Multi-Step FlexiSuperTrend - Strategy [presentTrading]At the heart of this endeavor is a passion for continuous improvement in the art of trading
█ Introduction and How it is Different
The "Multi-Step FlexiSuperTrend - Strategy " is an advanced trading strategy that integrates the well-known SuperTrend indicator with a nuanced and dynamic approach to market trend analysis. Unlike conventional SuperTrend strategies that rely on static thresholds and fixed parameters, this strategy introduces multi-step take profit mechanisms that allow traders to capitalize on varying market conditions in a more controlled and systematic manner.
What sets this strategy apart is its ability to dynamically adjust to market volatility through the use of an incremental factor applied to the SuperTrend calculation. This adjustment ensures that the strategy remains responsive to both minor and major market shifts, providing a more accurate signal for entries and exits. Additionally, the integration of multi-step take profit levels offers traders the flexibility to scale out of positions, locking in profits progressively as the market moves in their favor.
BTC 6hr Long/Short Performance
█ Strategy, How it Works: Detailed Explanation
The Multi-Step FlexiSuperTrend strategy operates on the foundation of the SuperTrend indicator, but with several enhancements that make it more adaptable to varying market conditions. The key components of this strategy include the SuperTrend Polyfactor Oscillator, a dynamic normalization process, and multi-step take profit levels.
🔶 SuperTrend Polyfactor Oscillator
The SuperTrend Polyfactor Oscillator is the heart of this strategy. It is calculated by applying a series of SuperTrend calculations with varying factors, starting from a defined "Starting Factor" and incrementing by a specified "Increment Factor." The indicator length and the chosen price source (e.g., HLC3, HL2) are inputs to the oscillator.
The SuperTrend formula typically calculates an upper and lower band based on the average true range (ATR) and a multiplier (the factor). These bands determine the trend direction. In the FlexiSuperTrend strategy, the oscillator is enhanced by iteratively applying the SuperTrend calculation across different factors. The iterative process allows the strategy to capture both minor and significant trend changes.
For each iteration (indexed by `i`), the following calculations are performed:
1. ATR Calculation: The Average True Range (ATR) is calculated over the specified `indicatorLength`:
ATR_i = ATR(indicatorLength)
2. Upper and Lower Bands Calculation: The upper and lower bands are calculated using the ATR and the current factor:
Upper Band_i = hl2 + (ATR_i * Factor_i)
Lower Band_i = hl2 - (ATR_i * Factor_i)
Here, `Factor_i` starts from `startingFactor` and is incremented by `incrementFactor` in each iteration.
3. Trend Determination: The trend is determined by comparing the indicator source with the upper and lower bands:
Trend_i = 1 (uptrend) if IndicatorSource > Upper Band_i
Trend_i = 0 (downtrend) if IndicatorSource < Lower Band_i
Otherwise, the trend remains unchanged from the previous value.
4. Output Calculation: The output of each iteration is determined based on the trend:
Output_i = Lower Band_i if Trend_i = 1
Output_i = Upper Band_i if Trend_i = 0
This process is repeated for each iteration (from 0 to 19), creating a series of outputs that reflect different levels of trend sensitivity.
Local
🔶 Normalization Process
To make the oscillator values comparable across different market conditions, the deviations between the indicator source and the SuperTrend outputs are normalized. The normalization method can be one of the following:
1. Max-Min Normalization: The deviations are normalized based on the range of the deviations:
Normalized Value_i = (Deviation_i - Min Deviation) / (Max Deviation - Min Deviation)
2. Absolute Sum Normalization: The deviations are normalized based on the sum of absolute deviations:
Normalized Value_i = Deviation_i / Sum of Absolute Deviations
This normalization ensures that the oscillator values are within a consistent range, facilitating more reliable trend analysis.
For more details:
🔶 Multi-Step Take Profit Mechanism
One of the unique features of this strategy is the multi-step take profit mechanism. This allows traders to lock in profits at multiple levels as the market moves in their favor. The strategy uses three take profit levels, each defined as a percentage increase (for long trades) or decrease (for short trades) from the entry price.
1. First Take Profit Level: Calculated as a percentage increase/decrease from the entry price:
TP_Level1 = Entry Price * (1 + tp_level1 / 100) for long trades
TP_Level1 = Entry Price * (1 - tp_level1 / 100) for short trades
The strategy exits a portion of the position (defined by `tp_percent1`) when this level is reached.
2. Second Take Profit Level: Similar to the first level, but with a higher percentage:
TP_Level2 = Entry Price * (1 + tp_level2 / 100) for long trades
TP_Level2 = Entry Price * (1 - tp_level2 / 100) for short trades
The strategy exits another portion of the position (`tp_percent2`) at this level.
3. Third Take Profit Level: The final take profit level:
TP_Level3 = Entry Price * (1 + tp_level3 / 100) for long trades
TP_Level3 = Entry Price * (1 - tp_level3 / 100) for short trades
The remaining portion of the position (`tp_percent3`) is exited at this level.
This multi-step approach provides a balance between securing profits and allowing the remaining position to benefit from continued favorable market movement.
█ Trade Direction
The strategy allows traders to specify the trade direction through the `tradeDirection` input. The options are:
1. Both: The strategy will take both long and short positions based on the entry signals.
2. Long: The strategy will only take long positions.
3. Short: The strategy will only take short positions.
This flexibility enables traders to tailor the strategy to their market outlook or current trend analysis.
█ Usage
To use the Multi-Step FlexiSuperTrend strategy, traders need to set the input parameters according to their trading style and market conditions. The strategy is designed for versatility, allowing for various market environments, including trending and ranging markets.
Traders can also adjust the multi-step take profit levels and percentages to match their risk management and profit-taking preferences. For example, in highly volatile markets, traders might set wider take profit levels with smaller percentages at each level to capture larger price movements.
The normalization method and the incremental factor can be fine-tuned to adjust the sensitivity of the SuperTrend Polyfactor Oscillator, making the strategy more responsive to minor market shifts or more focused on significant trends.
█ Default Settings
The default settings of the strategy are carefully chosen to provide a balanced approach between risk management and profit potential. Here is a breakdown of the default settings and their effects on performance:
1. Indicator Length (10): This parameter controls the lookback period for the ATR calculation. A shorter length makes the strategy more sensitive to recent price movements, potentially generating more signals. A longer length smooths out the ATR, reducing sensitivity but filtering out noise.
2. Starting Factor (0.618): This is the initial multiplier used in the SuperTrend calculation. A lower starting factor makes the SuperTrend bands closer to the price, generating more frequent trend changes. A higher starting factor places the bands further away, filtering out minor fluctuations.
3. Increment Factor (0.382): This parameter controls how much the factor increases with each iteration of the SuperTrend calculation. A smaller increment factor results in more gradual changes in sensitivity, while a larger increment factor creates a wider range of sensitivity across the iterations.
4. Normalization Method (None): The default is no normalization, meaning the raw deviations are used. Normalization methods like Max-Min or Absolute Sum can make the deviations more consistent across different market conditions, improving the reliability of the oscillator.
5. Take Profit Levels (2%, 8%, 18%): These levels define the thresholds for exiting portions of the position. Lower levels (e.g., 2%) capture smaller profits quickly, while higher levels (e.g., 18%) allow positions to run longer for more significant gains.
6. Take Profit Percentages (30%, 20%, 15%): These percentages determine how much of the position is exited at each take profit level. A higher percentage at the first level locks in more profit early, reducing exposure to market reversals. Lower percentages at higher levels allow for a portion of the position to benefit from extended trends.
Cryptomarket
Market Structure Based Stop LossMarket Structure Based Dynamic Stop Loss
Introduction
The Market Structure Based Stop Loss indicator is a strategic tool for traders designed to be useful in both rigorous backtesting and live testing, by providing an objective, “guess-free” stop loss level. This indicator dynamically plots suggested stop loss levels based on market structure, and the concepts of “interim lows/highs.”
It provides a robust framework for managing risk in both long and short positions. By leveraging historical price movements and real time market dynamics, this indicator helps traders identify quantitatively consistent risk levels while optimizing trade returns.
Legend
This indicator utilizes various inputs to customize its functionality, including "Stop Loss Sensitivity" and "Wick Depth," which dictate how closely the stop loss levels hug the price's highs and lows. The stop loss levels are plotted as lines on the trading chart, providing clear visual cues for position management. As seen in the chart below, this indicator dynamically plots stop loss levels for both long and short positions at every point in time.
A “Stop Loss Table” is also included, in order to enhance precision trading and increase backtesting accuracy. It is customizable in both size and positioning.
Case Study
Methodology
The methodology behind this indicator focuses on the precision placement of stop losses using market structure as a guide. It calculates stop losses by identifying the "lowest close" and the corresponding "lowest low" for long setups, and inversely for short setups. By adjusting the sensitivity settings, traders can tweak the indicator's responsiveness to price changes, ensuring that the stop losses are set with a balance between tight risk control and enough room to avoid premature exits due to market noise. The indicator's ability to adapt to different trading styles and time frames makes it an essential tool for traders aiming for efficiency and effectiveness in their risk management strategies.
An important point to make is the fact that the stop loss levels are always placed within the wicks. This is important to avoid what can be described as a “floating stop loss”. A stop loss placed outside of a wick is susceptible to an outsized degree of slippage. This is because traders always cluster their stop losses at high/low wicks, and a stop loss placed outside of this level will inevitably be caught in a low liquidity cascade or “wash-out.” When price approaches a cluster of stop losses, it is highly probable that you will be stopped out anyway, so it is prudent to attempt to be the trader who gets stopped out first in order to avoid high slippage, and losses above what you originally intended.
// For long positions: stop-loss is slightly inside the lowest wick
float dynamic_SL_Long = lowestClose - (lowestClose - lowestLow) * (1 - WickDepth)
// For short positions: stop-loss is slightly inside the highest wick
float dynamic_SL_Short = highestClose + (highestHigh - highestClose) * (1 - WickDepth)
The percentage depth of the wick in which the stop loss is placed is customisable with the “Wick Depth” variable, in order to customize stop loss strategies around the liquidity of the market a trader is executing their orders in.
PUMP IndicatorsPUMP Indicator Description
★ Supported Markets and Assets
The PUMP indicator is a versatile tool that can be effectively applied to various markets and assets, including:
▶ Korean Stocks: KOSPI, KOSDAQ, etc.
▶ U.S. Stocks: NYSE, NASDAQ, etc.
▶ Cryptocurrencies: Major cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), etc.
▶ Futures: Major futures contracts like gold, silver, crude oil, etc.
▶ ETFs: SPY, QQQ, etc.
★ Indicator Description
The PUMP indicator is designed to analyze price divergence and volatility.
It is provided with minimal representation on the chart, allowing users to use it in conjunction with other indicators, such as classical RSI, TRIX, CCI, ADX, BWI, Bollinger Bands, etc.
Everything displayed on the chart can be turned on or off in the options, allowing users to customize their setup.
The PUMP indicator is based on the concept of the MACD indicator, which calculates the difference between the leading line and the lagging line to generate signals.
GOOD, UP, and CR signals predict price increases.
DOWN and BAD signals predict price decreases.
