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!
BTCUSD
High of Day Low of Day hourly timings: Statistics. Time of day %High of Day (HoD) & Low of Day (LoD) hourly timings: Statistics. Time of day % likelihood for high and low.
//Purpose:
To collect stats on the hourly occurrences of HoD and LoD in an asset, to see which times of day price is more likely to form its highest and lowest prices.
//How it works:
Each day, HoD and LoD are calculated and placed in hourly 'buckets' from 0-23. Frequencies and Percentages are then calculated and printed/tabulated based on the full asset history available.
//User Inputs:
-Timezone (default is New York); important to make sure this matches your chart's timezone
-Day start time: (default is Tradingview's standard). Toggle Custom input box to input your own custom day start time.
-Show/hide day-start vertical lines; show/hide previous day's 'HoD hour' label (default toggled on). To be used as visual aid for setting up & verifying timezone settings are correct and table is populating correctly).
-Use historical start date (default toggled off): Use this along with bar-replay to backtest specific periods in price (i.e. consolidated vs trending, dull vs volatile).
-Standard formatting options (text color/size, table position, etc).
-Option to show ONLY on hourly chart (default toggled off): since this indicator is of most use by far on the hourly chart (most history, max precision).
// Notes & Tips:
-Make sure Timezone settings match (input setting & chart timezone).
-Play around with custom input day start time. Choose a 'dead' time (overnight) so as to ensure stats are their most meaningful (if you set a day start time when price is likely to be volatile or trending, you may get a biased / misleadingly high readout for the start-of-day/ end-of-day hour, due to price's tendency for continuation through that time.
-If you find a time of day with significantly higher % and it falls either side of your day start time. Try adjusting day start time to 'isolate' this reading and thereby filter out potential 'continuation bias' from the stats.
-Custom input start hour may not match to your chart at first, but this is not a concern: simply increment/decrement your input until you get the desired start time line on the chart; assuming your timezone settings for chart and indicator are matching, all will then work properly as designed.
-Use the the lines and labels along with bar-replay to verify HoD/LoD hours are printing correctly and table is populating correctly.
-Hour 'buckets' represent the start of said hour. i.e. hour 14 would be populated if HoD or LoD formed between 14:00 and 15:00.
-Combined % is simply the average of HoD % and LoD %. So it is the % likelihood of 'extreme of day' occurring in that hour.
-Best results from using this on Hourly charts (sub-hourly => less history; above hourly => less precision).
-Note that lower tier Tradingview subscriptions will get less data history. Premium acounts get 20k bars history => circa 900 days history on hourly chart for ES1!
-Works nicely on Btc/Usd too: any 24hr assets this will give meaningful data (whereas some commodities, such as Lean Hogs which only trade 5hrs in a day, will yield less meaningful data).
Example usage on S&P (ES1! 1hr chart): manual day start time of 11pm; New York timezone; Visual aid lines and labels toggled on. HoD LoD hour timings with 920 days history:
kyle algo v1
Integration of multiple technical indicators: The strategy mainly combines two technical indicators - Keltner Channels and Supertrend, to generate trading signals. It also calculates fifteen exponential moving averages (EMAs) for the high price with different periods ranging from 9 to 51.
Unique combination of indicators: The traditional Supertrend typically uses Average True Range (ATR) to calculate its upper and lower bands. In contrast, this script modifies the approach to use Keltner Channels instead.
Flexible sensitivity adjustment: This strategy provides a "sensitivity" input parameter for users to adjust, which controls the multiplier for the range in the Supertrend calculation. This can make the signals more or less sensitive to price changes, allowing users to tailor the strategy to their own risk tolerance and trading style.
EMA Energy Representation: The code offers a visualization of "EMA Energy", which color-codes the EMA lines based on whether the closing price is above or below the EMA line. This can provide an intuitive understanding of market trends.
Clear visual signals: The strategy generates clear "BUY" and "SELL" signals, represented as labels on the chart. This makes it easy to identify potential entry and exit points in the market.
