSI)
Exponential Moving Average (EMA)
Volume Weighted Average Price (VWAP)
It's crucial for you guys to understand the strengths and weaknesses of each indicator and identify synergies between them to improve the strategy's effectiveness.
Indicator Settings:
DMI (Directional Movement Index):
Length: This parameter determines the number of bars used in calculating the DMI. A higher length may provide smoother results but might lag behind the actual price action.
Bollinger Bands:
Length: This parameter specifies the number of bars used to calculate the moving average for the Bollinger Bands. A longer length results in a smoother average but might lag behind the price action.
Multiplier: The multiplier determines the width of the Bollinger Bands. It scales the standard deviation of the price data. A higher multiplier leads to wider bands, indicating increased volatility, while a lower multiplier results in narrower bands, suggesting decreased volatility.
Schaff Trend Cycle (STC):
Length: This parameter defines the length of the STC calculation. A longer length may result in smoother but slower-moving signals.
Fast Length: Specifies the length of the fast moving average component in the STC calculation.
Slow Length: Specifies the length of the slow moving average component in the STC calculation.
MACD (Moving Average Convergence Divergence):
Fast Length: Determines the number of bars used to calculate the fast EMA (Exponential Moving Average) in the MACD.
Slow Length: Specifies the number of bars used to calculate the slow EMA in the MACD.
Signal Length: Defines the number of bars used to calculate the signal line, which is typically an EMA of the MACD line.
Momentum Indicator:
Length: This parameter sets the number of bars over which momentum is calculated. A longer length may provide smoother momentum readings but might lag behind significant price changes.
Aroon Indicator:
Length: Specifies the number of bars over which the Aroon indicator calculates its values. A longer length may result in smoother Aroon readings but might lag behind significant market movements.
Supertrend Indicator:
Trendline Length: Determines the length of the period used in the Supertrend calculation. A longer length results in a smoother trendline but might lag behind recent price changes.
Trendline Factor: Specifies the multiplier used in calculating the trendline. It affects the sensitivity of the indicator to price changes.
RSI (Relative Strength Index):
Length: This parameter sets the number of bars over which RSI calculates its values. A longer length may result in smoother RSI readings but might lag behind significant price changes.
EMA (Exponential Moving Average):
Fast EMA: Specifies the number of bars used to calculate the fast EMA. A shorter period results in a more responsive EMA to recent price changes.
Slow EMA: Determines the number of bars used to calculate the slow EMA. A longer period results in a smoother EMA but might lag behind recent price changes.
VWAP (Volume Weighted Average Price):
Default settings are typically used for VWAP calculations, which consider the volume traded at each price level over a specific period. This indicator provides insights into the average price weighted by trading volume.
backtest range and rules:
You can specify the start date for backtesting purposes.
You can can select the desired trade direction: Long, Short, or Both.
Entry and Exit Conditions:
LONG:
DMI Cross Up: The Directional Movement Index (DMI) indicates a bullish trend when the positive directional movement (+DI) crosses above the negative directional movement (-DI).
Bollinger Bands (BB): The price is below the upper Bollinger Band, indicating a potential reversal from the upper band.
Momentum Indicator: Momentum is positive, suggesting increasing buying pressure.
MACD (Moving Average Convergence Divergence): The MACD line is above the signal line, indicating bullish momentum.
Supertrend Indicator: The Supertrend indicator signals an uptrend.
Schaff Trend Cycle (STC): The STC indicates a bullish trend.
Aroon Indicator: The Aroon indicator signals a bullish trend or crossover.
When all these conditions are met simultaneously, the strategy considers it a favorable opportunity to enter a long trade.
SHORT:
DMI Cross Down: The Directional Movement Index (DMI) indicates a bearish trend when the negative directional movement (-DI) crosses above the positive directional movement (+DI).
Bollinger Bands (BB): The price is above the lower Bollinger Band, suggesting a potential reversal from the lower band.
Momentum Indicator: Momentum is negative, indicating increasing selling pressure.
MACD (Moving Average Convergence Divergence): The MACD line is below the signal line, signaling bearish momentum.