WARN emphasizes that the buy position is not certain, regardless of price increases or decreases.
Therefore, the PUMP indicator is good to use with other indicators. It visually displays divergence and volatility signals along with the MACD movements below, and users can receive alerts for movements in their interested stocks using the alarm function.
It can be used as an indicator for viewing buy and sell signals, as well as predicting the price flow.
▶ (Drawback) Unlike typical TRIX, RSI, TRIX, CCI, ADX, BWI indicators, which are implemented in a new lower window, the PUMP indicator displays both signals and the leading and lagging lines simultaneously, so it is not implemented in a new window, meaning the baseline may vary depending on the daily chart appearance.
★ The PUMP indicator consists of the following components:
▶ PUMP Indicator Leading and Lagging Lines
PUMP t: Leading line (yellow)
PUMP p: Lagging line (blue)
The MACD displayed at the bottom of the chart calculates the divergence between the PUMP t leading line and the PUMP p lagging line.
▶ EA Formula
The core calculation of the PUMP indicator is as follows:
EA (Exponential Average): 100 * (eavg1 / eavg2)
Where eavg1 is the short-term EMA, and eavg2 is the long-term EMA.
It calculates the divergence of the index.
▶ The PUMP indicator is a fixed indicator (cannot be arbitrarily modified).
▶ Highlights: The method of calculating the interval or number of uses is an important part of the index calculation and is therefore private.
★ Signal Description
The PUMP indicator provides a total of six major signals:
▶ UP Signal: Occurs when the divergence between the MACD PUMP t leading line and PUMP p lagging line narrows, and the divergence of the exponential moving average widens compared to before.
▶ DOWN Signal: Occurs when the MACD PUMP t leading line crosses above the PUMP p lagging line.
▶ GOOD Signal: Represents an UP signal with added volume.
(The GOOD signal is not necessarily better than the UP signal. If a GOOD signal appears in a stock that has sufficiently fallen in price, it helps understand that a rebound has started. Therefore, the GOOD signal is made to find a rebound in stocks that have continuously declined, rather than finding signals in consistently rising prices.)
▶ BAD Signal: Occurs when the PUMP t leading line crosses above the 0 baseline, indicating a potential sell signal.
▶ WARN Signal: A warning signal occurring at high levels, indicating that buying is not recommended (regardless of buy or sell).
▶ CR Signal: Occurs in all sections where the PUMP t leading line crosses below the PUMP p lagging line.
★ Lower MACD Horizontal Baseline
The PUMP indicator provides three horizontal baselines from the MACD indicator for additional analysis:
▶ Pump H
▶ PUMP M
▶ PUMP L
It visually provides the divergence of the lower MACD indicator for rising and falling changes, with the default set to 0, and users can change the numbers in the options as needed.
★ Moving Averages
The PUMP indicator provides three basic moving averages:
▶ Buzz 7: 7-day moving average
▶ Buzz 26: 26-day moving average
▶ Buzz 120: 120-day moving average
The number of moving averages is fixed, but users can use them in conjunction with the moving averages provided by TradingView as needed.
★ Alert Function
Using the Alert function of TradingView, you can set alerts for various signals generated by the PUMP indicator.
▶ GOOD Signal Alert
▶ UP Signal Alert
▶ CR Signal Alert
▶ DOWN Signal Alert
▶ BAD Signal Alert
▶ WARN Signal Alert
★ Usage
1. The PUMP indicator is not focused on buy and sell signals but calculates the current price movement and divergence and is designed to express it through MACD leading and lagging lines and signals.
2. The PUMP indicator can be used alone or in conjunction with other indicators for technical analysis.
3. You can analyze buy and sell using the signals of the PUMP indicator along with fundamental analysis, such as news, issues, national policies, company profits, and sales increases.
4. The MACD leading and lagging lines at the bottom of the chart move inversely to the price, ensuring that the PUMP indicator does not interfere when used with other indicators.
5. You can receive real-time alerts using the alarm function.
Below, we attach pictures to help users understand.
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PUMP 인디케이터 설명(한글)
★ 지원되는 시장 및 자산
PUMP 표시기는 다음과 같은 다양한 시장 및 자산에 효과적으로 적용할 수 있는 다용도 도구입니다:
▶ 한국주식: KOSPI, KOSDAQ 등.
▶ 미국주식: NYSE, NASDAQ 등.
▶ 암호화폐: 비트코인(BTC), 이더리움(ETH) 등 주요 암호화폐.
▶ 선물 : 금, 은, 원유 등 주요 선물 계약.
▶ 상장지수펀드(ETF) : SPY, QQQ 등.
★ 지표 설명
PUMP 지표는 가격 이격과 변동성을 분석하도록 설계되었습니다.
사용자가 만든 지표 또는 고전 RSI, TRIX, CCI, ADX, BWI, Bollinger Bands 등과 함께 사용할 수 있게 차트에 최소한의 표현으로 제공됩니다.
그리고 차트에 표현되는 모든 것들을 옵션에서 on / off 가능하게 하였기에 사용자가 커스텀 할 수 있게 하였습니다.
PUMP 지표 신호를 생성하기 위해 선행 라인과 후행 라인 간의 차이를 계산하는 MACD 지표의 개념을 기반으로 합니다.
GOOD, UP, CR 신호는 가격 상승을 예측합니다.
DOWN, BAD 신호는 가격 하락을 예측합니다.
WARN은 가격 상승과 하락에 관계없이, 매수 자리는 확실히 아님을 강조한 신호입니다.
그러므로 PUMP 지표는 다른 지표와 함께 사용하기 좋고, 이격과 변동성을 신호와 하단 MACD 움직임을 눈으로 볼 수 있으며, 알람 기능을 활용하여 관심 있는 종목의 움직임을 알람으로 받아 볼 수 있는 지표입니다.
매수와 매도를 보는 지표로 사용할 수 있으며, 가격의 흐름을 예상하는 지표로 사용할 수 있습니다.
▶ (단점) 보통의 TRIX, RSI, TRIX, CCI, ADX, BWI 지표들은 하단의 새로운 창에서 구현됩니다. 하지만 PUMP 지표는 신호와 하단 선행과 후행을 동시에 표현하기 때문에 새로운 창에서 구현되지 않기에 기준 축이 일봉의 모습에 따라 달라질 수 있습니다.
★ PUMP 지표는 다음과 같은 구성요소로 구성됩니다
▶ PUMP 지표 선행과 후행
PUMP t : 선행라인 (노란색)
PUMP p : 후행라인 (파란색)
차트 하단에 나타나는 MACD는 PUMP t선행라인과 PUMP p 후행라인의 이격도를 계산합니다.
▶ EA공식
PUMP 지표의 핵심 계산식은 다음과 같습니다:
EA(지수평균): 100 * (eavg1 / eavg2)
여기서 eavg1은 단기 EMA이고 eavg2는 장기 EMA입니다.
지수의 이격도를 계산합니다.
▶ PUMP 지표는 고정 지표입니다. (임의 수정 불가)
▶ 강조 : 이격의 계산법이나 사용하는 숫자는 지표 계산의 중요한 부분이므로 비공개입니다.
★ 신호 설명
PUMP 표시등은 총 6개의 주요 신호를 제공합니다:
▶ UP 신호: MACD PUMP t 선행과 PUMP p 후행의 이격이 줄어들 때, 지수 이동 평균의 이격도가 이전 보다 넓어지면 발생합니다.
▶ DOWN 신호: MACD PUMP t 선행이 PUMP p 후행을 상향 교차할 때 발생합니다.
▶ GOOD 신호: 거래량이 추가된 UP 신호를 나타냅니다.
(GOOD 신호가 UP 신호보다 좋다기 보다, 충분히 가격 하락한 종목에서 GOOD 신호가 나온다면 반등이 시작되는 것을 이해할 수 있게 만든 지표입니다. 그러므로 GOOD 신호는 가격이 꾸준히 상승하는 곳에서 신호를 찾기보다, 지속 하락하다 반등을 찾는 신호로 만들었습니다.)
▶ BAD 신호: PUMP t 선행이 0 기준선 이상으로 교차할 때 발생하며, 이는 잠재적인 판매 신호를 나타냅니다.
▶ 경고 신호: 높은 수준에서 발생하는 경고 신호로, 매수가 권장되지 않음을 나타냅니다(매수, 매도와 무관함).
▶ CR 신호: PUMP t 선행 라인이 PUMP p 후행 라인 아래로 교차하는 모든 구간에서 발생합니다.
★ 하단 MACD 가로 기준선
PUMP 표시기는 추가 분석을 위해 MACD 지표에서 3가지 가로 기준을 제공합니다:
▶ pump H
▶ PUMP M
▶ PUMP L
하단의 MACD 지표의 이격도를 상승 및 하강의 변화를 시각적으로 기준을 만들 수 있게 제공하며, 기본은 0으로 제공하고, 사용자의 필요에 따라 옵션에서 숫자를 변경할 수 있게 하였습니다.
★ 이동 평균
PUMP 표시기는 세 가지 기본 이동 평균을 제공 합니다:
▶ Buzz 7: 7일 이동 평균
▶ Buzz 26: 26일 이동 평균
▶ Buzz 120 : 120일 이동 평균
이동 평균의 수는 고정되어 있지만, 사용자는 필요에 따라 TradingView에서 제공하는 이동 평균과 함께 사용할 수 있습니다.
★ 알림 기능
TradingView의 Alert 기능을 사용하여 PUMP 지표 생성되는 다양한 신호에 대한 Alert를 설정할 수 있습니다.
▶ GOOD 신호 알림
▶ UP 신호 알림
▶ CR 신호 알림
▶ DOWN 신호 알림
▶ BAD 신호 알림
▶ WARN 신호 알림
★ 사용법
1.PUMP 지표는 매수와 매도에 중점을 둔 지표가 아니며 현재 가격의 움직임과 이격도를 계산하며 MACD 선행과 후행 그리고 신호로 표현하기 위해 만들어진 지표입니다.
2. PUMP 지표는 단일로 사용할 수 있고, 또는 다른 지표와 함께 기술적분석으로 사용할 수 있습니다.
3. 뉴스와 이슈, 국가의 정책, 회사의 이익, 매출의 상승 등 기본적분석과 함께 PUMP 지표의 신호를 이용하여 매수와 매도 분석을 할 수 있습니다.
4. 차트 하단의 MACD 선행과 후행은 가격의 움직임을 반대로 움직이며, 가격과 반대로 움직이게 함으로써 다른 지표와 함께 사용하였을 때, PUMP 지표가 방해가 되지 않게 하였습니다.
5. 알람을 사용하여 실시간으로 알람을 받아 보실 수 있습니다.
아래 사진을 첨부하여 사용자 이해를 돕습니다.
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UP신호는 이격을
▶ The UP signal indicates horizontal divergence.
CR신호는 선행이 후행을 아래로 돌파
▶ The CR signal indicates vertical divergence when the leading line crosses below the lagging line.
WARN 신호를 확인
▶ Check the WARN signal.
BAD와 DOWN 신호
▶ BAD and DOWN signals.
PUMP 지표의 기준 3개
3 criteria for PUMP indicators
따로 그림을 그리지 않은 차트
▶ A chart without separate drawings.
