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Gap Trading Strategy: CME BitcoinI created a strategy which finds gaps on CME Futures market for Bitcoin, BTC1! and opens a long or short position on the crypto exchange depending on what kind of gap was found (up or down) on CME. Up gap: today open price > previous day high price Down gap: today open price < previous day low price Two lines below the main chart show when gaps appear. The green line represents the size of up gaps, when it crosses zero it gives a long signal. The red line - represents the size of down gaps, when it crosses zero - short signal. Instead of having to look between multiple charts, this simply overlays the past weeks open and close should a gap appear. Usage: Strategy flips long / short depending of indicator signals. It could cut the position by stop loss or take profit. Features: ability to set stop loss and take profit in % ability to set delta for gaps (if you want to filter small ones) ability to choose strategy type. Standart: Long Up/Short Down || Inverse: Short Up/Long Down. Backtesting: Backtested on BTCPERP ( FTX ) and XBTUSD (Bitmex). It shows a low drawdown, a small number of trades and 50% of profit for the 2020. For example, I set high delta for down gaps to filter small ones Strategy doesn't repaint.
Strategi Pine Script®
oleh Bitduke
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.
Strategi Pine Script®
oleh lilibtc
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.
Strategi Pine Script®
oleh lilibtc
BitcoinNinjas NINJASIGNALS V4 (Strategy)BitcoinNinjas NINJASIGNALS V4 (Strategy) (for Cryptocurrencies, Forex, GunBot, ProfitTrailer, Autoview, CryptoHopper, manual or automated trading, and more) This is version 4 of our Ninja Signals trading script, with accompanying backtesting strategy. BitcoinNinjas NINJASIGNALS V4 (Script) •Allows users to easily set automated buy/long and sell/short alerts on TradingView for use with automatic and manual trading of cryptocurrencies, Forex securities, and more (alerts are compatible with automatic trading software such as GunBot, ProfitTrailer, Autoview, CryptoHopper, and more). •Synthesizes many powerful indicators [e.g., Relative Strength Index (RSI), Stochastic RSI, Money Flow Index (MFI), Moving Average Convergence Divergence (MACD), etc.) into one powerful script to generate very precise buy and sell signals in virtually all market conditions. •Features user-defined adjustable calibration settings, allowing traders to customize the script to fit any currency / security on any exchange available through TradingView.com, simply by adjusting settings. •Buy/Long arrows, Sell/Short arrows, & EMA trendline can be customized or hidden, if desired. •Complete with backtesting strategy version of script which allows users to test various trading strategies based on the alerts the script generates (see information and screenshots below). •Backtesting strategy features a user-defined adjustable date range, so traders can estimate performance of the script over specific periods of time, such as the last week, month, or year. •Script and backtesting strategy feature many user-adjustable settings including stop loss and take profit alerts, an ‘only sell for profit’ option (Gunbot-specific), many different buy and sell filters, and more. Simply adjust the script settings and the backtesting results will automatically refresh. •Backtesting strategy allows for pyramid buying to test various average down / dollar cost average trading strategies. Simply adjust the number of pyramid buys and the quantity of each buy. •Fully compatible with margin and futures trading for any currency / security on TradingView.com. DISCLAIMER: By using our BitcoinNinjas ‘Ninja Signals’ planning script, you agree to the BitcoinNinjas 'Terms of Use'. No sharing, copying, reselling, modifying, or any other forms of use are authorized for our documents, script / strategy, and the information published with them. This informational planning script / strategy is strictly for individual use and educational purposes only. This is not financial or investment advice. Investments are always made at your own risk and are based on your personal judgement. BitcoinNinjas is not responsible for any losses you may incur. Please invest wisely.
