Tetuan SniperThe TEMA and EMA Crossover Alert with SL, TP, and Order Signal strategy combines the power of Triple Exponential Moving Average (TEMA) and Exponential Moving Average (EMA) to generate high-quality trading signals. This strategy is designed to provide clear entry and exit points, manage risk through dynamic Stop Loss (SL) and Take Profit (TP) levels, and optimize trade sizes based on account balance and risk tolerance.
Key Features:
EMA and TEMA Crossover:
The strategy identifies potential buy and sell signals based on the crossover of EMA and TEMA. A buy signal is generated when TEMA crosses above EMA, and a sell signal is generated when TEMA crosses below EMA.
Dynamic Stop Loss (SL) and Take Profit (TP):
Stop Loss levels are dynamically set based on a user-defined number of pips below (for buy orders) or above (for sell orders) the lowest or highest point since the crossover.
Take Profit levels are dynamically adjusted using another TEMA, providing a flexible exit strategy that adapts to market conditions.
Lot Size Calculation:
The strategy calculates the optimal lot size based on the account balance, risk percentage per trade, and the number of maximum open orders. For JPY pairs, the lot size is adjusted by dividing by 100 to account for the different pip value.
The lot size is rounded to two decimal places for better readability and precision.
Visual Alerts and Labels:
Clear visual alerts and labels are provided for each buy and sell signal, including the recommended SL, TP, and lot size. The labels are placed in a way to avoid overlapping important chart elements.
Trend Visualization:
The area between the TEMA and EMA is colored to indicate the trend, with green for bullish trends and red for bearish trends, making it easy to visualize the market direction.
Inputs:
SL Points: Number of pips for the Stop Loss.
EMA Period: Period for the Exponential Moving Average.
TEMA Period: Period for the Triple Exponential Moving Average.
Account Balance: The total account balance for calculating the lot size.
Risk Percentage: The percentage of the account balance to risk per trade.
Take Profit TEMA Period: Period for the TEMA used to set Take Profit levels.
Lot per Pip Value: The value of 1 pip per lot.
Maximum Open Orders: The maximum number of open orders to split the balance among.
Example Usage
This strategy is suitable for traders who want to automate their trading signals and manage risk effectively. By combining TEMA and EMA crossovers with dynamic SL and TP levels and precise lot size calculation, traders can achieve a disciplined and methodical approach to trading.
Komut dosyalarını "Exponential Moving Average" için ara
Uptrick: Bullish/Bearish Highlight -DEMO 1 Indicator Purpose:
• The indicator serves as a technical analysis tool for traders to identify potential bullish
and bearish trends in the market.
• It highlights periods where the closing price is above or below a 50-period simple
moving average (SMA), indicating potential bullish or bearish sentiment, respectively.
2 Moving Averages:
• The indicator calculates a 50-period SMA (sma50) to smooth out price fluctuations
and identify the overall trend direction.
• It also computes an 8-period exponential moving average (EMA), which responds
more quickly to recent price changes compared to the SMA.
3 Bollinger Bands:
• Bollinger Bands are plotted around the SMA, indicating volatility in the price
movement.
• The bands are typically set at two standard deviations above and below the SMA,
representing approximately 95% of the price data within that range.
4 Bullish and Bearish Conditions:
• The indicator defines conditions for identifying bullish and bearish market sentiments.
• When the closing price is above the SMA50, it indicates a bullish condition, and when
it's below, it suggests a bearish condition.
5 Plotting:
• The indicator visualizes the bullish and bearish conditions by changing the
background color accordingly.
• It also plots the SMA50, EMA, and Bollinger Bands to provide a graphical
representation of the market dynamics.
6 User Interface:
• The indicator is designed to be used as an overlay on price charts, allowing traders to
easily incorporate it into their analysis.
Overall, the "Uptrick: Bullish/Bearish Highlight" indicator offers traders a comprehensive view of market trends and potential reversal points, helping them make informed trading decisions.
TIP: When the white line, which is the EMA , crosses above the SMA (the orange line), it is usually a good idea to buy, but when the EMA crosses below the SMA it is a good idea to sell.
Twin Range Filter VisualizedVisulaized version of @colinmck's Twin Range Filter version on TradingView.
On @colinmck's Twin Range Filter version, you can only see Long and Short signals on the chart.
But in this version of TRF, users can visually see the BUY and SELL signals on the chart with an added line of TRF.