Customizable: The script provides several user inputs, making it possible to fine-tune the strategy according to different market conditions and individual trading preferences.
EMA (Exponential Moving Average) Principle:
The EMA is a type of moving average that assigns more weight to the most recent data.
It responds more quickly to recent price changes and is used to capture short-term price trends.
Principle of Color Change :
In this trading strategy, the color of the EMA line changes based on whether the closing price is above or below the EMA. If the closing price is above the EMA, the EMA line turns green,
indicating an upward price trend. Conversely, if the closing price is below the EMA, the EMA line turns red,
indicating a downward price trend. These color changes help traders to more intuitively identify price trends
In short, our team provides a lot of practical space
That is your development space
Bitcoin Limited Growth ModelThe Bitcoin Limeted Growth is a model proposed by QuantMario that offers an alternative approach to estimating Bitcoin's price based on the Stock-to-Flow (S2F) ratio. This model takes into account the limitations of the traditional S2F model and introduces refinements to enhance its analysis.
The S2F model is commonly used to analyze Bitcoin's price by considering the scarcity of the asset, measured by the stock (existing supply) relative to the flow (new supply). However, the LGS-S2F Bitcoin Price Formula recognizes the need for improvements and presents an updated perspective on Bitcoin's price dynamics.
Invalidation of the Normal S2F Model:
The normal S2F model has faced criticisms and challenges. One of the limitations is its assumption of a linear relationship between the S2F ratio and Bitcoin's price, overlooking potential nonlinearities and other market dynamics. Additionally, the normal S2F model does not account for external influences, such as market sentiment, regulatory developments, and technological advancements, which can significantly impact Bitcoin's price.
Addressing the Issues:
The LGS-S2F Bitcoin Price Formula introduces refinements to address the limitations of the traditional S2F model. These refinements aim to provide a more comprehensive analysis of Bitcoin's price dynamics:
Nonlinearity: The LGS-S2F model recognizes that the relationship between the S2F ratio and Bitcoin's price may not be linear. It incorporates a logistic growth function that considers the diminishing returns of scarcity and the saturation of market demand.
Data Analysis: The LGS-S2F model employs statistical analysis and data-driven techniques to validate its predictions. It leverages historical data and econometric modeling to support its analysis of Bitcoin's price.
Utility:
The LGS-S2F Bitcoin Price Formula offers insights for traders and investors in the cryptocurrency market. By incorporating a more refined approach to analyzing Bitcoin's price, this model provides an alternative perspective. It allows market participants to consider various factors beyond the S2F ratio alone, potentially aiding in their decision-making processes.
Key Features:
Adjustable Coefficients
Sigma calculation methods: Normal or Stdev
Credit:
The LGS-S2F Bitcoin Price Formula was developed by QuantMario, who has contributed to the field of cryptocurrency analysis through their research and modeling efforts.
The Z-score The Z-score, also known as the standard score, is a statistical measurement that describes a value's relationship to the mean of a group of values. It's measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. Z-scores may be positive or negative, with a positive value indicating the score is above the mean and a negative score indicating it is below the mean.
The concept of Z-score was introduced by statistician Carl Friedrich Gauss as part of his "method of the least squares," which was an important step in the development of the normal distribution and Z-score tables. It's a key concept in statistics and is used in various statistical tests.
In financial analysis, Z-scores are used to determine whether a data point is usual or unusual. You can think of it as a measure of how many standard deviations an element is from the mean. For instance, a Z-score of 1.0 would denote a value that is one standard deviation from the mean. Z-scores are also used to predict probabilities, with Z-scores having a distribution that is expected to be normal.
In trading, a Z-score is used to determine how often a trading system may produce a string of winners or losers. It can help a trader to understand whether the losses or profits they see are something that the system would most likely produce, or if it's a once in a blue moon situation. This helps traders make decisions about when to start or stop a system.
I just wanted to play a bit with the Z-score I guess.