Supertrend Indicator: The Supertrend indicator signals a downtrend.
Schaff Trend Cycle (STC): The STC indicates a bearish trend.
Aroon Indicator: The Aroon indicator signals a bearish trend or crossover.
When all these conditions align, the strategy considers it an opportune moment to enter a short trade.
Disclaimer:
THIS ISN'T AN OPTIMAL STRATEGY AT ALL! It was just an old project from when I started learning pine script!
The backtest doesn't promise the same results in the future, always do both in-sample and out-of-sample testing when backtesting a strategy. And make sure you forward test it as well before implementing it!
Furthermore this strategy uses both trend and mean-reversion systems, that is usually a no-go if you want to build robust trend systems .
Don't hesitate to comment if you have any questions or if you have some good notes for a beginner.
Dynamic Momentum GaugeOverview
The Dynamic Momentum Gauge is an indicator designed to provide information and insights into the trend and momentum of a financial asset. While this indicator is not directional , it helps you know when there will be a trend, big move, or when momentum will have a run, and when you should take profits.
How It Works
This indicator calculates momentum and then removes the negative values to focus instead on when the big trend could likely happen and when it could end, or when you should enter a trade based on momentum or exit. Traders can basically use this indicator to time their market entries or exits, and align their strategies with momentum dynamics.
How To Use
As previously mentioned, this is not a directional indicator but more like a timing indicator. This indicator helps you find when the trend moves, and big moves in the markets will occur and its possibly best to exit the trades. For example, if you decide to enter a long trade if the Dynamic Momentum Gauge value is at an extreme low and another momentum indicator that you use has conditions that you would consider to long with, then this indicator is basically telling you that there isn't more space for the momentum to squeeze any longer, can only really expand from that point or stay where it currently is, but this is also a mean reverting process so it does tend to go back up from the low point.
Settings:
Length: This is the length of the momentum, by default its at 100.
Normalization Length: Length of the Normalization which ensures the the values fall within a consistent range.
Volume Spike IndicatorHello dear traders,
Today we're discussing an indicator I've coded: the Volume Spike Indicator (VSI).
The indicator isn't a groundbreaking invention and certainly not a novelty. Nevertheless, I haven't seen this version of the indicator on TradingView before, so I'd like to introduce it.
1. The Origin of the Idea:
We're all familiar with volume charts: A volume chart visually represents the trading activity for a specific asset over a certain period, indicating the total number of shares or contracts traded.
We also know that volume spikes can significantly impact the market. A volume spike represents an extreme anomaly, a day, week, or month with an extraordinary amount of trading. However, recognizing these spikes in practice isn't always straightforward. What constitutes high volume? How do we define and identify it? The answers to these questions aren't easy.
It's commonly said that a volume spike could be identified if the volume is 25% more than the average of the two weeks prior, but how do you measure this 25%? It's not always easy to calculate, especially in real-time.
This challenge led me to develop the concept into an indicator.
How Does It Work?
Imagine being able to "feel" the market's energy like a surfer feels the ocean. The VSI does something similar by examining trading volume and comparing it to what has been typical over the past few weeks. Here's a quick look at the magic behind it:
Step 1: Establishing the Baseline: We start by establishing a baseline, i.e., the average trading volume over a given period. Let's use the last 10 days as the default setting. We choose 10 days because, in the traditional stock market, 10 days represent two weeks if you subtract weekends. This gives us a fixed line to compare against.
Step 2: Recognizing Peaks: Next, we look for days when the trading volume significantly exceeds this average. The size of the jump is where you have a say. You can set a threshold, such as 25%, to define what you consider a volume spike.
Step 3: The Calculation: This is where the math comes into play. We calculate the percentage change in today's volume compared to the average volume of the last 10 days. For example, if today's volume is 30% above the average and you've set your threshold at 25%, the VSI will recognize this as a spike.
Step 4: Visual Cue: These spikes are then plotted on a graph, with each spike represented as a bar. The height of the bar indicates the spike's percentage size, so you can see at a glance how significant a spike is.