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다른 지표와 + 조합
+ Combination with other indicators
[Suitable Hope] Crypto Upside Model 3.0The "Crypto Upside Model 3.0" indicator dynamically calculates the potential price of any cryptocurrency based on various percentages of Ethereum or Bitcoin's market capitalization.
By fetching and analyzing marketcap data from TradingView sources, it allows traders to visualize potential price targets if their chosen cryptocurrency reaches specific market dominance levels. This tool is designed for daily timeframe analysis and can be used to set informed price expectations and strategic investment goals, providing valuable insights for long-term investment planning.
Why using the Crypto Upside Model 3.0?
Strategic Planning: Helps traders and investors set realistic price targets and investment goals by visualizing potential market cap scenarios.
Informed Decision-Making: Provides a data-driven approach to understanding how a cryptocurrency might perform relative to major assets like Bitcoin and Ethereum.
Customizable Analysis: Allows users to choose different comparison assets (ETH or BTC) and visualize various market cap dominance percentages, offering tailored insights.
Daily Timeframe Focus: Ideal for swing traders and long-term investors who operate on a daily analysis timeframe, providing relevant and actionable data.
Bull Markets: Identify potential price targets if your cryptocurrency's market cap increases significantly.
Bear Markets: Assess how much value could be retained relative to major cryptocurrencies.
Strategic Entry/Exit Points: Use the visualized targets to plan entry or exit points in your trading strategy.
Comparative Advantage
Dynamic Adaptation: Unlike fixed indicators, this tool adapts to any active chart, making it versatile for multiple cryptocurrencies.
Market Cap Insights: Provides a unique perspective by linking price targets to market cap dominance, a critical factor in the crypto market.
User Instructions
Setup: Add the " Upside Model 3.0" indicator to your TradingView chart.
Configuration: Use the input settings to select the comparison cryptocurrency (ETH or BTC) and enable the desired market cap percentage plots.
Analysis: The indicator will display potential price targets based on the selected market cap percentages, providing a visual guide for setting price expectations.
Limitations
Marketcap Data Availability: The indicator relies on marketcap data from TradingView, which may not be available for all cryptocurrencies. If the data is unavailable, the indicator will not function for that asset. This tool is more likely to work with older, established cryptocurrencies, as marketcap data for newer cryptocurrencies may not yet be available.
Daily Timeframe Restriction: The indicator is designed to work exclusively on the daily timeframe, limiting its applicability for intraday trading.
Assumptions of Market Dynamics: The calculations assume a direct correlation between market dominance and price, which may not account for other market dynamics and external factors influencing prices.
Data Accuracy: The accuracy of the indicator depends on the reliability of the data provided by TradingView, which may sometimes experience delays or inaccuracies.
Currently available cryptocurrencies: Bitcoin, Ethereum, Solana, Binance Coin, Cardano, Ripple, Polkadot, Avalanche, Chainlink, Litecoin, Dogecoin, Terra, Uniswap, VeChain, Stellar, Internet Computer, Hedera, Filecoin, Monero, Aave, TRON, NEAR Protocol, Compound, Maker,... For all compatible cryptocurrencies, please consult CRYPTOCAP's documentation.
Final notes
Although various sources ask a payment or user data for similar kind of private indicators, this one is entirely free and open source. "Uncanny" isn't it? I hope this indicator will provide you value. Feel free to leave a message if you have any questions or constructive feedback.
Examples of how I use this indicator
When using ETH's historical price as a reference compared to Bitcoin's marketcap, we can notice that price generally has been held between the +-30% and 50% lines of BTC's marketcap. If history is repeating again, we can expect major resistances around the 50% looking ahead into the future. This for me would be a great area to potentially reduce my ETH spot position.
When using SOL's historical price action, we can notice that the 15% line of ETH's marketcap has been a top in the previous cycle. Today SOL (July 2024), is back at this level. Could this be a top again or could price break this 15% level and head perhaps towards 30% which currently sits around $260? Time will tell.
These are 2 simple example of how I interpret the data. I'm keen to hear what other findings with other pairs you can find.
CryptoLibrary "Crypto"
This Library includes functions related to crytocurrencies and their blockchain
btcBlockReward(t)
Delivers the BTC block reward for a specific date/time
Parameters:
t (int) : Time of the current candle
Returns: blockRewardBtc
ATH/ATL Tracker [LuxAlgo]The ATH/ATL Tracker effectively displays changes made between new All-Time Highs (ATH)/All-Time Lows (ATL) and their previous respective values, over the entire history of available data.
The indicator shows a histogram of the change between a new ATH/ATL and its respective preceding ATH/ATL. A tooltip showing the price made during a new ATH/ATL alongside its date is included.
🔶 USAGE
By tracking the change between new ATHs/ATLs and older ATHs/ATLs, traders can gain insight into market sentiment, breadth, and rotation.
If many stocks are consistently setting new ATHs and the number of new ATHs is increasing relative to old ATHs, it could indicate broad market participation in a rally. If only a few stocks are reaching new ATHs or the number is declining, it might signal that the market's upward momentum is decreasing.
A significant increase in new ATHs suggests optimism and willingness among investors to buy at higher prices, which could be considered a positive sentiment. On the other hand, a decrease or lack of new ATHs might indicate caution or pessimism.
By observing the sectors where stocks are consistently setting new ATHs, users can identify which sectors are leading the market. Sectors with few or no new ATHs may be losing momentum and could be identified as lagging behind the overall market sentiment.
🔶 DETAILS
The indicator's main display is a histogram-style readout that displays the change in price from older ATH/ATLs to Newer/Current ATH/ATLs. This change is determined by the distance that the current values have overtaken the previous values, resulting in the displayed data.
The largest changes in ATH/ATLs from the ticker's history will appear as the largest bars in the display.
The most recent bars (depending on the selected display setting) will always represent the current ATH or ATL values.
When determining ATH & ATL values, it is important to filter out insignificant highs and lows that may happen constantly when exploring higher and lower prices. To combat this, the indicator looks to a higher timeframe than your chart's timeframe in order to determine these more significant ATHs & ATLs.
For Example: If a user was on a 1-minute chart and 5 highs-new highs occur across 5 adjacent bars, this has the potential to show up as 5 new ATHs. When looking at a higher timeframe, 5 minutes, only the highest of the 5 bars will indicate a new ATH. To assist with this, the indicator will display warnings in the dashboard when a suboptimal timeframe is selected as input.
🔹 Dashboard
The dashboard displays averages from the ATH/ATL data to aid in the anticipation and expectations for new ATH/ATLs.
The average duration is an average of the time between each new ATH/ATL, in this indicator it is calculated in "Days" to provide a more comprehensive understanding.
The average change is the average of all change data displayed in the histogram.
🔶 SETTINGS
Duration: The designated higher timeframe to use for filtering out insignificant ATHs & ATLs.
Order: The display order for the ATH/ATL Bars, Options are to display in chronological (oldest to newest) or reverse chronological order (newest to oldest).
Bar Width: Sets the width for each ATH/ATL bar.
Bar Spacing: Sets the # of empty bars in between each ATH/ATL bar.
Dashboard Settings: Parameters for the dashboard's size and location on the chart.
Cosine Kernel Regressions [QuantraSystems]Cosine Kernel Regressions
Introduction
The Cosine Kernel Regressions indicator (CKR) uses mathematical concepts to offer a unique approach to market analysis. This indicator employs Kernel Regressions using bespoke tunable Cosine functions in order to smoothly interpret a variety of market data, providing traders with incredibly clean insights into market trends.
The CKR is particularly useful for traders looking to understand underlying trends without the 'noise' typical in raw price movements. It can serve as a standalone trend analysis tool or be combined with other indicators for more robust trading strategies.
Legend
Fast Trend Signal Line - This is the foreground oscillator, it is colored upon the earliest confirmation of a change in trend direction.
Slow Trend Signal Line - This oscillator is calculated in a similar manner. However, it utilizes a lower frequency within the cosine tuning function, allowing it to capture longer and broader trends in one signal. This allows for tactical trading; the user can trade smaller moves without losing sight of the broader trend.
Case Study
In this case study, the CKR was used alongside the Triple Confirmation Kernel Regression Oscillator (KRO)
Initially, the KRO indicated an oversold condition, which could be interpreted as a signal to enter a long position in anticipation of a price rebound. However, the CKR’s fast trend signal line had not yet confirmed a positive trend direction - suggesting that entering a trade too early and without confirmation could be a mistake.
Waiting for a confirmed positive trend from the CKR proved beneficial for this trade. A few candles after the oversold signal, the CKR's fast trend signal line shifted upwards, indicating a strong upward momentum. This was the optimal entry point suggested by the CKR, occurring after the confirmation of the trend change, which significantly reduced the likelihood of entering during a false recovery or continuation of the downtrend.
This is one of the many uses of the CKR - by timing entries using the fast signal line , traders could avoid unnecessary losses by preventing premature entries.
Methodology
The methodology behind CKR is a multi-layered approach and utilizes many ‘base’ indicators.
Relative Strength Index
Stochastic Oscillator
Bollinger Band Percent
Chande Momentum Oscillator
Commodity Channel Index
Fisher Transform
Volume Zone Oscillator
The calculated output from each indicator is standardized and scaled before being averaged. This prevents any single indicator from overpowering the resulting signal.
// ╔════════════════════════════════╗ //
// ║ Scaling/Range Adjustment ║ //
// ╚════════════════════════════════╝ //
RSI_ReScale (_res ) => ( _res - 50 ) * 2.8
STOCH_ReScale (_stoch ) => ( _stoch - 50 ) * 2
BBPCT_ReScale (_bbpct ) => ( _bbpct - 0.5 ) * 120
CMO_ReScale (_chandeMO ) => ( _chandeMO * 1.15 )
CCI_ReScale (_cci ) => ( _cci / 2 )
FISH_ReScale (_fish1 ) => ( _fish1 * 30 )
VZO_ReScale (_VP, _TV ) => (_VP / _TV) * 110
These outputs are then fed into a customized cosine kernel regression function, which smooths the data, and combines all inputs into a single coherent output.
// ╔════════════════════════════════╗ //
// ║ COSINE KERNEL REGRESSIONS ║ //
// ╚════════════════════════════════╝ //
// Define a function to compute the cosine of an input scaled by a frequency tuner
cosine(x, z) =>
// Where x = source input
// y = function output
// z = frequency tuner
var y = 0.
y := math.cos(z * x)
Y
// Define a kernel that utilizes the cosine function
kernel(x, z) =>
var y = 0.
y := cosine(x, z)
math.abs(x) <= math.pi/(2 * z) ? math.abs(y) : 0. // cos(zx) = 0
// The above restricts the wave to positive values // when x = π / 2z
The tuning of the regression is adjustable, allowing users to fine-tune the sensitivity and responsiveness of the indicator to match specific trading strategies or market conditions. This robust methodology ensures that CKR provides a reliable and adaptable tool for market analysis.
Bitcoin Futures vs. Spot Tri-Frame - Strategy [presentTrading]Prove idea with a backtest is always true for trading.