Strategi Pine Script®
oleh Bitcoin_Ninjas
32
Bitcoin Ninjas 'Ninja Signals' Script [NLM Edition] (STRATEGY)Bitcoin Ninjas 'Ninja Signals' Buy/Sell Alerts Script & Backtesting Strategy (NLM Edition) Private script for research and development purposes based on our popular 'Ninja Signals' script. Public versions of our 'Ninja Signals' script are available below: 'Ninja Signals' v1.0 (SCRIPT) 'Ninja Signals' v1.0 (STRATEGY) 'Ninja Signals' v2.0 (SCRIPT) 'Ninja Signals' v2.0 (STRATEGY)
Strategi Pine Script®
oleh Bitcoin_Ninjas
Bitcoin Exponential Profit Strategy### Strategy Description: The **Bitcoin Trading Strategy** is an **Exponential Moving Average (EMA) crossover strategy** designed to identify bullish trends for Bitcoin. 1. **Indicators**: - **Fast EMA (default 9 periods)**: Represents the short-term trend. - **Slow EMA (default 21 periods)**: Represents the longer-term trend. 2. **Entry Condition**: - A **bullish crossover** occurs when the Fast EMA crosses above the Slow EMA. - The strategy enters a **long position** with a user-defined order size (default 0.01 BTC). 3. **Exit Conditions**: - **Take Profit**: Closes the position when the profit target is reached (default $100). - **Stop Loss**: Closes the position when the price drops below the stop loss level (default $50). - **Bearish Crossunder**: Closes the position when the Fast EMA crosses below the Slow EMA. 4. **Visual Signals**: - **BUY signals**: Displayed when a bullish crossover occurs. - **SELL signals**: Displayed when a bearish crossunder occurs. This strategy is optimized for trend-following behavior, ensuring positions are aligned with upward-moving trends while managing risk through clear stop-loss and take-profit levels.
Strategi Pine Script®
oleh kfirporter
Bitcoin 30m Swing Trader Long/Short StrategyIntro I want to share the results of my passionate hobby and the unstoppable chase for a profitable automated trading strategy. It has been created with the intention of trading only Bitcoin. Altcoins are not interesting for me, as I have discovered lots of issues with finding the right parameter values for experiencing a good performance. As altcoins typically follow the trend of bitcoin and characteristically have a high volatility that may cause stop-hunts, I decided to not over complicate this project. I was just aiming for a profitable trading strategy with an acceptable drawdown and enough confidence by a statistically significant number of trades beside a wide backtesting timespan (credits going out to TradingView: Deep Backtesting). Total time spent on this is approximately 2 years. Indicators used RSI: Used for entries and trend reversal spots MACD: Used for entry and exit optimiziation ATR: Used for dynamic offsets in trend definition indicator Custom trend indicator: Self-made indicator, based on simple price action of higher timeframes using pivot points to find support and resistance zones that have formerly been created Strategy parameters I have reduced the total parameters used to just a few. It took lots of working hours to find appropriate values along the trading algorithm and I don’t want to overcomplicate it to you. This strategy is for those, who have been looking for a working strategy. No DIY kit. Feel free to adapt Take profit or stop loss targets. But it’s not recommended to do so. How it works Entries: I started with a kind of template that I have been using for strategies for a long time. This includes how to find the right Entries during a trend as well as spotting trend reverse opportunities. Here I combine simple indicators like RSI and MACD beside necessary trend conditions. If a target RSI Value is hit, it will enter a trade, after MACD histogram has stopped to fall/rise. Depends on long/short. While we are in a trade and trend reversed, it waits for a specific RSI target level to be hit, to reverse the trade. As simple as it is, it closes the open one and starts a trade in other direction. Micro trend: It starts to get more interesting when it comes to trend recognition, as it forms the core of the strategy and discovering appropriate values for it has been very hard. The final trend variable is defined by the responses over higher timeframes of my self-made trend indicator. Executed on the current timeframe, the trend indicator is quite interesting. But for a automated trading strategy it is necessary to deviate trading instructions from higher timeframes trends. Macro trend: The same process that happens for micro trend is also applied with much higher timeframes, like 3D or weekly. The basic assumption is, that if we are in a bull or bear run, where retail investors are flooding the markets, we are increasing our take profit targets respectively. This way we can catch bigger moves in bigger trends. Exits: Closing a trade generally happens when a TP target (in %) is hit, or the SL (in %) is hit. The strategy has a special treatment with SL’s. After it happens, the strategy is more careful about market conditions and typically waits for a countertrade. The third way of closing a trade has already been mentioned: the reverse trades. They happen during choppy market conditions. The strategy has also special awareness here and tracks, if reverse trades start to happen more often. After a while, it starts to be more restrictive in opening new reverse trades. Performance Capabilities and limitations: As I have already mentioned the strategy is only optimized for bitcoin (Perpetual Futures). This does not mean, it can not be used on other markets, because the algorithm itself is universal appliable. A very hard task was about finding the right parameter values for the strategy performing like this. If you have a special wish to configure this strategy for a specific market, DM me. The strategy has been tested with different configurations on the following timeframes: 30, 15, 10, 5, 1. I have decided to publish the one for 30m TF, because its performance simply convinced me. Repainting: It has been tested lots of times against repainting. Confidence: The total backtesting performance reaches out to 2019-09-08. So the strategy has been managing to be successful since then, but this does not guarantee that the logic, this strategy follows, is going to continue this level in future. Commission: The algorithm is configured with 0.04% commission per trade, as it is on Binance (for Future Market orders). Ordersize: Its totally up to you, how much of your total equity should be traded. Nevertheless, I would personally recommend to not exceed 50% ordersize of your equity with this strategy. In the past, you would have had great performance beside a drawdown, that was from psychological point of view good to handle with. This strategy additionally uses STOP LOSSES, so you can never loose you whole ordersize at one trade. Slippage: You also must consider about getting slipped when trading this strategy on live markets. Statistically one could assume, that the slippage could be neutral, as it can be both positive or negative. It depends on your execution time, the exchange, on which you are executing trades and market conditions. But keep it in mind, as if you have too much slippage, this strategy would be unprofitable.