Step 5: Intuitive Color Coding: For quick analysis, the VSI employs a color-coding system. Exceptionally high peaks, such as those exceeding a 100% increase, are highlighted in blue to emphasize their importance. Other peaks are shown in red, creating a visual hierarchy for quick volume data interpretation.
Why This Matters:
Identifying these spikes can help pinpoint the beginning or end of a trend. The idea is that when trading peaks at a certain level, there might be no more buyers or sellers willing to engage at that price level. Volume peaks, and a reversal is likely imminent. It's a simple yet effective concept. Therefore, it's crucial to use this indicator in the context of the trend, as not every spike carries the same significance.
Customizable:
The beauty of the VSI lies in its flexibility. Trading futures? You might want to adjust the averaging period to 14 days to better suit your market. You have full control over the settings to tailor them to your trading style.
Interpreting the Figures:
A positive percentage indicates a volume spike above the average – the higher the percentage, the more significant the spike.
If the percentage exceeds a certain threshold (which you can set, e.g., 25%), it signals a volume spike, indicating increased market activity that could precede significant price movement.
What makes the VSI genuinely adaptable is your ability to tweak the parameters to suit your needs.
Are you trading in a volatile market? Extend the SMA period to smooth out the noise. Trading in a 24-hour market? Adjust the length of your SMA. Seeking finer details? Shorten it. The VSI is yours to adapt to your trading strategy.
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As we wrap up this introduction to the Volume Spike Indicator, I hope you're as excited about its potential as I am. This tool, born out of curiosity and a desire for clarity in the vast ocean of market data, is designed to be your ally in navigating the waves of trading activity.
Remember, the true power of the VSI lies not just in its ability to highlight significant volume spikes, but in its adaptability to your unique trading style and needs. Whether you're charting courses through the tumultuous seas of day trading or navigating the broader currents of long-term investments, the VSI is here to offer insights and guidance.
I encourage you to experiment with it, customize it, and see how it can enhance your trading strategy. And as you do, remember that every tool, no matter how powerful, is just one piece of the puzzle. Combine the VSI with your knowledge, experience, and intuition to make informed and strategic trading decisions.
Thank you for taking the time to explore the Volume Spike Indicator with me.
Best Regards,
Karim Subhieh
BTC ETF VolumesVolume
This script plots the trading volume of all BTC spot ETFs as well as the aggregate volume. Works on any chart and any timeframe.
Indicators
The volume of every ETF is plotted in a different color, with the total column adding up to the aggregate volume.
If you have price and indicator labels enabled you will also see individual ETF volume on your price scale on the right hand side.
If more BTC ETFs get launched I will add them.
Bitcoin/Hash Rate Oscillator & MAWhat it does:
Finds the ratio of BTC price to the Hash Rate with an additional MA applied to find changes in volatility with relative context. Best used as a two lines cross indicator.
When the ratio of price to hashrate increases, it may be a sign miners cannot or will not sell as much.
When the ratio decreases, it may indicate miners have more capability and/or incentive to sell.
How it works:
The indicator uses a MA applied to the hashrate(first MA input), then finds the difference between it and the actual hash rate. Then it finds the STD of that to create an oscillating value. BTC is divided by said value. Then a second MA is applied to that ratio(second MA input)
Session Breakout Scalper Trading BotHi Traders !
Introduction:
I have recently been exploring the world of automated algorithmic trading (as I prefer more objective trading strategies over subjective technical analysis (TA)) and would like to share one of my automation compatible (PineConnecter compatible) scripts “Session Breakout Scalper”.
The strategy is really simple and is based on time conditional breakouts although has more ”relatively” advanced optional features such as the regime indicators (Regime Filters) that attempt to filter out noise by adding more confluence states and the ATR multiple SL that takes into account volatility to mitigate the down side risk of the trade.
What is Algorthmic Trading:
Firstly what is algorithmic trading? Algorithmic trading also known as algo-trading, is a method of using computer programs (in this case pine script) to execute trades based on predetermined rules and instructions (this trading strategy). It's like having a robot trader who follows a strict set of commands to buy and sell assets automatically, without any human intervention.