Feel free to share your findings if you discover additional applications for this strategy or identify timeframes where it appears to perform more optimally.
How it works:
This strategy is based on a statistical concept called Z-score, which measures the number of standard deviations a data point is from the mean. In other words, it helps determine how unusual or usual a data point is.
In the context of this strategy, Z-score is applied to a 10-period EMA (Exponential Moving Average) of Heikin-Ashi candlestick close prices. The Z-score is calculated over a look-back period of 25 bars.
The EMA of the Z-score is then calculated over a 20-bar period, and the upper and lower thresholds (bounds for buy and sell signals) are defined using the 90th and 10th percentiles of this EMA score.
Long positions are taken when the Z-score crosses above the lower threshold or crosses above the mid-line (50th percentile). An additional long entry is made when the Z-score crosses above the highest value the EMA has been in the past 100 periods.
Short positions are initiated when the EMA crosses below the upper threshold, lower threshold or the highest value the EMA has been in the past 100 periods.
Positions are closed when opposing entry conditions are met, for example, a long position is closed when the short entry condition is true, and vice versa.
Set your desired start date for the strategy. This can be modified in the timestamp("YYYY MM DD") function at the top of the script.
Comparison with BTC (RSI)显示当前品种与BTC汇率对的RSI值
以此判断强势或弱势品种以及超买超卖
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Display the RSI value of the exchange rate between the current variety and BTC
Use this to determine strong or weak varieties, as well as overbought and oversold
Probability Trend IndicatorUnderstanding the Indicator:
The indicator calculates the probabilities of upward and downward trends based on the percentage change in price over a specified lookback period.
It displays these probabilities in a table and plots a histogram to represent the difference between the probabilities.
The colors of the histogram bars indicate the trend direction and whether the trend is increasing or decreasing.
Setting the Lookback Period:
The indicator allows you to specify the lookback period, which determines the number of bars to consider for calculating the probabilities.
By default, the lookback period is set to 50 bars. However, you can adjust it based on your trading preferences and the timeframe you're analyzing.
Analyzing the Probabilities:
The indicator calculates the probabilities of upward and downward trends and displays them in a table on the chart.
The probabilities are presented as percentages, representing the likelihood of each type of trend occurring.
You can use these probabilities to gain insights into the potential market direction and assess the strength of the prevailing trend.
Interpreting the Histogram:
The histogram is plotted based on the difference between the probabilities of upward and downward trends, known as the oscillator value.
The histogram bars are colored to provide visual cues about the trend direction and whether the trend is gaining or losing strength.
Green bars indicate upward trends, and red bars indicate downward trends.
Lighter shades of green or red suggest increasing trends, while darker shades suggest decreasing trends.
Making Trading Decisions:
The indicator serves as a tool for assessing the probabilities of trends and can be used alongside other technical analysis methods.
You can consider the probabilities, the histogram pattern, and the overall market context to make informed trading decisions.
It's important to remember that no indicator or tool can guarantee future market movements, so prudent risk management and additional analysis are essential.
Crypto Trend IndicatorThe Crypto Trend Indicator is a trend-following indicator specifically designed to identify bullish and bearish trends in the price of Bitcoin, and other cryptocurrencies. This indicator doesn't provide explicit instructions on when to buy or sell, but rather offers an understanding of whether the trend is bullish or bearish. It's important to note that this indicator is only useful for trend trading.
The band is a visual representation of the 30-day and 60-day Exponential Moving Average (EMA). When the 30-day EMA is above the 60-day EMA, the trend is bullish and the band is green. When the 30-day EMA is below the 60-day EMA, the trend is bearish and the band is red. When the 30-day EMA starts to converge with the 60-day EMA, the trend is neutral and the band is grey.
The line is a visual representation of the 20-week Simple Moving Average (SMA) in the daily timeframe. "Bull" and "Bear" signals are generated when the 20-day EMA is either above or below the 20-week SMA, in conjunction with a bullish or bearish trend. When the band is green and the 20-day EMA is above the 20-week SMA, a “Bull” signal emerges. When the band is red and the 20-day EMA is below the 20-week SMA, a “Bear” signal emerges. The 20-week SMA can potentially also function as a leading indicator, as substantial price deviations from the SMA typically indicate an overextended market.