I developed and open-sourced it as an educational material for crypto traders to understand that the futures and spot spread may be effective but not be as effective as they might think. It serves as an indicator of sentiment rather than a reliable predictor of market trends over certain periods. It is better suited for specific trading environments, which require further research.
█ Introduction and How it is Different
The "Bitcoin Futures vs. Spot Tri-Frame Strategy" utilizes three different timeframes to calculate the Z-Score of the spread between BTC futures and spot prices on Binance and OKX exchanges. The strategy executes long or short trades based on composite Z-Score conditions across the three timeframes.
The spread refers to the difference in price between BTC futures and BTC spot prices, calculated by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges.
BTCUSD 1D L/S Performance
█ Strategy, How It Works: Detailed Explanation
🔶 Calculation of the Spread
The spread is the difference in price between BTC futures and BTC spot prices. The strategy calculates the spread by taking a weighted average of futures prices from multiple exchanges (Binance and OKX) and subtracting a weighted average of spot prices from the same exchanges. This spread serves as the primary metric for identifying trading opportunities.
Spread = Weighted Average Futures Price - Weighted Average Spot Price
🔶 Z-Score Calculation
The Z-Score measures how many standard deviations the current spread is from its historical mean. This is calculated for each timeframe as follows:
Spread Mean_tf = SMA(Spread_tf, longTermSMA)
Spread StdDev_tf = STDEV(Spread_tf, longTermSMA)
Z-Score_tf = (Spread_tf - Spread Mean_tf) / Spread StdDev_tf
Local performance
🔶 Composite Entry Conditions
The strategy triggers long and short entries based on composite Z-Score conditions across all three timeframes:
- Long Condition: All three Z-Scores must be greater than the long entry threshold.
Long Condition = (Z-Score_tf1 > zScoreLongEntryThreshold) and (Z-Score_tf2 > zScoreLongEntryThreshold) and (Z-Score_tf3 > zScoreLongEntryThreshold)
- Short Condition: All three Z-Scores must be less than the short entry threshold.
Short Condition = (Z-Score_tf1 < zScoreShortEntryThreshold) and (Z-Score_tf2 < zScoreShortEntryThreshold) and (Z-Score_tf3 < zScoreShortEntryThreshold)
█ Trade Direction
The strategy allows the user to specify the trading direction:
- Long: Only long trades are executed.
- Short: Only short trades are executed.
- Both: Both long and short trades are executed based on the Z-Score conditions.
█ Usage
The strategy can be applied to BTC or Crypto trading on major exchanges like Binance and OKX. By leveraging discrepancies between futures and spot prices, traders can exploit market inefficiencies. This strategy is suitable for traders who prefer a statistical approach and want to diversify their timeframes to validate signals.
█ Default Settings
- Input TF 1 (60 minutes): Sets the first timeframe for Z-Score calculation.
- Input TF 2 (120 minutes): Sets the second timeframe for Z-Score calculation.
- Input TF 3 (180 minutes): Sets the third timeframe for Z-Score calculation.
- Long Entry Z-Score Threshold (3): Defines the threshold above which a long trade is triggered.
- Short Entry Z-Score Threshold (-3): Defines the threshold below which a short trade is triggered.
- Long-Term SMA Period (100): The period used to calculate the simple moving average for the spread.
- Use Hold Days (true): Enables holding trades for a specified number of days.
- Hold Days (5): Number of days to hold the trade before exiting.
- TPSL Condition (None): Defines the conditions for taking profit and stop loss.
- Take Profit (%) (30.0): The percentage at which the trade will take profit.
- Stop Loss (%) (20.0): The percentage at which the trade will stop loss.
By fine-tuning these settings, traders can optimize the strategy to suit their risk tolerance and trading style, enhancing overall performance.
Multi ETH Rolling APY Calculator [presentTrading]This one is for SEC paves way for Ethereum ETFs in boost for crypto!
█ Introduction and How it is Different
The "Multi ETH Rolling APY Calculator" is a sophisticated Pine Script tool designed to analyze the annualized difference between Ethereum (ETH) spot and futures prices. This tool is essential for identifying arbitrage opportunities and assessing market sentiment, offering traders invaluable insights into market dynamics. By calculating the premium or discount of futures contracts relative to the spot price and annualizing this figure based on the time until each contract's expiration, the Multi ETH Rolling APY Calculator provides a clear view of potential profit margins and market trends.
Unlike traditional trading indicators that focus solely on price movements or technical patterns, this calculator delves deeper into the futures market, providing a dual-purpose tool. It not only helps in spotting arbitrage opportunities but also serves as a gauge for the emotional state of the market, thereby offering a more comprehensive analysis of market conditions. This dual functionality sets it apart, making it a must-have for traders looking to navigate the volatile cryptocurrency trading landscape effectively.
Historical backtesting has revealed that Bitcoin's Rolling APY can serve as a robust indicator of market sentiment:
- Below 0%: Often indicates panic or 'end-of-world' scenarios.
- 0-5%: Signifies extreme market fear.
- 5-10%: Reflects a calm market environment.
- 10-15%: Suggests a moderately warm market.
- 15-20%: Indicates an overheated market.
- **Above 20%: Signals FOMO (fear of missing out).
█ Strategy, How it Works: Detailed Explanation
The Multi ETH Rolling APY Calculator employs a systematic approach to derive its insights. The process is broken down into several steps, each contributing to the overall analysis:
🔶 Data Fetching: The script first fetches the necessary data, including the closing prices of Ethereum's spot market and selected futures contracts. These futures contracts are typically set to expire at different dates, providing a broad perspective on market expectations over time.
🔶 Time and Expiration: The tool takes into account the current time and the expiration dates of the futures contracts. This helps in calculating the number of days remaining until each contract's expiration.
🔶 Premium Calculations: The premium or discount of each futures contract relative to the spot price is computed. This is done by subtracting the spot price from the futures price and then dividing the result by the spot price. This calculation gives a percentage that represents the premium or discount.
🔶 Annualized Percentage Yield (APY) Calculations: The calculated premium or discount is then annualized based on the number of days remaining until the contract's expiration. This involves multiplying the premium or discount by the factor (365 / days remaining) to annualize the figure. If the user chooses not to annualize the numbers, this step is skipped.
🔶 Plotting Results: The annualized yields are then plotted on a chart, allowing traders to visualize the potential returns from different futures contracts. The plots are color-coded for easy differentiation and quick analysis.
By following this structured approach, the Multi ETH Rolling APY Calculator provides traders with clear, actionable insights into market dynamics and potential arbitrage opportunities.
█ Trade Direction
While this tool does not provide direct trading signals, it informs traders about potential arbitrage opportunities and the prevailing market sentiment. Traders can leverage this data to make strategic decisions, aligning long or short positions with the anticipated market movements and arbitrage conditions.
█ Usage
By inputting specific parameters related to their market analysis, traders can monitor discrepancies in Bitcoin’s pricing across different timelines, which is especially beneficial for those involved in derivatives trading, arbitrage, and sentiment analysis.
█ Default Settings
- Resolution: Controls the frequency of data (default is daily).
- Show numbers in annual: Determines whether APY is displayed on an annual basis.
- Base Symbol and Future Symbols: Specify the spot and futures markets for analysis.
Crypto Realized Profits/Losses Extremes [AlgoAlpha]🌟🚀 Introducing the Crypto Realized Profits/Losses Extremes Indicator by AlgoAlpha 🚀🌟
Unlock the potential of cryptocurrency markets with our cutting-edge On-Chain Pine Script™ indicator, designed to highlight extreme realized profit and loss zones! 🎯📈
Key Features:
✨ Realized Profits/Losses Calculation: Uses real-time data from the blockchain to monitor profit and loss realization events.
📊 Multi-Crypto Compatibility: The Indicator is compatible on other Crypto tickers besides Bitcoin.
⚙️ Customizable Sensitivity: Adjust the look-back period, normalization period, and deviation thresholds to tailor the indicator to your trading style.
🎨 Visual Enhancements: Choose from a variety of colors for up and down trends, and toggle extreme profit/loss overlay for easy viewing.
🔔 Integrated Alerts: Set up alerts for high and extreme profit or loss conditions, helping you stay ahead of significant market movements.
🔍 How to Use:
🛠 Add the Indicator: Add the indicator to favorites. Customize settings like period lengths and deviation thresholds according to your needs.
📊 Market Analysis: Monitor the main oscillator and the bands to understand current profit and loss extremes in the market. When the oscillator is at the upper band, this means that the market is doing really well and traders/investors will be likely to take profit and cause a reversal. The opposite is true when the oscillator reaches the lower band. The main oscillator can also be used for trend analysis.
🔔 Set Alerts: Configure alerts to notify you when the market enters a zone of high profit or loss, or during trend changes, enabling timely decisions without constant monitoring.
How It Works:
The indicator calculates a normalized area under the RSI curve applied on on-chain data regarding the number of wallets in profit. It employs a custom "src" variable that aggregates data from the blockchain about profit and loss addresses, adapting to intraday or longer timeframes as needed. The main oscillator plots this normalized area, while the upper and lower bands are plotted based on a deviation metric to identify extreme conditions. Colored fills between these bands visually denote these zones. For interaction, the indicator plots bubbles for extreme profits or losses and provides optional bar coloring to reflect the current market trend.
🚀💹 Enjoy a comprehensive, customizable, and visually engaging tool that helps you stay ahead in the fast-paced crypto market!
Crypto Liquidation Heatmap [LuxAlgo]The Crypto Liquidation Heatmap tool offers real-time insights into the liquidations of the top cryptocurrencies by market capitalization, presenting the current state of the market in a visually accessible format. Assets are sorted in descending order, with those experiencing the highest liquidation values placed at the top of the heatmap.
Additional details, such as the breakdown of long and short liquidation values and the current price of each asset, can be accessed by hovering over individual boxes.
🔶 USAGE
The crypto liquidation heatmap tool provides real-time insights into liquidations across all timeframes for the top 29 cryptocurrencies by market capitalization. The assets are visually represented in descending order, prioritizing assets with the highest liquidation values at the top of the heatmap.
Different colors are used to indicate whether long or short liquidations are dominant for each asset. Green boxes indicate that long liquidations surpass short liquidations, while red boxes indicate the opposite, with short liquidations exceeding long liquidations.
Hovering over each box provides additional details, such as the current price of the asset, the breakdown of long and short liquidation values, and the duration for the calculated liquidation values.
🔶 DETAILS
🔹Crypto Liquidation
Crypto liquidation refers to the process of forcibly closing a trader's positions in the cryptocurrency market. It occurs when a trader's margin account can no longer support their open positions due to significant losses or a lack of sufficient margin to meet the maintenance requirements. Liquidations can be categorized as either a long liquidation or a short liquidation.
A long liquidation occurs when long positions are being liquidated, typically due to a sudden drop in the price of the asset being traded. Traders who were bullish on the asset and had opened long positions will face losses as the market moves against them.
On the other hand, a short liquidation occurs when short positions are being liquidated, often triggered by a sudden spike in the price of the asset. Traders who were bearish on the asset and had opened short positions will face losses as the market moves against them.