Important Note:
For Algorithmic trading the strategy will require you having an essential TV subscription at the minimum (so that you can set alerts) plus a PineConnecter subscription (scroll down to the .”How does the strategy send signals” headings to read more)
The Strategy Explained:
Is the Time input true ? (this can be changed by toggling times under the “TRADE MEDIAN TIMES” group for user inputs).
Given the above is true the strategy waits x bars after the session and then calculates the highest high (HH) to lowest low (LL) range. For this box to form, the user defined amount of bars must print after the session. The box is symmetrical meaning the HH and LL are calculated over a lookback that is equal to the sum of user defined bars before and after the session (+ 1).
The Strategy then simultaneously defines the HH as the buy level (green line) and the LL as the sell level (red line). note the strategy will set stop orders at these levels respectively.
Enter a buy if price action crosses above the HH, and then cancel the sell order type (The opposite is true for a stop order).
If the momentum based regime filters are true the strategy will check for the regime / regimes to be true, if the regime if false the strategy will exit the current trade, as the regime filter has predicted a slowing / reversal of momentum.
The image below shows the strategy executing these trading rules ( Regime filters, "Trades on chart", "Signal & Label" and "Quantity" have been omitted. "Strategy label plots" has been switched to true)
Other Strategy Rules:
If a new session (time session which is user defined) is true (blue vertical line) and the strategy is currently still in a trade it will exit that trade immediately.
It is possible to also set a range of percentage gain per day that the strategy will try to acquire, if at any point the strategy’s profit is within the percentage range then the position / trade will be exited immediately (This can be changed in the “PERCENT DAY GAIN” group for user inputs)
Stops and Targets:
The strategy has either static (fixed) or variable SL options. TP however is only static. The “STRAT TP & TP” group of user inputs is responsible for the SL and TP values (quoted in pips). Note once the ATR stop is set to true the SL values in the above group no longer have any affect on the SL as expected.
What are the Regime Filters:
The Larry Williams Large Trade Index (LWLTI): The Larry Williams Large Trade Index (LWTI) is a momentum-based technical indicator developed by iconic trader Larry Williams. It identifies potential entries and exits for trades by gauging market sentiment, particularly the buying and selling pressure from large market players. Here's a breakdown of the LWTI:
LWLTI components and their interpretation:
Oscillator: It oscillates between 0 and 100, with 50 acting as the neutral line.
Sentiment Meter: Values above 75 suggest a bearish market dominated by large selling, while readings below 25 indicate a bullish market with strong buying from large players.
Trend Confirmation: Crossing above 75 during an uptrend and below 25 during a downtrend confirms the trend's continuation.
The Andean Oscillator (AO) : The Andean Oscillator is a trend and momentum based indicator designed to measure the degree of variations within individual uptrends and downtrends in the prices.
Regime Filter States:
In trading, a regime filter is a tool used to identify the current state or "regime" of the market.
These Regime filters are integrated within the trading strategy to attempt to lower risk (equity volatility and/or draw down). The regime filters have different states for each market order type (buy and sell). When the regime filters are set to true, if these regime states fail to be true the trade is exited immediately.
For Buy Trades:
LWLTI positive momentum state: Quotient of the lagged trailing difference and the ATR > 50
AO positive momentum state: Bull line > Bear line (signal line is omitted)
For Sell Trades:
LWLTI negative momentum stat: Quotient of the lagged trailing difference and the ATR < 50
AO negative momentum state: Bull line < Bear line (signal line is omitted)
How does the Strategy Send Signals:
The strategy triggers a TV alert (you will neet to set a alert first), TV then sends a HTTP request to the automation software (PineConnecter) which receives the request and then communicates to an MT4/5 EA to automate the trading strategy.
For the strategy to send signals you must have the following
At least a TV essential subscription
This Script added to your chart
A PineConnecter account, which is paid and not free. This will provide you with the expert advisor that executes trades based on these strategies signals.
For more detailed information on the automation process I would recommend you read the PineConnecter documentation and FAQ page.