Strategi Pine Script®
oleh btfactory
76
Bitcoin Momentum StrategyThis is a very simple long-only strategy I've used since December 2022 to manage my Bitcoin position. I'm sharing it as an open-source script for other traders to learn from the code and adapt it to their liking if they find the system concept interesting. General Overview Always do your own research and backtesting - this script is not intended to be traded blindly (no script should be) and I've done limited testing on other markets beyond Ethereum and BTC, it's just a template to tweak and play with and make into one's own. The results shown in the strategy tester are from Bitcoin's inception so as to get a large sample size of trades, and potential returns have diminished significantly as BTC has grown to become a mega cap asset, but the script includes a date filter for backtesting and it has still performed solidly in recent years (speaking from personal experience using it myself - DYOR with the date filter). The main advantage of this system in my opinion is in limiting the max drawdown significantly versus buy & hodl. Theoretically much better returns can be made by just holding, but that's also a good way to lose 70%+ of your capital in the inevitable bear markets (also speaking from experience). In saying all of that, the future is fundamentally unknowable and past results in no way guarantee future performance. System Concept: Capture as much Bitcoin upside volatility as possible while side-stepping downside volatility as quickly as possible. The system uses a simple but clever momentum-style trailing stop technique I learned from one of my trading mentors who uses this approach on momentum/trend-following stock market systems. Basically, the system "ratchets" up the stop-loss to be much tighter during high bearish volatility to protect open profits from downside moves, but loosens the stop loss during sustained bullish momentum to let the position ride. It is invested most of the time, unless BTC is trading below its 20-week EMA in which case it stays in cash/USDT to avoid holding through bear markets. It only trades one position (no pyramiding) and does not trade short, but can easily be tweaked to do whatever you like if you know what you're doing in Pine. Default parameters: HTF: Weekly Chart EMA: 20-Period ATR: 5-period Bar Lookback: 7 Entry Rule #1: Bitcoin's current price must be trading above its higher-timeframe EMA (Weekly 20 EMA). Entry Rule #2: Bitcoin must not be in 'caution' condition (no large bearish volatility swings recently). Enter at next bar's open if conditions are met and we are not already involved in a trade. "Caution" Condition: Defined as true if BTC's recent 7-bar swing high minus current bar's low is > 1.5x ATR, or Daily close < Daily 20-EMA. Trailing Stop: Stop is trailed 1 ATR from recent swing high, or 20% of ATR if in caution condition (ie. 0.2 ATR). Exit on next bar open upon a close below stop loss. I typically use a limit order to open & exit trades as close to the open price as possible to reduce slippage, but the strategy script uses market orders. I've never had any issues getting filled on limit orders close to the market price with BTC on the Daily timeframe, but if the exchange has relatively low slippage I've found market orders work fine too without much impact on the results particularly since BTC has consistently remained above $20k and highly liquid. Cost of Trading: The script uses no leverage and a default total round-trip commission of 0.3% which is what I pay on my exchange based on their tier structure, but this can vary widely from exchange to exchange and higher commission fees will have a significantly negative impact on realized gains so make sure to always input the correct theoretical commission cost when backtesting any script. Static slippage is difficult to estimate in the strategy tester given the wide range of prices & liquidity BTC has experienced over the years and it largely depends on position size, I set it to 150 points per buy or sell as BTC is currently very liquid on the exchange I trade and I use limit orders where possible to enter/exit positions as close as possible to the market's open price as it significantly limits my slippage. But again, this can vary a lot from exchange to exchange (for better or worse) and if BTC volatility is high at the time of execution this can have a negative impact on slippage and therefore real performance, so make sure to adjust it according to your exchange's tendencies. Tax considerations should also be made based on short-term trade frequency if crypto profits are treated as a CGT event in your region. Summary: A simple, but effective and fairly robust system that achieves the goals I set for it. From my preliminary testing it appears it may also work on altcoins but it might need a bit of tweaking/loosening with the trailing stop distance as the default parameters are designed to work with Bitcoin which obviously behaves very differently to smaller cap assets. Good luck out there!