TRF is an average of two smoothed Exponential Moving Averages, fast one has 27 bars of length and the slow one has 55 bars.
The purpose is to obtain two ranges that price fluctuates between (upper and lower range) and have LONG AND SHORT SIGNALS when close price crosses above the upper range and conversely crosses below lower range.
I personally combine the upper and lower ranges on one line to see the long and short signals with my own eyes so,
-BUY when price is higher or equal to the upper range level and the indicator line turns to draw the lower range to follow the price just under the bars as a trailing stop loss indicator like SuperTrend.
-SELL when price is lower or equal to the lower range levelline under the bars and then the indicator line turns to draw the upper range to follow the price just over the bars in that same trailing stop loss logic.
There are also two coefficients that adjusts the trailing line distance levels from the price multiplying the effect of the faster and slower moving averages.
The default values of the multipliers:
Fast range multiplier of Fast Moving Average(27): 1.6
Slow range multiplier of fSlow Moving Average(55): 2
Remember that if you enlarge these multipliers you will enlarge the ranges and have less but lagging signals. Conversely, decreasing the multipliers will have small ranges (line will get closer to the price and more signals will occur)
ChartRage - ELMAELMA - Exponential Logarithmic Moving Average
This is a new kind of moving average that is using exponential normalization of a logarithmic formula. The exponential function is used to average the weight on the moving average while the logarithmic function is used to calculate the overall price effect.
Features and Settings:
◻️ Following rate of change instead of absolute levels
◻️ Choose input source of the data
◻️ Real time signals through price interaction
◻️ Change ELMA length
◻️ Change the exponential decay rate
◻️ Customize base color and signal color
Equation of the ELMA:
This formula calculates a weighted average of the logarithm of prices, where more recent prices have a higher weight. The result is then exponentiated to return the ELMA value. This approach emphasizes the relative changes in price, making the ELMA sensitive to the % rate of change rather than absolute price levels. The decay rate can be adjusted in the settings.
Comparison EMA vs ELMA:
In this image we see the differences to the Exponential Moving Average.
Price Interaction and earlier Signals:
In this image we have added the bars, so we can see that the ELMA provides different signals of resistance and support zones and highlights them, by changing to the color yellow, when prices interact with the ELMA.
Strategy by trading Support and Resistance Zones:
The ELMA helps to evaluate trends and find entry points in bullish market conditions, and exit points in bearish conditions. When prices drop below the ELMA in a bull market, it is considered a buying signal. Conversely, in a bear market, it serves as an exit signal when prices trade above the ELMA.
Volatile Markets:
The ELMA works on all timeframes and markets. In this example we used the default value for Bitcoin. The ELMA clearly shows support and resistance zones. Depending on the asset, the length and the decay rate should be adjusted to provide the best results.
Real Time Signals:
Signals occur not after a candle closes but when price interacts with the ELMA level, providing real time signals by shifting color. (default = yellow)
Disclaimer* All analyses, charts, scripts, strategies, ideas, or indicators developed by us are provided for informational and educational purposes only. We do not guarantee any future results based on the use of these tools or past data. Users should trade at their own risk.
This work is licensed under Attribution-NonCommercial-ShareAlike 4.0 International
creativecommons.org
The Flash-Strategy with Minervini Stage Analysis QualifierThe Flash-Strategy (Momentum-RSI, EMA-crossover, ATR) with Minervini Stage Analysis Qualifier
Introduction
Welcome to a comprehensive guide on a cutting-edge trading strategy I've developed, designed for the modern trader seeking an edge in today's dynamic markets. This strategy, which I've honed through my years of experience in the trading arena, stands out for its unique blend of technical analysis and market intuition, tailored specifically for use on the TradingView platform.
As a trader with a deep passion for the financial markets, my journey began several years ago, driven by a relentless pursuit of a trading methodology that is both effective and adaptable. My background in trading spans various market conditions and asset classes, providing me with a rich tapestry of experiences from which to draw. This strategy is the culmination of that journey, embodying the lessons learned and insights gained along the way.
The cornerstone of this strategy lies in its ability to generate precise long signals in a Stage 2 uptrend and equally accurate short signals in a Stage 4 downtrend. This approach is rooted in the principles of trend following and momentum trading, harnessing the power of key indicators such as the Momentum-RSI, EMA Crossover, and Average True Range (ATR). What sets this strategy apart is its meticulous design, which allows it to adapt to the ever-changing market conditions, providing traders with a robust tool for navigating both bullish and bearish scenarios.