Dashboard:
This Dashboard (top right by defualt) lists some simple trading statistics and also shows when a trade is live.
Important Notice:
- USE THIS STRATEGY AT YOUR OWN RISK AND ALWAYS DO YOUR OWN RESEARCH & MANUAL BACKTESTING !
- THE STRATEGY WILL NOT EXHIBIT THE BACKTEST PERFORMANCE SEEN BELOW IN ALL MARKETS !
[ETNX] BTC CME OI L/SOVERVIEW
This indicator displays how many traders have Long and Short positions opened on CME Bitcoin Futures and Options. The data is provided from the CFTC Commitments of Traders Reports. These reports are given weekly. Therefore, this indicator works better on weekly timeframes.
The COT reports are separated into 5 categories:
Dealer/Intermediary - These participants are typically described as the “sell side” of the market. Though they may not predominately sell futures, they design and sell various financial assets to clients. They tend to have matched books or offset their risk across markets and clients. Futures contracts are part of the pricing and balancing of risk associated with the products they sell and their activities. These include large banks (U.S. and non-U.S.) and dealers in securities, swaps, and other derivatives.
Asset Manager/Institutional - These are institutional investors, including pension funds, endowments, insurance companies, mutual funds, and portfolio/investment managers whose clients are predominantly institutional.
Leveraged Funds - These are typically hedge funds and various types of money managers, including registered commodity trading advisors (CTAs); registered commodity pool operators (CPOs), or unregistered funds identified by CFTC. The strategies may involve taking outright positions or arbitrage within and across markets. The traders may be engaged in managing and conducting proprietary futures trading and trading on behalf of speculative clients.
Other Reportables - Reportable traders not placed into one of the first three categories are placed into the “other reportables” category. The traders in this category mostly use markets to hedge business risk, whether that risk is related to foreign exchange, equities, or interest rates. This category includes corporate treasuries, central banks, smaller banks, mortgage originators, credit unions and any other reportable traders not assigned to the other three categories.
Non Reportable
INPUT DISPLAY
The Open Interest can be displayed in three ways:
Contracts - How many contracts are opened on CME
BTC - How many BTC the contracts worth
Billions USD - How much is worth in USD based on the CME BTC Price
The Open Interest is calculated for:
Futures - The Futures Short and Long Positions Opened
Futures & Options - The Futures & Options Short and Long Positions Opened
Hull WavesThe Hull Waves indicator is based on the Hull Moving Averages (HMA), which are special moving averages that stand out for their ability to filter out market noise and offer a clearer view of price trends. Compared to traditional moving averages, HMAs are more responsive yet smoother, allowing traders to capture significant price movements without getting overwhelmed by short-term fluctuations.
The HMAs integrated into Hull Waves provide two distinct perspectives on the price trend:
8-period HMA: This short-term HMA is extremely reactive and closely follows price changes. It is ideal for capturing short-term trading signals while the medium-term 21-period HMA offers a more balanced view of price trends and identifies medium-term trends.
By crossing HMAs, traders can efficiently identify trend reversal points or strong market continuations.
Another feature of the indicator is the “fan” of dynamic lines, which acts as a visual float for price candles, allowing traders to quickly evaluate trading opportunities.
The "fan" or float of dynamic lines represents a visual representation of the candle's price movements. These lines extend from the start point to the end point, like an open fan. This visual approach makes the market dynamics immediately evident.
Strategy:
Long Entry Signal (Buy):
When the Hull Waves range shows a series of upward sloping lines and the Hull Moving Averages (e.g. 8-period HMA) crosses the 21-period HMA upwards, it is a long entry signal.
Confirmation of the signal can come from an increase in trader volume or other supporting indicators.
Place a buy order at the next closing price.
Short Entry Signal (Sell):
When the Hull Waves range shows a series of downward sloping lines and the Hull Moving Averages (e.g. 8-period HMA) crosses the 21-period HMA downward, it is a short entry signal.
Confirm the signal with an increase in trader volume or other relevant indicators.
Place a sell order at the next closing price.