While this indicator has traditionally identified bullish and bearish trends in various cryptocurrency assets, past performance does not guarantee future results. Therefore, it is advisable to supplement this indicator with other technical tools. For instance, range-bound indicators can greatly improve the decision-making process when planning for entries and exits points.
Cryptocurrency Market Sentiment v1.0Introduction:
Capable of observing the market sentiment of the cryptocurrency market
The relative status of BTC and altcoins
How it works:
1. The general uptrend process of the cryptocurrency market is BTC → ETH → high-cap altcoins → low-cap altcoins. When funds cannot push up BTC's market cap, funds gradually flow into smaller-cap altcoins until the upward trend ends.
2. Select ETH as the representative of altcoins, and understand the sentiment and current stage
3. Mathematical principle : divide the price of ETH by the price of BTC, and then apply it to the RSI formula .
How to use it:
1. Similar to the RSI indicator , when CMS enters the overbought zone, it represents an active altcoin market, a passionate market sentiment , and the end of the uptrend.
2. When CMS enters the oversold zone, it indicates the leading stage of BTC in the rising trend or the capital flow back to BTC in the declining process .
3. If CMS is at a low level, long positions should focus on altcoins, and short positions should focus on BTC, and vice versa.
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简单介绍：
能够观察加密市场市场情绪
BTC和寨币的相对状态
如何工作：
1、加密市场一般的上涨过程为 BTC → ETH → 大市值山寨 → 小市值山寨，当资金无法推动大市值的BTC上涨时，资金就会逐渐流向市值较小的山寨，直到一轮上涨结束。
2、选取ETH作为altcoins的代表，通过ETH与BTC的关系来了解加密市场的情绪和目前上涨的阶段。
3、数学原理：将ETH的价格/BTC的价格，随后将其带入RSI公式
如何使用：
1、与RSI指标类似，当cms进入超买时，代表寨币市场的活跃，市场情绪热烈，上涨进入尾声。
2、当cms进入超卖时，为上涨中BTC领涨的阶段或下降过程中资金回流BTC。
3、如果cms在低位，做多应关注altcoins，做空应关注btc，反之亦然。
Kimchi Premium StrategyThis strategy is based on the Korea Premium, also known as the “Kimchi Premium,” which indicates how expensive or cheap the price of Bitcoin in Korean Won on a Bitcoin exchange in South Korea is relative to the price of Bitcoin being traded in USD or Tether. Inverse Kimchi Premium RSI was newly defined to create a strategy with Kimchi Premium. Assuming that the larger the kimchi premium, the greater the individual's purchasing power. In this case, if the Inverse Kimchi Premium RSI falls and closes the candle below the bear level, a short is triggered. Long is the opposite.
This strategy defaults to a combination of the traditional RSI and the Inverse Kimchi Premium RSI. If the user wishes to unlock the Inverse Kimchi Premium RSI combination and only use it as a traditional RSI strategy, the following settings can be used.
Use Combination of Inverse Kimchi Premium RSI: Uncheck
Resolution: Chart (4hr Candle)
Source: Close
Length of RSI: 14
Bull Level: 74
Bear Level: 25
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김치프리미엄(김프) 전략은 달러 혹은 테더로 거래되고 있는 비트코인 가격 대비 한국에 있는 비트코인 거래소의 비트코인 원화 가격이 얼마나 비싸고 싼 지를 나타내는 코리아 프리미엄, 일명 "김치 프리미엄" 지표를 기반으로 만들어졌습니다. 김치 프리미엄을 가지고 전략을 만들기위해 Inverse Kimchi Premium RSI를 새롭게 정의하였습니다. 김치 프리미엄이 커질수록 개인의 매수세가 커진다고 가정하고, 이 경우 Inverse Kimchi Premium RSI이 하락하여 Bear Level 아래에서 캔들 마감을 하면 Short을 트리거 합니다. Long은 그 반대입니다.