Strategi Pine Script®
oleh ZenAndTheArtOfTrading
6
Bitcoin - MA Crossover StrategyBefore You Begin: Please read these warnings carefully before using this script, you will bear all fiscal responsibility for your own trades. Trading Strategy Warning - Past performance of this strategy may not equal future performance, due to macro-environment changes, etc. Account Size Warning - Performance based upon default 10% risk per trade, of account size $100,000. Adjust BEFORE you trade to see your own drawdown. Time Frame - D1 and H4. H4 has a lower profit factor (more fake-outs, and account drawdown), D1 recommended. Trend Following System - Profitability of this system is dependent on STRONG future trends in Bitcoin (BTCUSD). Default Settings: This script was tested on Daily and 4 Hourly charts using the following default settings. Note that 4 Hourly exhibits higher drawdowns and lower profit factor, whilst Daily appears more stable. Account Size ($): 100,000 (please adjust to simulate your own risk) Equity Risk (%): 10 (please adjust to simulate your own risk) Fast Moving Average (Period): 20 Slow Moving Average (Period): 40 Relative Strength Index (Period): 14 Trading Mechanism: Trend following strategies work well for assets that display the tendency of long-trends. Please do not use this script on financial assets that have a historical tendency for mean reversion. Bitcoin has historically exhibited strong trends, and thus this script is designed to capitalise on that behaviour. It is hoped (but we cannot predict), that Bitcoin will strongly trend in the coming days. LONG: Enter Long - When fast moving average (20) crosses ABOVE slow moving average (40) Exit Long - When fast moving average (20) crosses BELOW slow moving average (40) SHORT: Enter Short - When fast moving average (20) crosses BELOW slow moving average (40) Exit Short - When fast moving average (20) crosses ABOVE slow moving average (40) Risk Warnings: Do note that "moving averages" are a lagging indicator, and as such heavy drawdowns could occur when a trade is open. If you are trading this system manually, it is best to avoid emotions and let the system tell you when to enter and exit. Do not panic and exit manually when under heavy drawdown, always follow the system. Do not be emotional. If possible, connect this to your broker for auto-trading. Ensure that your risk per trade (Equity Risk) is SMALL enough that it does not result in a margin-call on your trading account. Equity risk must always be considered relative to your total account size. Remember: You bear all financial responsibility for your trades, best of luck.