Exit Signal (Closing a Position):
To close a long position, wait for a signal reversal, such as the Hull Moving Averages crossing downwards or a change in the Hull Waves range.
To close a short position, wait for a signal reversal, such as the Hull Moving Averages crossing higher or a change in the Hull Waves range.
3kilos BTC 15mThe "3kilos BTC 15m" is a comprehensive trading strategy designed to work on a 15-minute timeframe for Bitcoin (BTC) or other cryptocurrencies. This strategy combines multiple indicators, including Triple Exponential Moving Averages (TEMA), Average True Range (ATR), and Heikin-Ashi candlesticks, to generate buy and sell signals. It also incorporates risk management features like take profit and stop loss.
Indicators
Triple Exponential Moving Averages (TEMA): Three TEMA lines are used with different lengths and sources:
Short TEMA (Red) based on highs
Long TEMA 1 (Blue) based on lows
Long TEMA 2 (Green) based on closing prices
Average True Range (ATR): Custom ATR calculation with EMA smoothing is used for volatility measurement.
Supertrend: Calculated using ATR and a multiplier to determine the trend direction.
Simple Moving Average (SMA): Applied to the short TEMA to smooth out its values.
Heikin-Ashi Close: Used for additional trend confirmation.
Entry & Exit Conditions
Long Entry: Triggered when the short TEMA is above both long TEMA lines, the Supertrend is bullish, the short TEMA is above its SMA, and the Heikin-Ashi close is higher than the previous close.
Short Entry: Triggered when the short TEMA is below both long TEMA lines, the Supertrend is bearish, the short TEMA is below its SMA, and the Heikin-Ashi close is lower than the previous close.
Take Profit and Stop Loss: Both are calculated as a percentage of the entry price, and they are set for both long and short positions.
Risk Management
Take Profit: Set at 1% above the entry price for long positions and 1% below for short positions.
Stop Loss: Set at 3% below the entry price for long positions and 3% above for short positions.
Commission and Pyramiding
Commission: A 0.07% commission is accounted for in the strategy.
Pyramiding: The strategy does not allow pyramiding.
Note
This strategy is designed for educational purposes and should not be considered as financial advice. Always do your own research and consider consulting a financial advisor before engaging in trading.
MAX_MIN_V1
Another simple indicator, maximum, minimum and average values. The point of imbalance in the price of an asset is sought.
It is used for any temporality and in almost any asset.
You can configure the visibility of the different elements.
Blackrock Spot ETF Premium BTCUSD (COINBASE) V1I created an indicator that takes the spot BTC/USD pair from major exchanges and compares it to the Spot BTC/USD pair on Coinbase that institutions will use for their Spot ETFs.
Blackrock Spot ETF Premium BTCUSD (COINBASE)
I suspect we will see a new "Kimchi Premium" where the Spot ETF pressures from institutions will raise the Coinbase Bitcoin price by a factor of 10-50% premium to the other exchanges.
Naturally excess coins from other exchanges will flow into Coinbase to capture this.
This indicator should be good for some time until one of the other exchanges delist or stop using BTCUSD "spot" If it breaks it I will update it if I remember.
FederalXBT,
Realized Profit & Loss [BigBeluga]The Realized Loss & Profit indicator aims to find potential dips and tops in price by utilizing the security function syminfo.basecurrency + "_LOSSESADDRESSES".
The primary objective of this indicator is to present an average, favorable buying/selling opportunity based on the number of people currently in profit or loss.
The script takes into consideration the syminfo.basecurrency, so it should automatically adapt to the current coin.
🔶 USAGE
Users have the option to enable the display of either Loss or Profit, depending on their preferred visualization.
Examples of displaying Losses:
Example of displaying Profits:
🔶 CONCEPTS
The concept aims to assign a score to the data in the ticker representing the realized losses. This score will provide users with an average of buying/selling points that are better to the typical investor.
🔶 SETTINGS
Users have complete control over the script settings.
🔹 Calculation
• Profit: Display people in profit on an average of the selected length.
• Loss: Display people in loss on an average of the selected length.