이 전략은 전통적인 RSI와 Inverse Kimchi Premium RSI을 조합하여 기본값을 설정하였습니다. 유저가 원한다면 Inverse Kimchi Premium RSI의 조합을 해제하고 전통적인 RSI 전략으로만 사용하려면 아래 다음의 설정값을 사용할 수 있습니다.
Use Combination of Inverse Kimchi Premium RSI: 체크 해제
Resolution: Chart (4hr Candle)
Source: Close
Length of RSI: 14
Bull Level: 74
Bear Level: 25
BTC / DXY, BTC / US10Y
The combination of the DXY and US02Y can be used to gauge market sentiment and assess the state of the global economy.
When the DXY is rising, it indicates that the U.S. dollar is strengthening relative to other currencies, which can lead to increased risk aversion among investors as the U.S. dollar is often seen as a safe-haven currency.
When the US02Y is rising, it suggests that market expectations for future inflation and interest rate increases are increasing, which can lead to a decrease in the value of riskier assets such as stocks.
In general, the combination of the DXY and US02Y can provide important information on the direction of global market trends and the state of the economy, and as such, they are important indicators to consider when making investment decisions.
ATR Mean Reversion Strategy V1**Long Only Strategy**
When Price drops below the ATR band below it will enter a buy on the next candle open
SL at current price minus ATR* ATR multiplier
TP at Mean EMA or if higher than Mean EMA and current candle low is below previous candle low or if price is above ATR
NB: I would highly recommend a low fee broker (I use ICmarkets raw spread account) due to the fact that this is a decently high frequency trading strategy you will rack up a lot of commission, if you use and exchange like Bybit or Binance the strategy will not be profitable due to the high commissions.
Price Distance RatioThis study plots the ratio between current price and the price N days ago.
With N input that is configurable, users can find optimal long/short entries when price is in an established trend and price has diverge far from a given local peak or all time high.
With many years of stock trading the analysis indicates a connection between the distance of price and subsequent returns.
Portfolios of stocks with lower price to local highes ratios generally underperformed portfolios of stocks with higher prices to peaks reached similar N days ago.
The highest returns to previous peak are recorded when buying at the biggest dip.
For example, the purchase at 20% drawdown could generate 25% when price returns to the peak. The purchase at 50% drawdown could generate bigger, i.e. 100% return, when price returns to the peak. And the purchase at 90% drawdown could generate much bigger, i.e. 900% return, in a case the price returns to the peak.
However, buying very far below local peaks on almost all holding periods produces lower CAGR returns because of "timing adjustment". In simple words, typically the drawdown takes less time vs. further recovery.
For example:
👉 The largest BTC drawdown in 2013-2015 took 410 days (Peak-to-Valley) . And the recovery of BTC to new highs took 771 days (Valley-to-Peak) after that.
👉 The 3rd longest drawdown in BTC took 363 days (observed from December 17, 2017 to December 15, 2018). And further recovery in BTC to its new high took almost two years - 716 days .
👉The 4th longest drawdown in BTC took 162 days (observed from June 08, 2011 to November 17, 2011). And further recovery in BTC to its new high took more than a year - 469 days .
The concept of this study could recognizes at least 4 different modes of action.
👉 In a clearly established upward trend traders should be buying (following the trend) when Ratio is above 100% and reducing the size when Ratio turns below 100%.
👉 Conversely, in a clearly established downward trend traders should be shorted when Ratio is below 100% and covering when the Ratio turns back to 100%.
👉 In a sideways movement traders are advised to wait carefully if the Ratio near 100% for a long time, and take a position the trend is clear.
👉 Chartists can analyze the dynamic of the indicator - both in terms of trends and overall level. For example as it shown at the chart.
The understading of the study and rules of "timing adjustments" could genarate the awesome opportunities for stock options traders also, with strategies of selling uncovered call options and vertical call spreads.