🔹Liquidation Data
It's worth noting that liquidation data is not readily available on TradingView. However, we recognize the close correlation between liquidation data, trading volumes, and asset price movements. Therefore, this script analyzes accessible data sources, extracts necessary information, and offers an educated estimation of liquidation data. It's important to emphasize that the presented data doesn't reflect precise quantitative values of liquidations. Traders and analysts should instead focus on observing changes over time and identifying correlations between liquidation data and price movements.
🔶 SETTINGS
🔹Cryptocurrency Asset List
It is highly recommended to select instruments from the same exchange with the same currency to maintain proportional integrity among the chosen assets, as different exchanges may have varying trading volumes.
Supported currencies include USD, USDT, USDC, USDP, and USDD. Remember to use the same currency when selecting assets.
List of Crypto Assets: The default options feature the top 29 cryptocurrencies by market capitalization, currently listed on the Binance Exchange. Please note that only crypto assets are supported; any other asset type will not be processed or displayed. To maximize the utility of this tool, it is crucial to heed the warning message displayed above.
🔹Liquidation Heatmap Settings
Position: Specifies the placement of the liquidation heatmap on the chart.
Size: Determines the size of the liquidation heatmap displayed on the chart.
🔶 RELATED SCRIPTS
Liquidations-Meter
Liquidation-Estimates
Liquidation-Levels
Trend Regression Kernel [IkkeOmar]Kernel by @jdehorty huge shoutout to him! This is only an idea for how I use it when trading
All credit for the kernel goes to him, I did not make the kernel! I don't know how to make it more clear.
I use this to assist with top-down analysis.
timeframe I want to trade : timeframe to analyse with white noise and kernel:
1m : 1H
5m : 2H
15m : 4H
1H : 1D
In the chart you see that I have the 1H open, I use the white noise at a "lower setting length" (55 in this case), I change the source of to be the kernel on the higher timeframe. When a new trend is detected by the White noise I wait for price to retest the kernel before building a position. Another case described below:
Here i use the adaptive MCVF (I have made this free for everyone on TradingView) to buy when price is below the kernel while the trend for the white noise is bullish .
Notice that the Kernel is set on the 4H timeframe! The source of the white noise is the kernel!
Here is an example in a bearish trend:
Notice, I am on the 5m chart, kernel uses the 2H chart and the source of the white noise is the kernel.
I use the adaptive MCVF to help me get entries AFTER the first touch of the kernel.
Mandatory code explanation, with respect to the house rules:
Input settings:
Input Settings:
The script provides various input parameters to customize the indicator:
src: The source of price data, defaulted to closing prices.
h, r, x_0: Parameters for Kernel 1.
h2, r2, x_2: Parameters for Kernel 2.
Kernel Regression Functions:
Two functions kernel_regression1 and kernel_regression2 are defined to perform kernel regression calculations.
These functions estimate the trend using the Nadaraya-Watson kernel non-parametric regression method.
They take the source data (_src), the size of the data series (_size), and the lookback window (_h) as inputs.
They iterate over the data series and calculate the weighted sum of the values based on the specified kernel parameters.
The result is divided by the cumulative weight to obtain the estimated value.
Estimations:
The kernel_regression1 and kernel_regression2 functions are called with the respective parameters to estimate trends (yhat1 and yhat2).
Buy and Sell Signals:
Buy and sell signals are generated based on crossover and crossunder conditions between the two trend estimates (yhat1 and yhat2).
buySignal is true when yhat1 crosses above yhat2.
SellSignal is true when yhat1 crosses below yhat2.
Plotting:
The average of the two trend estimates (yhat1 and yhat2) is calculated and plotted.
The color of the plot is determined based on whether yhat1 is greater than yhat2, less than yhat2, or equal to yhat2.
Buy and sell signals are plotted using triangle shapes below and above bars, respectively.
Alerts:
Alert conditions are set based on buy and sell signals. Alerts are triggered when a crossover (long signal) or crossunder (short signal) occurs.
The alerts include information about the signal type, symbol, and price.
It's important to mention that the buy and sell signals from the indicator is very discretionary, I rarely use them, and if I do it's if they are in confluence with a correction i am biased towards or if it has confluence with some of my other systems.
The adaptive MCVF and White noise is free for everyone on TradingView, linked below:)
Huge shoutout to @jdehorty, original kernel below:
Spot Martingale KuCoin - The Quant ScienceINTRODUCTION
Backtesting software of the Spot Martingale algorithm offered by the KuCoin exchange.
This script replicates the logic used by the KuCoin bot and is useful for analyzing strategy on any cryptocurrency historical series.
It's not intended as an automatic trading algorithm and does not offer the possibility of automatic order execution.
The trader will use this software exclusively to research the best parameters with which to work on KuCoin.
LOGIC OF EXECUTION
The execution of orders is composed as follows:
1) Start Martingale: initial order
2) Martingale-Number: orders following Start Martingale
(A) The software is designed and developed to replicate trading without taking into account technical indicators or particular market conditions. The Initial Order (Start Martingale) will be executed immediately the close of the previous Martingale when the balance of market orders is zero. It will use the capital set in the Properties section for the initial order.
(B) After the first order, the software will open new orders as the price decreases. For orders following Start Martingale, the initial capital, multiplier, and number of orders in the exponential growth context are considered. The multiplier is the factor that determines the proportional increase in capital with each new order. The number of orders, indicates how many times the multiplier is applied to increase the investment.
Example
To find out the capital used in Martingale order number 5, with a Multiple For Position Increase equal to 2 and a starting capital of $100, the formula will be as follows:
Martingale Order = ($100 * (2 * 2 * 2 * 2 * 2)) = $100 * 32 = $3.200
(C) A multiplier is used for each new order that will increase the quantity purchased.
(D) All previously open orders are closed once the take profit is reached.
USER MANUAL
The user interface consists of two main sections:
1. Settings
Percentage Drop for Position Increase (0.1-15%) : percentage distance between Martingale orders. For example, if you set 5% each new order will be opened after a 5% price decrease from the previous one.
Max Position Increases (1-15) : number of Martingale orders to be executed after Start Martingale. For example, if you set 10, up to10 orders will be opened after Start Martingale.
Multiple For Position Increase (1-2x) : capital multiplier. For example, if you set 2 each for each new order, the capital involved will be doubled, order by order.
Take Profit Percentage (0.5-1000%) : percentage take profit, calculated on the average entry price.
2. Date Range Backtesting
The Date Range Backtesting section adjusts the analysis period. The user can easily adjust the UI parameters, and automatically the software will update the data.
LIMITATIONS OF THE MODEL
Although the Martingale model is widely used in position management, even this model has limitations and is subject to real risks during particular market conditions. Knowing these conditions will help you understand which asset is best to use the strategy on.
The main risks in adopting this automatic strategy are 2:
1) The price falls below our last order.
It happens during periods of strong bear-market in which the price collapses abruptly without experiencing any pullback. In this case the algorithm will enter a drawdown phase and the strategy will become a loser. The trader will then have to consider whether to wait for a price recovery or to incur a loss by manually closing the algorithm.
2) The price increases quickly.
It happens during periods of strong bull-market in which the price rises abruptly without experiencing any pullback. In this case the algorithm will not optimize order execution, working only with Start Martingale in the vast majority of trades. Given the exponential nature of the investment, the algorithm will in this case generate a profit that is always less than that of the reference market.
The best market conditions to use this strategy are characterized by high volatility such as correction phases during a bull run and/or markets that exhibit sideways price trends (such as areas of accumulation or congestion where price will generate many false signals).
FEATURES
This script was developed by including features to optimize the user experience.
Includes a dashboard at launch that allows the user to intuitively enter backtesting parameters.
Includes graphical indicator that helps the user analyze the behavior of the strategy.
Includes a date period backtesting feature that allows the user to adjust and choose custom historical periods.
DISCLAIMER
This script was released using parameters researched solely for the BTC/USDT pair, 4H timeframe, traded on the KuCoin Exchange (2017-present). Do not consider this combination of parameters as universal and usable on all assets and timeframes.
Bitcoin Leverage Sentiment - Strategy [presentTrading]█ Introduction and How it is Different
The "Bitcoin Leverage Sentiment - Strategy " represents a novel approach in the realm of cryptocurrency trading by focusing on sentiment analysis through leveraged positions in Bitcoin. Unlike traditional strategies that primarily rely on price action or technical indicators, this strategy leverages the power of Z-Score analysis to gauge market sentiment by examining the ratio of leveraged long to short positions. By assessing how far the current sentiment deviates from the historical norm, it provides a unique lens to spot potential reversals or continuation in market trends, making it an innovative tool for traders who wish to incorporate market psychology into their trading arsenal.
BTC 4h L/S Performance
local
█ Strategy, How It Works: Detailed Explanation
🔶 Data Collection and Ratio Calculation
Firstly, the strategy acquires data on leveraged long (**`priceLongs`**) and short positions (**`priceShorts`**) for Bitcoin. The primary metric of interest is the ratio of long positions relative to the total of both long and short positions:
BTC Ratio=priceLongs / (priceLongs+priceShorts)
This ratio reflects the prevailing market sentiment, where values closer to 1 indicate a bullish sentiment (dominance of long positions), and values closer to 0 suggest bearish sentiment (prevalence of short positions).
🔶 Z-Score Calculation
The Z-Score is then calculated to standardize the BTC Ratio, allowing for comparison across different time periods. The Z-Score formula is:
Z = (X - μ) / σ
Where:
- X is the current BTC Ratio.
- μ is the mean of the BTC Ratio over a specified period (**`zScoreCalculationPeriod`**).
- σ is the standard deviation of the BTC Ratio over the same period.
The Z-Score helps quantify how far the current sentiment deviates from the historical norm, with high positive values indicating extreme bullish sentiment and high negative values signaling extreme bearish sentiment.
🔶 Signal Generation: Trading signals are derived from the Z-Score as follows:
Long Entry Signal: Occurs when the BTC Ratio Z-Score crosses above the thresholdLongEntry, suggesting bullish sentiment.
- Condition for Long Entry = BTC Ratio Z-Score > thresholdLongEntry
Long Exit/Short Entry Signal: Triggered when the BTC Ratio Z-Score drops below thresholdLongExit for exiting longs or below thresholdShortEntry for entering shorts, indicating a shift to bearish sentiment.
- Condition for Long Exit/Short Entry = BTC Ratio Z-Score < thresholdLongExit or BTC Ratio Z-Score < thresholdShortEntry
Short Exit Signal: Happens when the BTC Ratio Z-Score exceeds the thresholdShortExit, hinting at reducing bearish sentiment and a potential switch to bullish conditions.
- Condition for Short Exit = BTC Ratio Z-Score > thresholdShortExit
🔶Implementation and Visualization: The strategy applies these conditions for trade management, aligning with the selected trade direction. It visualizes the BTC Ratio Z-Score with horizontal lines at entry and exit thresholds, illustrating the current sentiment against historical norms.
█ Trade Direction
The strategy offers flexibility in trade direction, allowing users to choose between long, short, or both, depending on their market outlook and risk tolerance. This adaptability ensures that traders can align the strategy with their individual trading style and market conditions.
█ Usage
To employ this strategy effectively:
1. Customization: Begin by setting the trade direction and adjusting the Z-Score calculation period and entry/exit thresholds to match your trading preferences.