🔹 Candle coloring
• True: Color the candle when data is above the threshold.
• False: Do not color the candle.
🔹 Levels
- Set the level of a specific threshold.
• Low: Low losses (green).
• Normal: Low normal (yellow).
• Medium: Low medium (orange).
• High: Low high (red).
🔹 Z-score Length: Length of the z-score moving window.
🔹 Threshold: Filter out non-significant values.
🔹 Histogram width: Width of the histogram.
🔹 Colors: Modify the colors of the displayed data.
🔶 LIMITATIONS
• Since the ticker from which we obtain data works only on the daily timeframe, we are
restricted to displaying data solely from the 1D timeframe.
• If the coin does not have any realized loss data, we can't use this script.
Extreme Reversal SignalThe Extreme Reversal Signal is designed to signal potential pivot points when the price of an asset becomes extremely overbought or oversold. Extreme conditions typically signal a brief or extensive price reversal, offering valuable entry or exit points. It's important to note that this indicator may produce multiple signals, making it essential to corroborate these signals with other forms of analysis to determine their validity. While the default settings provide valuable insights, it might be beneficial to experiment with different configurations to ensure the indicator's efficacy.
Two primary conditions define extremely overbought and oversold states. The first condition is that the price must deviate by two standard deviations from the 20-day Simple Moving Average (SMA). The second condition is that the 3-day SMA of the 14-day Stochastic Oscillator (STO) derived from the 14-day Relative Strength Index (RSI) is above or below the upper or lower limit.
Oversold states arise when the first condition is met and the 3-day SMA of the 14-day Stochastic RSI falls below the lower limit, suggesting a buy signal. These are visually represented by green triangles below the price bars. Overbought states arise when the first condition is met and the 3-day SMA of the 14-day Stochastic RSI rises above the upper limit, suggesting a sell signal. These are visually represented by red triangles above the price bars. It's also possible to set up automated alerts to get notifications when either of these two conditions is met to avoid missing out.
While this indicator has traditionally identified overbought and oversold conditions in various different assets, past performance does not guarantee future results. Therefore, it is advisable to supplement this indicator with other technical tools. For instance, trend indicators can greatly improve the decision-making process when planning for entries and exit points.
EMA Power BandsHello!
Today, I am delighted to introduce you to the "EMA Power Bands" indicator, designed to assist in identifying buying and selling points for assets moving in the markets.
Key Features of the Indicator:
EMA Bands: "EMA Power Bands" utilizes Exponential Moving Average (EMA) to create trend lines. These bands automatically expand or contract based on the price trend, adapting to market conditions.
ATR-Based Volatility: The indicator measures price volatility using the Average True Range (ATR) indicator, adjusting the width of the EMA bands accordingly. As a result, wider bands form during periods of increased volatility, while they narrow during lower volatility.
RSI-Based Buy-Sell Signals: "EMA Power Bands" uses the Relative Strength Index (RSI) to identify overbought and oversold zones. Entering the overbought zone generates a sell signal, while entering the oversold zone produces a buy signal.
Trend Direction Identification: The indicator assists in determining the price trend direction by analyzing the slope of the EMA bands. This allows you to identify periods of uptrends and downtrends.
Visualization of Buy-Sell Signals: "EMA Power Bands" visually marks the buy and sell signals:
- When RSI enters the overbought zone, it displays a sell signal (🪫).
- When RSI enters the oversold zone, it indicates a buy signal (🔋).
- When a candle closes above the emaup line, it displays a bearish signal (🔨).
- When a candle closes below the emadw line, it indicates a bullish signal (🚀).
By using the "EMA Power Bands" (EMA Güç Bantları) indicator, especially in trend-following strategies and periods of volatility, you can make more informed and disciplined trading decisions. However, I recommend using it in conjunction with other technical analysis tools and fundamental data.
*You can also use it with CCI as an example.
With this indicator, you can identify potential trend reversals in advance and strengthen your risk management strategies.
So, go ahead and try the "EMA Power Bands" (EMA Güç Bantları) indicator to enhance your technical analysis skills and make more informed trading decisions!