This strategy was born out of a desire to create a trading system that is not only highly effective in identifying potential trade setups but also straightforward enough to be implemented by traders of varying skill levels. It's a reflection of my belief that successful trading hinges on clarity, precision, and disciplined execution. Whether you are a seasoned trader or just beginning your journey, this guide aims to provide you with a comprehensive understanding of how to harness the full potential of this strategy in your trading endeavors.
In the following sections, we will delve deeper into the mechanics of the strategy, its implementation, and how to make the most out of its features. Join me as we explore the nuances of a strategy that is designed to elevate your trading to the next level.
Stage-Specific Signal Generation
A distinctive feature of this trading strategy is its focus on generating long signals exclusively during Stage 2 uptrends and short signals during Stage 4 downtrends. This approach is based on the widely recognized market cycle theory, which divides the market into four stages: Stage 1 (accumulation), Stage 2 (uptrend), Stage 3 (distribution), and Stage 4 (downtrend). By aligning the signal generation with these specific stages, the strategy aims to capitalize on the most dynamic and clear-cut market movements, thereby enhancing the potential for profitable trades.
1. Long Signals in Stage 2 Uptrends
• Characteristics of Stage 2: Stage 2 is characterized by a strong uptrend, where prices are consistently rising. This stage typically follows a period of accumulation (Stage 1) and is marked by increased investor interest and bullish sentiment in the market.
• Criteria for Long Signal Generation: Long signals are generated during this stage when the technical indicators align with the characteristics of a Stage 2 uptrend.
• Rationale for Stage-Specific Signals: By focusing on Stage 2 for long trades, the strategy seeks to enter positions during the phase of strong upward momentum, thus riding the wave of rising prices and investor optimism. This stage-specific approach minimizes exposure to less predictable market phases, like the consolidation in Stage 1 or the indecision in Stage 3.
2. Short Signals in Stage 4 Downtrends
• Characteristics of Stage 4: Stage 4 is identified by a pronounced downtrend, with declining prices indicating prevailing bearish sentiment. This stage typically follows the distribution phase (Stage 3) and is characterized by increasing selling pressure.
• Criteria for Short Signal Generation: Short signals are generated in this stage when the indicators reflect a strong bearish trend.
• Rationale for Stage-Specific Signals: Targeting Stage 4 for shorting capitalizes on the market's downward momentum. This tactic aligns with the natural market cycle, allowing traders to exploit the downward price movements effectively. By doing so, the strategy avoids the potential pitfalls of shorting during the early or late stages of the market cycle, where trends are less defined and more susceptible to reversals.
In conclusion, the strategy’s emphasis on stage-specific signal generation is a testament to its sophisticated understanding of market dynamics. By tailoring the long and short signals to Stages 2 and 4, respectively, it leverages the most compelling phases of the market cycle, offering traders a clear and structured approach to aligning their trades with dominant market trends.
Strategy Overview
At the heart of this trading strategy is a philosophy centered around capturing market momentum and trend efficiency. The core objective is to identify and capitalize on clear uptrends and downtrends, thereby allowing traders to position themselves in sync with the market's prevailing direction. This approach is grounded in the belief that aligning trades with these dominant market forces can lead to more consistent and profitable outcomes.
The strategy is built on three foundational components, each playing a critical role in the decision-making process:
1. Momentum-RSI (Relative Strength Index): The Momentum-RSI is a pivotal element of this strategy. It's an enhanced version of the traditional RSI, fine-tuned to better capture the strength and velocity of market trends. By measuring the speed and change of price movements, the Momentum-RSI provides invaluable insights into whether a market is potentially overbought or oversold, suggesting possible entry and exit points. This indicator is especially effective in filtering out noise and focusing on substantial market moves.
2. EMA (Exponential Moving Average) Crossover: The EMA Crossover is a crucial component for trend identification. This strategy employs two EMAs with different timeframes to determine the market trend. When the shorter-term EMA crosses above the longer-term EMA, it signals an emerging uptrend, suggesting a potential long entry. Conversely, a crossover below indicates a possible downtrend, hinting at a short entry opportunity. This simple yet powerful tool is key in confirming trend directions and timing market entries.
3. ATR (Average True Range): The ATR is instrumental in assessing market volatility. This indicator helps in understanding the average range of price movements over a given period, thus providing a sense of how much a market might move on a typical day. In this strategy, the ATR is used to adjust stop-loss levels and to gauge the potential risk and reward of trades. It allows for more informed decisions by aligning trade management techniques with the current volatility conditions.