2. Observation: Monitor the Z-Score and its moving average for potential trading signals. Look for crossover events relative to the predefined thresholds to identify entry and exit points.
3. Confirmation: Consider using additional analysis or indicators for signal confirmation, ensuring a comprehensive approach to decision-making.
█ Default Settings
- Trade Direction: Determines if the strategy engages in long, short, or both types of trades, impacting its adaptability to market conditions.
- Timeframe Input: Influences signal frequency and sensitivity, affecting the strategy's responsiveness to market dynamics.
- Z-Score Calculation Period: Affects the strategy’s sensitivity to market changes, with longer periods smoothing data and shorter periods increasing responsiveness.
- Entry and Exit Thresholds: Set the Z-Score levels for initiating or exiting trades, balancing between capturing opportunities and minimizing false signals.
- Impact of Default Settings: Provides a balanced approach to leverage sentiment trading, with adjustments needed to optimize performance across various market conditions.
Stablecoin Dominance [LuxAlgo]The Stablecoin Dominance tool displays the evolution of the relative supply dominance of major stablecoins such as USDT, USDC, BUSD, DAI, and TUSD.
Users can disable supported stablecoins to only show the supply dominance relative to the ones enabled.
🔶 USAGE
The stablecoin space is subject to constant change due to new arriving stablecoins, regulation, collapse of coins...etc.
Studying the evolution in supply dominance can help see the effect that certain events can have on the stablecoin sphere.
This dominance graph is displayed over the user price chart to easily observe the correlation between stablecoin dominances and market prices. Users can still move the tool to a new pane below if having it on the price chart is not desired.
🔶 DETAILS
Supported stablecoins include:
Tether (USDT)
USD Coin (USDC)
Binance USD (BUSD)
Dai (DAI)
TrueUSD (TUSD)
Supply dominance of a stablecoin is calculated by dividing the total supply of that stablecoin by the total supply of all enabled stablecoins. That is for N stablecoins:
sd(stablecoin A) = supply(stablecoin 1) / [supply(stablecoin 1) + supply(stablecoin 2) + supply(stablecoin 3) + ... + supply(stablecoin N)
🔹 Display
Users can control the fill style of the displayed areas, with "Gradient" enabled by default. Using "Solid" will use a solid color for each area:
This can improve the performance of the script.
Selecting "None" will not display areas.
🔶 SETTINGS
Fill Style: Fill style of the areas between each returned supply dominance. "Gradient" will color the areas using a gradient, while "Solid" will use a solid color.
Stablecoins List: List of stablecoins used for the supply dominance calculation, disabling one stablecoin will exclude it from all calculations.
RSI Volatility Bands [QuantraSystems]RSI Volatility Bands
Introduction
The RSI Volatility Bands indicator introduces a unique approach to market analysis by combining the traditional Relative Strength Index (RSI) with dynamic, volatility adjusted deviation bands. It is designed to provide a highly customizable method of trend analysis, enabling investors to analyze potential entry and exit points in a new and profound way.
The deviation bands are calculated and drawn in a manner which allows investors to view them as areas of dynamic support and resistance.
Legend
Upper and Lower Bands - A dynamic plot of the volatility-adjusted range around the current price.
Signals - Generated when the RSI volatility bands indicate a trend shift.
Case Study
The chart highlights the occurrence of false signals, emphasizing the need for caution when the bands are contracted and market volatility is low.
Juxtaposing this, during volatile market phases as shown, the indicator can effectively adapt to strong trends. This keeps an investor in a position even through a minor drawdown in order to exploit the entire price movement.
Recommended Settings
The RSI Volatility Bands are highly customisable and can be adapted to many assets with diverse behaviors.
The calibrations used in the above screenshots are as follows:
Source = close
RSI Length = 8
RSI Smoothing MA = DEMA
Bandwidth Type = DEMA
Bandwidth Length = 24
Bandwidth Smooth = 25
Methodology
The indicator first calculates the RSI of the price data, and applies a custom moving average.
The deviation bands are then calculated based upon the absolute difference between the RSI and its moving average - providing a unique volatility insight.
The deviation bands are then adjusted with another smoothing function, providing clear visuals of the RSI’s trend within a volatility-adjusted context.
rsiVal = ta.rsi(close, rsiLength)
rsiEma = ma(rsiMA, rsiVal, bandLength)
bandwidth = ma(bandMA, math.abs(rsiVal - rsiEma), bandLength)
upperBand = ma(bandMA, rsiEma + bandwidth, smooth)
lowerBand = ma(bandMA, rsiEma - bandwidth, smooth)
long = upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50)
short= not (upperBand > 50 and not (lowerBand < lowerBand and lowerBand < 50))
By dynamically adjusting to market conditions, the RSI trend bands offer a unique perspective on market trends, and reversal zones.
Crypto Stablecoin Supply - Indicator [presentTrading]█ Introduction and How it is Different
The "Stablecoin Supply - Indicator" differentiates itself by focusing on the aggregate supply of major stablecoins—USDT, USDC, and DAI—rather than traditional price-based metrics. Its premise is that fluctuations in the total supply of these stablecoins can serve as leading indicators for broader market movements, offering traders a unique vantage point to anticipate shifts in market sentiment.
BTCUSD 6h for recent bull market
BTCUSD 8h
█ Strategy, How it Works: Detailed Explanation
🔶 Data Collection
The strategy begins with the collection of the closing supply for USDT, USDC, and DAI stablecoins. This data is fetched using a specified timeframe (**`tfInput`**), allowing for flexibility in analysis periods.
🔶 Supply Calculation
The individual supplies of USDT, USDC, and DAI are then aggregated to determine the total stablecoin supply within the market at any given time. This combined figure serves as the foundation for the subsequent statistical analysis.
🔶 Z-Score Computation
The heart of the indicator's strategy lies in the computation of the Z-Score, which is a statistical measure used to identify how far a data point is from the mean, relative to the standard deviation. The formula for the Z-Score is:
Z = (X - μ) / σ
Where:
- Z is the Z-Score
- X is the current total stablecoin supply (TotalStablecoinClose)
- μ (mu) is the mean of the total stablecoin supply over a specified length (len)
- σ (sigma) is the standard deviation of the total stablecoin supply over the same length
A moving average of the Z-Score (**`zScore_ma`**) is calculated over a short period (defaulted to 3) to smooth out the volatility and provide a clearer signal.
🔶 Signal Interpretation
The Z-Score itself is plotted, with its color indicating its relation to a defined threshold (0.382), serving as a direct visual cue for market sentiment. Zones are also highlighted to show when the Z-Score is within certain extreme ranges, suggesting overbought or oversold conditions.
Bull -> Bear
█ Trade Direction
- **Entry Threshold**: A Z-Score crossing above 0.382 suggests an increase in stablecoin supply relative to its historical average, potentially indicating bullish market sentiment or incoming capital flow into cryptocurrencies.
- **Exit Threshold**: Conversely, a Z-Score dropping below -0.382 may signal a reduction in stablecoin supply, hinting at bearish sentiment or capital withdrawal.
█ Usage
Traders can leverage the "Stablecoin Supply - Indicator" to gain insights into the underlying market dynamics that are not immediately apparent through price analysis alone. It is particularly useful for identifying potential shifts in market sentiment before they are reflected in price movements. By integrating this indicator with other technical analysis tools, traders can develop a more rounded and informed trading strategy.
█ Default Settings
- Timeframe Input (`tfInput`): Allows users to specify the timeframe for data collection, adding flexibility to the analysis.
- Z-Score Length (`len`): Set to 252 by default, representing the period over which the mean and standard deviation of the stablecoin supply are calculated.
- Color Coding: Uses distinct colors (green for bullish, red for bearish) to indicate the Z-Score's position relative to its thresholds, enhancing visual clarity.
- Extreme Range Fill: Highlights areas between defined high and low Z-Score thresholds with distinct colors to indicate potential overbought or oversold conditions.
By integrating considerations of stablecoin supply into the analytical framework, the "Stablecoin Supply - Indicator" offers a novel perspective on cryptocurrency market dynamics, enabling traders to make more nuanced and informed decisions.
Smart DCA StrategyINSPIRATION
While Dollar Cost Averaging (DCA) is a popular and stress-free investment approach, I noticed an opportunity for enhancement. Standard DCA involves buying consistently, regardless of market conditions, which can sometimes mean missing out on optimal investment opportunities. This led me to develop the Smart DCA Strategy – a 'set and forget' method like traditional DCA, but with an intelligent twist to boost its effectiveness.
The goal was to build something more profitable than a standard DCA strategy so it was equally important that this indicator could backtest its own results in an A/B test manner against the regular DCA strategy.
WHY IS IT SMART?
The key to this strategy is its dynamic approach: buying aggressively when the market shows signs of being oversold, and sitting on the sidelines when it's not. This approach aims to optimize entry points, enhancing the potential for better returns while maintaining the simplicity and low stress of DCA.
WHAT THIS STRATEGY IS, AND IS NOT
This is an investment style strategy. It is designed to improve upon the common standard DCA investment strategy. It is therefore NOT a day trading strategy. Feel free to experiment with various timeframes, but it was designed to be used on a daily timeframe and that's how I recommend it to be used.
You may also go months without any buy signals during bull markets, but remember that is exactly the point of the strategy - to keep your buying power on the sidelines until the markets have significantly pulled back. You need to be patient and trust in the historical backtesting you have performed.
HOW IT WORKS
The Smart DCA Strategy leverages a creative approach to using Moving Averages to identify the most opportune moments to buy. A trigger occurs when a daily candle, in its entirety including the high wick, closes below the threshold line or box plotted on the chart. The indicator is designed to facilitate both backtesting and live trading.
HOW TO USE
Settings:
The input parameters for tuning have been intentionally simplified in an effort to prevent users falling into the overfitting trap.
The main control is the Buying strictness scale setting. Setting this to a lower value will provide more buying days (less strict) while higher values mean less buying days (more strict). In my testing I've found level 9 to provide good all round results.
Validation days is a setting to prevent triggering entries until the asset has spent a given number of days (candles) in the overbought state. Increasing this makes entries stricter. I've found 0 to give the best results across most assets.
In the backtest settings you can also configure how much to buy for each day an entry triggers. Blind buy size is the amount you would buy every day in a standard DCA strategy. Smart buy size is the amount you would buy each day a Smart DCA entry is triggered.
You can also experiment with backtesting your strategy over different historical datasets by using the Start date and End date settings. The results table will not calculate for any trades outside what you've set in the date range settings.
Backtesting:
When backtesting you should use the results table on the top right to tune and optimise the results of your strategy. As with all backtests, be careful to avoid overfitting the parameters. It's better to have a setup which works well across many currencies and historical periods than a setup which is excellent on one dataset but bad on most others. This gives a much higher probability that it will be effective when you move to live trading.
The results table provides a clear visual representation as to which strategy, standard or smart, is more profitable for the given dataset. You will notice the columns are dynamically coloured red and green. Their colour changes based on which strategy is more profitable in the A/B style backtest - green wins, red loses. The key metrics to focus on are GOA (Gain on Account) and Avg Cost .
Live Trading:
After you've finished backtesting you can proceed with configuring your alerts for live trading.