// Many thanks to @HPotter and @Wheeelman wizards for their continious support and assistance.
BTC Net Volume (Spot) (by JaggedSoft, fixed by SLN)• WHAT:
This indicator plots the aggregated net volume delta of BTC spot pairs from 8 exchanges over the last 60 periods (default settings).
Tracks the following pairs:
"BINANCE:BTCUSDT"
"BITFINEX:BTCUSD"
"POLONIEX:BTCUSDT"
"BITTREX:BTCUSDT"
"COINBASE:BTCUSD"
"BITSTAMP:BTCUSD"
"KRAKEN:XBTUSD"
"BITGET:BTCUSDT"
"GEMINI:BTCUSD"
• HOW TO USE:
Used for confirmation when watching futures that can experience quick movements in the form of liquidation-events. If the oscillator is green or trending upward, it's confirming a positive bias. The inverse is true for a negative bias. This is especially true on higher timeframes.
Can also be used to find correlations between different tech-assets.
• NOTES:
I forked JaggedSofts indicator to fix the data-source error it was having. Let me know if you want to customize exchanges or add more pairs, maybe I can add that in the future!
This indicator replaces the outdated alternative linked here : Please only use this one
• LIMITATIONS:
Only tested with normal japanese candlesticks .
• THANKS:
to the creator of this script, JaggedSoft. It's a great indicator!
• DISCLAIMER:
Not financial Advice, use at your own risk.
True Bitcoin Value USD - Mario MThe average mining costs of one bitcoin equals to the true intrinsic value
Globally, the Bitcoin network uses around 0.5% of the world’s electrical power supply.
The sheer amount of electrical power and complex hardware required to operate a mining farm has intrinsic value.
This gives bitcoin a fundamental cost to create, and thus intrinsic value.
Trend Following based on Trend ConfidenceThis is a Trend Following strategy based on the Trend Confidence indicator.
The goal of this strategy is to be a simple Trend Following strategy, but also to be as precise as possible when it comes to the question 'how confident are we that a linear trend is ongoing?'. For this we calculate the 'confidence' of a linear trend in the past number of closing prices. The idea of this strategy is that past a certain confidence, the ongoing linear trend is more likely to continue than not.
Trend Confidence:
The Trend Confidence shows us how strong of a linear trend the price has made in the past number (given by Length parameter) of closing prices. The steepness of the price change makes the Trend Confidence more extreme (more positive for an uptrend or more negative for a downtrend), and the deviation from a straight line makes the Trend Confidence less extreme (brings the confidence closer to 0). This way we can filter out signals by wild/sudden price moves that don't follow a clear linear trend.
Math behind the Trend Confidence:
A linear fit is made on the past number of closing prices, using Ordinary Linear Regression. We have the steepness of the linear fit: b in y=a+bx . And we have the standard deviation of the distances from the closing prices to the linear fit: sd . The Trend Confidence is the ratio b/sd .
Entries and Exits:
For entry and exit points we look at how extreme the Trend Confidence is. The strategy is based on the assumption that past a certain confidence level, the ongoing linear trend is more likely to continue than not.
So when the Trend Confidence passes above the 'Long entry" threshold, we go Long. After that when the Trend Confidence passes under the 'Long exit' threshold, we exit. The Long entry should be a positive value so that we go Long once a linear uptrend with enough confidence has been detected.
When the Trend Confidence passes below the 'Short entry' threshold, we go Short. After that when the Trend Confidence passes above the 'Short exit' threshold, we exit. The Short entry should be a negative value so that we go Short once a linear downtrend with enough confidence has been detected.
Default Parameters:
The strategy is intended for BTC-USD market, 4 hour timeframe. The strategy also works on ETH-USD with similar parameters.
The Length is arbitrarily set at 30, this means we look at the past 30 closing prices to determine a linear trend. Note that changing the length will change the range of Trend Confidence values encountered.