Strategi Pine Script®
oleh UnknownUnicorn1373033
3
Bitcoin Scalping Strategy (Sampled with: PMARP+MADRID MA RIBBON) DISCLAIMER: THE CONTENT WITHIN THIS STRATEGY IS CREATED FROM TWO INDICATORS CREATED BY TWO PINESCRIPTER'S. THE STRATEGY WAS EXECUTED BY MYSELF AND REVERSE-ENGINEERED TO MEET THE CONDITIONS OF THE INTENDED STRATEGY REQUESTOR. I DO NOT TAKE CREDIT FOR THE CONTENT WITHIN THE ESTABLISHED LINES MADE CLEAR BY MYSELF. The Sampled Scripts and creators: PMAR/PMARP by @The_Caretaker Link to original script: Madrid MA RIBBON BAR by @Madrid Link to original script: Cheat Code's strategy notes: This sampled strategy (Requested by @elemy_eth) is one combining previously created studies. I reverse-engineered the local scope for the Madrid moving average color plots and set entry and exit conditions for certain criteria met. This strategy is meant to deliver an extremely high hit rate on a daily time frame. This is made possible because of the very low take profit percentage, during the context of a macro downtrend it is made easier to hit 1-3% scalps which is made visible with the strategy using sampled scripts I created here. How it works: Entry Conditions: -Enter Long's if the lime color conditions are met true using the script detailed by Marid's MA - No re-entry into positions needs to be met true (this prevents pyramiding of orders due to conditions being met true) applicable to both long and short side entries. - To increase hit rate and prevent traps both the parameters of rsi being sub 80 and no previously engulfing candles need to be met true to enter a long position. - Enter Short's if the red color conditions of Madrid's moving average are met true. - Closing Long positions are typically not met within this indicator, however, it still sometimes triggers if necessary. This consists of a pmarp sub 99 and a position size greater than 0.0 - Closing Short positions are typically not met within this indicator, however, it still sometimes triggers if necessary. This consists of a pmarp over 01 and a position size less than 0.0 - Stop Loss: 27.75% Take Profit: 1% (Which does not trigger on ticks over 1% so you will see average trade profits greater than 1%) BYBIT:BTCUSDT BINANCE:BTCUSDT COINBASE:BTCUSD Best Of Luck :) -CheatCode1
Strategi Pine Script®
oleh CheatCode1
14
Bitcoin lover by Akthis strategy used to determine and anticipate potential changes in market price and reversals. The Highs referred to in the title are created based on the number of bars that exhibit lower highs on either side of a High, whereas the Lows are created based on the number of bars that exhibit higher lows on either side Low.it also analyzing price changes and reversals, a trader has more of an ability to determine and predict price patterns and general price trends...It take profit on pre define 500 points.. u can also change is take profit level. .and also added max intraday trades..so it help in risk management brokerage added 0.02% and slippage added 20. How it works?? It calculate swing on 5min chart.. When swing high is break it shows buy signal and when swing low is break it show sell signal.. And it book profit on 500 points.. U can also change profit points. .After booking profit if high or low of price break it again show buy and sell signal.. U can also use it with trend lines and moving average for better entry and exits.. I hope this will help trader for find out trends and profit booking and if u want to use it with comment strategy action you have to just add comment in comment box caution: always follow risk management before using strategy
Strategi Pine Script®
oleh Aakashparikh787
15
BITCOIN-BTCUSD Trading Strategy-1This Trading Strategy is for BTCUSD which is traded on BITSTAMP exchange. The aim of this strategy is to ride the market with its ebbs and flows. The strategy works best on 1 Hour timeframe. The results may very different depending upon time-frame and date range of your choice and different exchanges. Parameters details: 1. Trade Trigger - You can adjust it depending upon your risk management. Default value is 20. 2. Partial Profit Points – Part profit booking points. Default value is 350. 3. Lookback Period 1 – Default is 11. 4. Lookback Period 2 – Default is 5. The default trading quantity is 2. I have added functionality for part profit booking which increase profit and always keeps you in trade. Book 1 lot for certain points (350 points gives better results) and continue other with 1 lot till the position reversed. I have tested this indicator on 1 Hour time-frame on BITSTAMP exchange and the time period is maximum available on Tradingview. Period : 02/01/2019 to 30/03/2020. Points = $  17720.56 No. of trades = 129 % Profitable = 44.96% Profit Factor = 2.464 Sharpe Ratio = 0.674 For free trial, send me a private message in order to access this script. Thanks © allanster for Date Range functionality and ©LonesomeTheBlue for non-repaint concepts. Disclaimer: This script/strategy is for educational and information purpose only. The author of this script/strategy will not accept any liability for loss or damage as a result of reliance on the information contained in the charts and buy/sell signals. Please be fully informed regarding the risks and costs associated with trading the financial markets.