But first, you need to estimate the amount you should buy on each Smart DCA entry. We can use the Total invested row in the results table to calculate this. Assuming we're looking to trade on BITSTAMP:BTCUSD
Decide how much USD you would spend each day to buy BTC if you were using a standard DCA strategy. Lets say that is $5 per day
Enter that USD amount in the Blind buy size settings box
Check the Blind Buy column in the results table. If we set the backtest date range to the last 10 years, we would expect the amount spent on blind buys over 10 years to be $18,250 given $5 each day
Next we need to tweak the value of the Smart buy size parameter in setting to get it as close as we can to the Total Invested amount for Blind Buy
By following this approach it means we will invest roughly the same amount into our Smart DCA strategy as we would have into a standard DCA strategy over any given time period.
After you have calculated the Smart buy size , you can go ahead and set up alerts on Smart DCA buy triggers.
BOT AUTOMATION
In an effort to maintain the 'set and forget' stress-free benefits of a standard DCA strategy, I have set my personal Smart DCA Strategy up to be automated. The bot runs on AWS and I have a fully functional project for the bot on my GitHub account. Just reach out if you would like me to point you towards it. You can also hook this into any other 3rd party trade automation system of your choice using the pre-configured alerts within the indicator.
PLANNED FUTURE DEVELOPMENTS
Currently this is purely an accumulation strategy. It does not have any sell signals right now but I have ideas on how I will build upon it to incorporate an algorithm for selling. The strategy should gradually offload profits in bull markets which generates more USD which gives more buying power to rinse and repeat the same process in the next cycle only with a bigger starting capital. Watch this space!
MARKETS
Crypto:
This strategy has been specifically built to work on the crypto markets. It has been developed, backtested and tuned against crypto markets and I personally only run it on crypto markets to accumulate more of the coins I believe in for the long term. In the section below I will provide some backtest results from some of the top crypto assets.
Stocks:
I've found it is generally more profitable than a standard DCA strategy on the majority of stocks, however the results proved to be a lot more impressive on crypto. This is mainly due to the volatility and cycles found in crypto markets. The strategy makes its profits from capitalising on pullbacks in price. Good stocks on the other hand tend to move up and to the right with less significant pullbacks, therefore giving this strategy less opportunity to flourish.
Forex:
As this is an accumulation style investment strategy, I do not recommend that you use it to trade Forex.
STRATEGY IN ACTION
Here you see the indicator running on the BITSTAMP:BTCUSD pair. You can read the indicator as follows:
Vertical green bands on historical candles represents where buy signals triggered in the past
Table on the top right represents the results of the A/B backtest against a standard DCA strategy
Green Smart Buy column shows that Smart DCA was more profitable than standard DCA on this backtest. That is shown by the percentage GOA (Gain on Account) and the Avg Cost
Smart Buy Zone label marks the threshold which the entire candle must be below to trigger a buy signal (line can be changed to a box under plotting settings)
Green color of Smart Buy Zone label represents that the open candle is still valid for a buy signal. A signal will only be generated if the candle closes while this label is still green
Below is the same BITSTAMP:BTCUSD chart a couple of days later. Notice how the threshold has been broken and the Smart Buy Zone label has turned from green to red. No buy signal can be triggered for this day - even if the candle retraced and closed below the threshold before daily candle close.
Notice how the green vertical bands tend to be present after significant pullbacks in price. This is the reason the strategy works! Below is the same BITSTAMP:BTCUSD chart, but this time zoomed out to present a clearer picture of the times it would invest vs times it would sit out of the market. You will notice it invests heavily in bear markets and significant pullbacks, and does not buy anything during bull markets.
Finally, to visually demonstrate the indicator on an asset other than BTC, here is an example on CRYPTO:ETHUSD . In this case the current daily high has not touched the threshold so it is still possible for this to be a valid buy trigger on daily candle close. The vertical green band will not print until the buy trigger is confirmed.
BACKTEST RESULTS
Now for some backtest results to demonstrate the improved performance over a standard DCA strategy using all non-stablecoin assets in the top 30 cryptos by marketcap.
I've used the TradingView ticker (exchange name denoted as CRYPTO in the symbol search) for every symbol tested with the exception of BTCUSD because there was some dodgy data at the beginning of the TradingView BTCUSD chart which overinflated the effectiveness of the Smart DCA strategy on that ticker. For BTCUSD I've used the BITSTAMP exchange data. The symbol links below will take you to the correct chart and exchange used for the test.
I'm using the GOA (Gain on Account) values to present how each strategy performed.
The value on the left side is the standard DCA result and the right is the Smart DCA result.
✅ means Smart DCA strategy outperformed the standard DCA strategy
❌ means standard DCA strategy outperformed the Smart DCA strategy
To avoid overfitting, and to prove that this strategy does not suffer from overfitting, I've used the exact same input parameters for every symbol tested below. The settings used in these backtests are:
Buying strictness scale: 9
Validation days: 0
You can absolutely tweak the values per symbol to further improve the results of each, however I think using identical settings on every pair tested demonstrates a higher likelihood that the results will be similar in the live markets.
I'm presenting results for two time periods:
First price data available for trading pair -> closing candle on Friday 26th Jan 2024 (ALL TIME)
Opening candle on Sunday 1st Jan 2023 -> closing candle on Friday 26th Jan 2024 (JAN 2023 -> JAN 2024)
ALL TIME:
BITSTAMP:BTCUSD 80,884% / 133,582% ✅
CRYPTO:ETHUSD 17,231% / 36,146% ✅
CRYPTO:BNBUSD 5,314% / 2,702% ❌
CRYPTO:SOLUSD 1,745% / 1,171% ❌
CRYPTO:XRPUSD 2,585% / 4,544% ✅
CRYPTO:ADAUSD 338% / 353% ✅
CRYPTO:AVAXUSD 130% / 160% ✅
CRYPTO:DOGEUSD 13,690% / 16,432% ✅
CRYPTO:TRXUSD 414% / 466% ✅
CRYPTO:DOTUSD -16% / -7% ✅
CRYPTO:LINKUSD 1,161% / 2,164% ✅
CRYPTO:TONUSD 25% / 47% ✅
CRYPTO:MATICUSD 1,769% / 1,587% ❌
CRYPTO:ICPUSD 70% / 50% ❌
CRYPTO:SHIBUSD -20% / -19% ✅
CRYPTO:LTCUSD 486% / 718% ✅
CRYPTO:BCHUSD -4% / 3% ✅
CRYPTO:LEOUSD 102% / 151% ✅
CRYPTO:ATOMUSD 46% / 91% ✅
CRYPTO:UNIUSD -16% / 1% ✅
CRYPTO:ETCUSD 283% / 414% ✅
CRYPTO:OKBUSD 1,286% / 1,935% ✅
CRYPTO:XLMUSD 1,471% / 1,592% ✅
CRYPTO:INJUSD 830% / 1,035% ✅
CRYPTO:OPUSD 138% / 195% ✅
CRYPTO:NEARUSD 23% / 44% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset since the creation of each asset, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 4,998.65%
Profit: $499,865
Closing balance: $509,865
Smart DCA Strategy results:
Average percent return: 7,906.03%
Profit: $790,603
Closing balance: $800,603
JAN 2023 -> JAN 2024:
BITSTAMP:BTCUSD 47% / 66% ✅
CRYPTO:ETHUSD 26% / 33% ✅
CRYPTO:BNBUSD 15% / 17% ✅
CRYPTO:SOLUSD 272% / 394% ✅
CRYPTO:XRPUSD 7% / 12% ✅
CRYPTO:ADAUSD 43% / 59% ✅
CRYPTO:AVAXUSD 116% / 151% ✅
CRYPTO:DOGEUSD 8% / 14% ✅
CRYPTO:TRXUSD 48% / 65% ✅
CRYPTO:DOTUSD 24% / 35% ✅
CRYPTO:LINKUSD 83% / 124% ✅
CRYPTO:TONUSD 7% / 21% ✅
CRYPTO:MATICUSD -3% / 7% ✅
CRYPTO:ICPUSD 161% / 196% ✅
CRYPTO:SHIBUSD 1% / 8% ✅
CRYPTO:LTCUSD -15% / -7% ✅
CRYPTO:BCHUSD 47% / 68% ✅
CRYPTO:LEOUSD 9% / 11% ✅
CRYPTO:ATOMUSD 1% / 15% ✅
CRYPTO:UNIUSD 9% / 23% ✅
CRYPTO:ETCUSD 27% / 40% ✅
CRYPTO:OKBUSD 21% / 30% ✅
CRYPTO:XLMUSD 11% / 19% ✅
CRYPTO:INJUSD 477% / 446% ❌
CRYPTO:OPUSD 77% / 91% ✅
CRYPTO:NEARUSD 78% / 95% ✅
Backtest result analysis:
Assuming we have an initial investment amount of $10,000 spread evenly across each asset for the duration of 2023, it would have provided the following results.
Standard DCA Strategy results:
Average percent return: 61.42%
Profit: $6,142
Closing balance: $16,142
Smart DCA Strategy results:
Average percent return: 78.19%
Profit: $7,819
Closing balance: $17,819
Trend Change IndicatorThe Trend Change Indicator is an all-in-one, user-friendly trend-following tool designed to identify bullish and bearish trends in asset prices. It features adjustable input values and a built-in alert system that promptly notifies investors of potential shifts in both short-term and long-term price trends. This alert system is crucial for helping less active investors correctly position themselves ahead of major trend shifts and assists in risk management after a trend is established. It's important to note that this indicator is most effective with assets that historically exhibit strong trends.
At the heart of this tool is the interaction between the 30-day and 60-day Exponential Moving Averages (EMA). A bullish trend is indicated in green when the 30-day EMA is above the 60-day EMA, while a bearish trend is signaled in red when the 30-day EMA is below the 60-day EMA. The appearance of gray alerts users to potential shifts in the current trend as the EMAs converge, falling below the Average True Range (ATR) safety margin. This analysis is conducted across both hourly and daily timeframes, with the 4-hour timeframe providing early signals for daily trend changes. The band visually represents the interaction between the daily EMAs and is also displayed in the second row of the table, with the first row showing the same EMA interaction on the 4-hour timeframe.
This indicator also includes a 140-day (20-week) Simple Moving Average (SMA), visually represented by a line with predictive dots. This feature significantly enhances the investor's ability to understand long-term trends in asset prices, offering forward-looking insights by projecting the SMA value 10 days into the future. The value of this forecast lies in interpreting the slope of the dots; upward trending dots suggest a bullish underlying trend, while downward trending dots indicate a bearish trend. Generally, prices above the SMA signal bullishness, and prices below indicate bearishness.
In summary, the Trend Change Indicator is a comprehensive solution for identifying price trends and managing risk. Its intuitive, color-coded design makes it an indispensable tool for traders and investors who aim to be well-positioned ahead of trend shifts and manage risk once a trend has been established. While it has proven historically valuable in trending markets such as cryptocurrencies, tech stocks, and commodities, it is advisable to use this indicator in conjunction with other technical analysis tools for a more comprehensive and well-rounded decision-making process.