The default entry and exit thresholds for Longs and Shorts do not mirror each other. This is because the BTC-USD market goes up more heavily and more often than it goes down. So the ideal parameters for Longs and Shorts are not the same.
The positive results of the strategy remain when the parameters are slightly changed (robustness check).
The strategy uses 100% equity per trade, but has a 10% stop loss so that a maximum of 10% is risked per trade.
Commission is set at 0.1% as is the highest commission for most crypto exchanges.
Slippage is set at 5 ticks, source for this is theblock.co.
BTC Twitter SentimentBTC Twitter Sentiment - shows the total numbers of all negative, neutral and positive Bitcoin related tweets.
On default settings, the tweets are plotted in red (negative) white (neutral) as well as green (positive). The three charts are stacked so the total number of tweets is easily discernible.
Furthermore, there's an optional smoothing setting in the options.
The Twitter Sentiment data is provided daily by IntoTheBlock; Since data is only updated once a day the graph might look chunky on lower timeframes, even with smoothing.
BTC Hashrate ribbonsBTC Hash Rate ribbons / Hash Rate cross
This strategy goes long when BTCs Hash Rate 30 day moving average crosses above the 60 day moving average, signifying that miner capitulation is over and recovery has started.
When the opposite signal is given, which signifies the beginning of miner capitulation, the strategy goes short (or flat, depending on configuration). This is generally considered the most popular Hash Rate related strategy.
The strategy is based on this medium article: medium.com
Thanks to the recent integration of IntoTheBlock data into Tradingview, we can now effortlessly show Hash Rate data on our chart,
keep in mind however, that IntoTheBlock doesn't provide Hash Rate data on timeframes below daily, so this strategy is based used on the daily, weekly or even monthly time frames.
Hash Rate definition:
The Bitcoin hash rate is the number of times per second that computers on the Bitcoin network are hashing data to verify transactions and perform the encryption that secures the network. The hash rate is an indicator of how healthy the Bitcoin network is at any given time, and is driven primarily by difficulty mining and the number of miners. Generally, a high hash rate is considered a good thing.
More precisely, the Bitcoin hash rate is the number of times per second that computers on the Bitcoin network are hashing data to verify transactions and perform the encryption that secures the network.
Volume Buy/Sell (by iammaximov)Buy/Sell Volume
Buy/Sell volume based on total bar amplitude (high/low price) and close/open (for calculate vbuy > vsell or vbuy < vsell)
DlineDline is a indicator that was developed by B-Negative. This indicator was developed under convergence logic. If we have many information of prices, when the information was averaged with more enough, the average line will be the linear line that has direction. The direction of this linear line can help traders to analyze the direction of trends. Dline was made with TEMA, EMA, DEMA, and Dline line that is a average line between DEMA and EMA.
Under B-Negative's concept, DEMA and EMA that are average lines will convergence and have same direction when the trends are coming. Amount of data must more enough and diferrect by assets' type. However, user can change value of DEMA, Dline, EMA, and TEMA by themself under 7 concepts below.
1. EMA will convergence to close Dline when the trend will be changing.
2. The uptrend will occure when EMA above/below Dline and candle sticks are green/red color.
3. TEMA was setted similair DEMA.
4. When new high/low of wave cross TEMA and can not retrun to create higher/lower high/low (At oversold/overbought, Stocastic 9,3,3 counting with loop technique), that is exit point of position.
5. Difference of timeframe or assets could use different parameters. (Setting based on 4 rule above.)
6. Divergence between Dline and EMA mean sentiment of assets are sideways.
7. If Dline and EMA look like same line, the trend is most strength trend.
Dline use thickness = 4
EMA use thickness = 1
This ex. is timeframe day.
Trend Identifier StrategyTrend Identifier Strategy for 1D BTC.USD
The indicator smoothens a closely following moving average into a polynomial like plot and assumes 4 staged cycles based on the first and the second derivatives. This is an optimized strategy for long term buying and selling with a Sortino Ratio above 3. It is designed to be a more profitable alternative to HODLing. It can be combined with 'Accumulation/Distribution Bands & Signals' and 'Exponential Top and Bottom Finder'.