Strategi Pine Script®
oleh AdityaTechTrader
Crypto MVRV ZScore - Strategy [PresentTrading]█ Introduction and How it is Different The "Crypto Valuation Extremes: MVRV ZScore - Strategy " represents a cutting-edge approach to cryptocurrency trading, leveraging the Market Value to Realized Value (MVRV) Z-Score. This metric is pivotal for identifying overvalued or undervalued conditions in the crypto market, particularly Bitcoin. It assesses the current market valuation against the realized capitalization, providing insights that are not apparent through conventional analysis. BTCUSD 6h Long/Short Performance Local █ Strategy, How It Works: Detailed Explanation The strategy leverages the Market Value to Realized Value (MVRV) Z-Score, specifically designed for cryptocurrencies, with a focus on Bitcoin. This metric is crucial for determining whether Bitcoin is currently undervalued or overvalued compared to its historical 'realized' price. Below is an in-depth explanation of the strategy's components and calculations. 🔶Conceptual Foundation - Market Capitalization (MC): This represents the total dollar market value of Bitcoin's circulating supply. It is calculated as the current price of Bitcoin multiplied by the number of coins in circulation. - Realized Capitalization (RC): Unlike MC, which values all coins at the current market price, RC is computed by valuing each coin at the price it was last moved or traded. Essentially, it is a summation of the value of all bitcoins, priced at the time they were last transacted. - MVRV Ratio: This ratio is derived by dividing the Market Capitalization by the Realized Capitalization (The ratio of MC to RC (MVRV Ratio = MC / RC)). A ratio greater than 1 indicates that the current price is higher than the average price at which all bitcoins were purchased, suggesting potential overvaluation. Conversely, a ratio below 1 suggests undervaluation. 🔶 MVRV Z-Score Calculation The Z-Score is a statistical measure that indicates the number of standard deviations an element is from the mean. For this strategy, the MVRV Z-Score is calculated as follows: MVRV Z-Score = (MC - RC) / Standard Deviation of (MC - RC) This formula quantifies Bitcoin's deviation from its 'normal' valuation range, offering insights into market sentiment and potential price reversals. 🔶 Spread Z-Score for Trading Signals The strategy refines this approach by calculating a 'spread Z-Score', which adjusts the MVRV Z-Score over a specific period (default: 252 days). This is done to smooth out short-term market volatility and focus on longer-term valuation trends. The spread Z-Score is calculated as follows: Spread Z-Score = (Market Z-Score - MVVR Ratio - SMA of Spread) / Standard Deviation of Spread Where: - SMA of Spread is the simple moving average of the spread over the specified period. - Spread refers to the difference between the Market Z-Score and the MVRV Ratio. 🔶 Trading Signals - Long Entry Condition: A long (buy) signal is generated when the spread Z-Score crosses above the long entry threshold, indicating that Bitcoin is potentially undervalued. - Short Entry Condition: A short (sell) signal is triggered when the spread Z-Score falls below the short entry threshold, suggesting overvaluation. These conditions are based on the premise that extreme deviations from the mean (as indicated by the Z-Score) are likely to revert to the mean over time, presenting opportunities for strategic entry and exit points. █ Practical Application Traders use these signals to make informed decisions about opening or closing positions in the Bitcoin market. By quantifying market valuation extremes, the strategy aims to capitalize on the cyclical nature of price movements, identifying high-probability entry and exit points based on historical valuation norms. █ Trade Direction A unique feature of this strategy is its configurable trade direction. Users can specify their preference for engaging in long positions, short positions, or both. This flexibility allows traders to tailor the strategy according to their risk tolerance, market outlook, or trading style, making it adaptable to various market conditions and trader objectives. █ Usage To implement this strategy, traders should first adjust the input parameters to align with their trading preferences and risk management practices. These parameters include the trade direction, Z-Score calculation period, and the thresholds for long and short entries. Once configured, the strategy automatically generates trading signals based on the calculated spread Z-Score, providing clear indications for potential entry and exit points. It is advisable for traders to backtest the strategy under different market conditions to validate its effectiveness and adjust the settings as necessary. Continuous monitoring and adjustment are crucial, as market dynamics evolve over time. █ Default Settings - Trade Direction: Both (Allows for both long and short positions) - Z-Score Calculation Period: 252 days (Approximately one trading year, capturing a comprehensive market cycle) - Long Entry Threshold: 0.382 (Indicative of moderate undervaluation) - Short Entry Threshold: -0.382 (Signifies moderate overvaluation) These default settings are designed to balance sensitivity to market valuation extremes with a pragmatic approach to trade execution. They aim to filter out noise and focus on significant market movements, providing a solid foundation for both new and experienced traders looking to exploit the unique insights offered by the MVRV Z-Score in the cryptocurrency market.
Strategi Pine Script®
oleh PresentTrading
6