Aggregated Funding RateAFR - A tool designed for traders who demand real-time, comprehensive insights into funding rates across various exchanges. This script stands out in its ability to aggregate and analyze funding rates from 9 different exchanges , offering unparalleled depth and breadth in market analysis.
Key Features:
Real-Time Monitoring Across Multiple Exchanges: Seamlessly tracks funding rates from top exchanges including Binance, Bybit, Bitmex, Bitget, OKX, and more, ensuring a broad market view.
Sophisticated Algorithms for Accurate Calculations: Each exchange has unique algorithms for funding rate calculation. This script accounts for these variations, providing precise, reliable data. It is important to note that exact calculations are not possible on TradingView due to data limitations; there is no direct access to order books, so for some parts, generalizations have been made.
Dual Modes for Enhanced Understanding:
Standard Mode: Displays the current funding rate as is, for immediate insight.
Annualized Mode: Projects the funding rate on an annual basis, offering a perspective on long-term trends and impacts.
Weighted Analysis Options:
Average Mode: Treats each exchange equally, providing a balanced overview.
Weighted Mode: Adjusts the influence of each exchange based on their perpetual trading volume, offering a nuanced, market-relevant view.
Customizable Exchange Selection: Users have the flexibility to include or exclude specific exchanges from the calculation, allowing for tailored analysis based on personal trading strategies.
User-Friendly Visualization: The script features clear, intuitive plots and color-coded visuals to make data interpretation straightforward and effective.
Versatility in Timeframes: Designed to be adaptable across any timeframe, this tool is equally effective whether you are looking at a 1-minute interval or longer durations. This makes it an indispensable tool for both high-frequency traders and those analyzing broader market trends.
As always, use this indicator in tandem with other tools. This is a very useful indicator, but one should always rely on self-made, backtested and forward tested strategies
Liquidation Level ScreenerThe Liquidation Level Screener is an analytical tool designed for traders who seek a comprehensive view of potential liquidation zones in the market. This script, adaptable to almost any timeframe from 1 minute to 3 days, offers a unique perspective by mapping out key liquidation levels where significant market actions could occur.
Key Features:
Multi-Exchange Data Aggregation: Unlike many other indicators, the Liquidation Levels Indicator compiles data from multiple leading exchanges including Binance, Bitmex, Kraken, and Bitfinex. This approach ensures a more holistic and accurate representation of market sentiment, providing insights into potential liquidation points across various platforms.
Customizable Timeframes and Modes: The script is versatile, working effectively across various timeframes. It operates in two distinct modes:
Actual Levels Display: Visually represents potential liquidation levels.
Settings Mode: Showcases an open interest (OI) oscillator. When OI is exceptionally high, indicating a surge in opened positions at a specific candle, it signals traders to be vigilant about upcoming liquidation levels.
Three-Tier Liquidation System: The indicator categorizes liquidation levels into three distinct tiers based on open interest levels—1, 2, and 3—with Level 3 representing the highest concentration of open positions. This tiered approach allows traders to gauge the significance of each level and adjust their strategies accordingly.
Histogram Visualization: A novel feature of this script is the histogram on the chart's right side, representing the concentration of liquidation levels in specific market zones. This visual aid helps traders identify crucial areas that warrant close attention, enhancing decision-making.
Customizable Options:
Moving Averages: Choose from a wide range of moving average types, including VWMA, SMA, EMA, and more, to tailor the indicator to your analysis style.
Histogram Settings: Adjust the number of histograms, lookback bars, and their proximity to the latest candle, allowing for a personalized density and range of visualization.
Liquidation Level Sensitivity: Set thresholds for different liquidation levels, fine-tuning the indicator to detect varying degrees of market leverage.
Color Coding: Customize the color scheme for different leverage levels, enhancing visual clarity and ease of interpretation.
The Liquidation Level Screener offers a unique edge by highlighting potential zones where significant market movements can occur due to liquidations. By consolidating data from multiple exchanges, it provides a more rounded view of market behavior, which is essential in today’s interconnected trading environment. The tiered liquidation system and histogram feature equip traders with the ability to identify and focus on key market segments where high activity is expected. This tool is particularly valuable for traders who base their strategies on market liquidity and leverage dynamics.
Open Interest OscillatorIn the middle of a bustling cryptocurrency market, with Bitcoin navigating a critical phase and the community hype over potential ETF approvals, current funding rates, and market leverage, the timing is optimal to harness the capabilities of sophisticated trading tools.
Meet the Open Interest Oscillator – special indicator tailored for the volatile arena of cryptocurrency trading. This powerful instrument is adept at consolidating open interest data from a multitude of exchanges, delivering an in-depth snapshot of market sentiment across all timeframes, be it a 1-minute sprint or a weekly timeframe.
This versatile indicator is compatible with nearly all cryptocurrency pairs, offering an expansive lens through which traders can gauge the market's pulse.
Key Features:
-- Multi-exchange Data Aggregation: This feature taps into the heart of the crypto market by aggregating open interest data from premier exchanges such as BINANCE, BITMEX, BITFINEX, and KRAKEN. It goes a step further by integrating data from various pairs and stablecoins, thus providing traders with a rich, multi-dimensional view of market activities.
-- Open Interest Bars: Witness the flow of market dynamics through bars that depict the volume of positions being opened or closed, offering a clear visual cue of trading behavior. In this mode, If bars are going into negative zone, then traders are closing their positions. If they go into positive territory - leveraged positions are being opened.
-- Bollinger Band Integration: Incorporate a layer of statistical analysis with standard deviation calculations, which frame the open interest changes, giving traders a quantified edge to evaluate the market's volatility and momentum.
-- Oscillator with Customizable Thresholds: Personalize your trading signals by setting thresholds that resonate with your unique trading tactics. This customization brings the power of tailored analytics to your strategic arsenal.
-- Max OI Ceiling Setting: In the fast-paced crypto environment where data can surge to overwhelming levels, the Max OI Ceiling ensures you maintain a clear view by capping the open interest data, thus preserving the readability and interpretability of information, even when market activity reaches feverish heights.
Triple Confirmation Kernel Regression Overlay [QuantraSystems]Kernel Regression Oscillator - Overlay
Introduction
The Kernel Regression Oscillator (ᏦᏒᎧ) represents an advanced tool for traders looking to capitalize on market trends.
This Indicator is valuable in identifying and confirming trend directions, as well as probabilistic and dynamic oversold and overbought zones.
It achieves this through a unique composite approach using three distinct Kernel Regressions combined in an Oscillator.
The additional Chart Overlay Indicator adds confidence to the signal.
Which is this Indicator.
This methodology helps the trader to significantly reduce false signals and offers a more reliable indication of market movements than more widely used indicators can.
Legend
The upper section is the Overlay. It features the Signal Wave to display the current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 3), which can indicate extremely rare situations which can lead to either a softening momentum in the trend or even a mean reversion situation.
The lower one is the Base Chart.
The Indicator is linked here
It features the Kernel Regression Oscillator to display a composite of three distinct regressions, also displaying current trend.
Its Overbought and Oversold zones start at 50% and end at 100% of the selected Standard Deviation (default σ = 2), which can indicate extremely rare situations.
Case Study
To effectively utilize the ᏦᏒᎧ, traders should use both the additional Overlay and the Base
Chart at the same time. Then focus on capturing the confluence in signals, for example:
If the 𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮 on the Overlay and the ᏦᏒᎧ on the Base Chart both reside near the extreme of an Oversold zone the probability is higher than normal that momentum in trend may soften or the token may even experience a reversion soon.
If a bar is characterized by an Oversold Shading in both the Overlay and the Base Chart, then the probability is very high to experience a reversion soon.
In this case the trader may want to look for appropriate entries into a long position, as displayed here.
If a bar is characterized by an Overbought Shading in either Overlay or Base Chart, then the probability is high for momentum weakening or a mean reversion.
In this case the trade may have taken profit and closed his long position, as displayed here.
Please note that we always advise to find more confluence by additional indicators.
Recommended Settings
Swing Trading (1D chart)
Overlay
Bandwith: 45
Width: 2
SD Lookback: 150
SD Multiplier: 2
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Fast-paced, Scalping (4min chart)
Overlay
Bandwith: 75
Width: 2
SD Lookback: 150
SD Multiplier: 3
Base Chart
Bandwith: 45
SD Lookback: 150
SD Multiplier: 2
Notes
The Kernel Regression Oscillator on the Base Chart is also sensitive to divergences if that is something you are keen on using.
For maximum confluence, it is recommended to use the indicator both as a chart overlay and in its Base Chart.
Please pay attention to shaded areas with Standard Deviation settings of 2 or 3 at their outer borders, and consider action only with high confidence when both parts of the indicator align on the same signal.
This tool shows its best performance on timeframes lower than 4 hours.
Traders are encouraged to test and determine the most suitable settings for their specific trading strategies and timeframes.
The trend following functionality is indicated through the "𝓢𝓲𝓰𝓷𝓪𝓵 𝓦𝓪𝓿𝓮" Line, with optional "Up" and "Down" arrows to denote trend directions only (toggle “Show Trend Signals”).
Methodology
The Kernel Regression Oscillator takes three distinct kernel regression functions,
used at similar weight, in order to calculate a balanced and smooth composite of the regressions. Part of it are:
The Epanechnikov Kernel Regression: Known for its efficiency in smoothing data by assigning less weight to data points further away from the target point than closer data points, effectively reducing variance.
The Wave Kernel Regression: Similarly assigning weight to the data points based on distance, it captures repetitive and thus wave-like patterns within the data to smoothen out and reduce the effect of underlying cyclical trends.
The Logistic Kernel Regression: This uses the logistic function in order to assign weights by probability distribution on the distance between data points and target points. It thus avoids both bias and variance to a certain level.
kernel(source, bandwidth, kernel_type) =>
switch kernel_type
"Epanechnikov" => math.abs(source) <= 1 ? 0.75 * (1 - math.pow(source, 2)) : 0.0
"Logistic" => 1/math.exp(source + 2 + math.exp(-source))
"Wave" => math.abs(source) <= 1 ? (1 - math.abs(source)) * math.cos(math.pi * source) : 0.
kernelRegression(src, bandwidth, kernel_type) =>
sumWeightedY = 0.
sumKernels = 0.
for i = 0 to bandwidth - 1
base = i*i/math.pow(bandwidth, 2)
kernel = kernel(base, 1, kernel_type)
sumWeightedY += kernel * src
sumKernels += kernel
(src - sumWeightedY/sumKernels)/src
// Triple Confirmations
Ep = kernelRegression(source, bandwidth, 'Epanechnikov' )
Lo = kernelRegression(source, bandwidth, 'Logistic' )
Wa = kernelRegression(source, bandwidth, 'Wave' )
By combining these regressions in an unbiased average, we follow our principle of achieving confluence for a signal or a decision, by stacking several edges to increase the probability that we are correct.
// Average
AV = math.avg(Ep, Lo, Wa)
The Standard Deviation bands take defined parameters from the user, in this case sigma of ideally between 2 to 3,
to help the indicator detect extremely improbable conditions and thus take an inversely probable signal from it to forward to the user.
The parameter settings and also the visualizations allow for ample customizations by the trader. The indicator comes with default and recommended settings.
For questions or recommendations, please feel free to seek contact in the comments.