Fast v Slow Moving Averages Strategy (Variable) [divonn1994]This is a simple moving average based strategy that takes 2 moving averages, a Fast and a Slow one, plots them both, and then decides to enter a 'long' position or exit it based on whether the two lines have crossed each other. It goes 'long when the Fast Moving Average crosses above the Slow Moving Average. This could indicate upwards momentum in prices in the future. It then exits the position when the the Fast Moving Average crosses back below. This could indicate downwards momentum in prices in the future. This is only speculative, though, but sometimes it can be a very good indicator/strategy to predict future action.
I've tried some strategy settings and I found different promising strategies. Here are a few:
BTCUSD ( BitStamp ) 1 Day Timeframe : EMA, Fast length 25 bars, Slow length 62 bars => 28,792x net profit (default)
BTCUSD ( BitStamp ) 1 Day Timeframe : VWMA, Fast length 21 bars, Slow length 60 bars => 15,603x net profit
BTCUSD ( BitStamp ) 1 Day Timeframe : SMA, Fast length 18 bars, Slow length 51 bars => 19,507x net profit
BTCUSD ( BitStamp ) 1 Day Timeframe : RMA, Fast length 20 bars, Slow length 52 bars => 5,729x net profit
BTCUSD ( BitStamp ) 1 Day Timeframe : WMA, Fast length 29 bars, Slow length 60 bars => 19,869x net profit
Features:
-You can choose your preferred moving average: SMA , EMA , WMA , RMA & VWMA .
-You can change the length average for each moving average
-I made the background color Green when you're currently in a long position and Red when not. I made it so you can see when you'd be actively in a trade or not. The Red and Green background colors can be toggled on/off in order to see other indicators more clearly overlayed in the chart, or if you prefer a cleaner look on your charts.
-I also have a plot of the Fast moving average and Slow moving average together. The Opening moving average is Purple, the Closing moving average is White. White on top is a sign of a potential upswing and purple on top is a sign of a potential downswing. I've made this also able to be toggled on/off.
Let me know if you think I should change anything with my script, I'm always open to constructive criticism so feel free to comment below :)
Trade HourThis script is just finds the best hour to buy and sell hour in a day by checking chart movements in past
For example if the red line is on the 0.63 on BTC/USDT chart it mean the start of 12AM hour on a day is the best hour to buy (all based on
It's just for 1 hour time-frame but you can test it on other charts.
IMPORTANT: You can change time Zone in strategy settings.to get the real hours as your location timezone
IMPORTANT: Its for now just for BTC/USDT but you can optimize and test for other charts...
IMPORTANT: A green and red background color calculated for show the user the best places of buy and sell (green : positive signal, red: negative signals)
settings :
timezone : We choice a time frame for our indicator as our geo location
source : A source to calculate rate of change for it
Time Period : Time period of ROC indicator
About Calculations:
1- We first get a plot that just showing the present hour as a zigzag plot
2- So we use an indicator ( Rate of change ) to calculate chart movements as positive and negative numbers. I tested ROC is the best indicator but you can test close-open or real indicator or etc as indicator.
3 - for observe effects of all previous data we should indicator_cum that just a full sum of indicator values.
4- now we need to split this effects to hours and find out which hour is the best place to buy and which is the best for sell. Ok we should just calculate multiple of hour*indicator and get complete sum of it so:
5- we will divide this number to indicator_cum : (indicator_mul_hour_cum) / indicator_cum
6- Now we have the best hour to buy! and for best sell we should just reverse the ROC indicator and recalculate the best hour for it!
7- A green and red background color calculated for show the user the best places of buy and sell that dynamically changing with observing green and red plots(green : positive signal, red: negative signals) when green plot on 15 so each day on hour 15 the background of strategy indicator will change to 15 and if its go upper after some days and reached to 16 the background green color will move to 16 dynamically.