The synergy of these three components – the Momentum-RSI, EMA Crossover, and ATR – creates a robust framework for this trading strategy. By combining momentum analysis, trend identification, and volatility assessment, the strategy offers a comprehensive approach to navigating the markets. Whether it's capturing a strong trend in its early stages or identifying a potential reversal, this strategy aims to provide traders with the tools and insights needed to make well-informed, strategically sound trading decisions.
Detailed Component Analysis
The efficacy of this trading strategy hinges on the synergistic functioning of its three key components: the Momentum-RSI, EMA Crossover, and Average True Range (ATR). Each component brings a unique perspective to the strategy, contributing to a well-rounded approach to market analysis.
1. Momentum-RSI (Relative Strength Index)
• Definition and Function: The Momentum-RSI is a modified version of the classic Relative Strength Index. While the traditional RSI measures the velocity and magnitude of directional price movements, the Momentum-RSI amplifies aspects that reflect trend strength and momentum.
• Significance in Identifying Trend Strength: This indicator excels in identifying the strength behind a market's move. A high Momentum-RSI value typically indicates strong bullish momentum, suggesting the potential continuation of an uptrend. Conversely, a low Momentum-RSI value signals strong bearish momentum, possibly indicative of an ongoing downtrend.
• Application in Strategy: In this strategy, the Momentum-RSI is used to gauge the underlying strength of market trends. It helps in filtering out minor fluctuations and focusing on significant movements, providing a clearer picture of the market's true momentum.
2. EMA (Exponential Moving Average) Crossover
• Definition and Function: The EMA Crossover component utilizes two exponential moving averages of different timeframes. Unlike simple moving averages, EMAs give more weight to recent prices, making them more responsive to new information.
• Contribution to Market Direction: The interaction between the short-term and long-term EMAs is key to determining market direction. A crossover of the shorter EMA above the longer EMA is an indicator of an emerging uptrend, while a crossover below signals a developing downtrend.
• Application in Strategy: The EMA Crossover serves as a trend confirmation tool. It provides a clear, visual representation of the market's direction, aiding in the decision-making process for entering long or short positions. This component ensures that trades are aligned with the prevailing market trend, a crucial factor for the success of the strategy.
3. ATR (Average True Range)
• Definition and Function: The ATR is an indicator that measures market volatility by calculating the average range between the high and low prices over a specified period.
• Role in Assessing Market Volatility: The ATR provides insights into the typical market movement within a given timeframe, offering a measure of the market's volatility. Higher ATR values indicate increased volatility, while lower values suggest a calmer market environment.
• Application in Strategy: Within this strategy, the ATR is instrumental in tailoring risk management techniques, particularly in setting stop-loss levels. By accounting for the market's volatility, the ATR ensures that stop-loss orders are placed at levels that are neither too tight (risking premature exits) nor too loose (exposing to excessive risk).
In summary, the combination of Momentum-RSI, EMA Crossover, and ATR in this trading strategy provides a comprehensive toolkit for market analysis. The Momentum-RSI identifies the strength of market trends, the EMA Crossover confirms the market direction, and the ATR guides in risk management by assessing volatility. Together, these components form the backbone of a strategy designed to navigate the complexities of the financial markets effectively.
1. Signal Generation Process
• Combining Indicators: The strategy operates by synthesizing signals from the Momentum-RSI, EMA Crossover, and ATR indicators. Each indicator serves a specific purpose: the Momentum-RSI gauges trend momentum, the EMA Crossover identifies the trend direction, and the ATR assesses the market’s volatility.
• Criteria for Signal Validation: For a signal to be considered valid, it must meet specific criteria set by each of the three indicators. This multi-layered approach ensures that signals are not only based on one aspect of market behavior but are a result of a comprehensive analysis.
2. Conditions for Long Positions
• Uptrend Confirmation: A long position signal is generated when the shorter-term EMA crosses above the longer-term EMA, indicating an uptrend.
• Momentum-RSI Alignment: Alongside the EMA crossover, the Momentum-RSI should indicate strong bullish momentum. This is typically represented by the Momentum-RSI being at a high level, confirming the strength of the uptrend.
• ATR Consideration: The ATR is used to fine-tune the entry point and set an appropriate stop-loss level. In a low volatility scenario, as indicated by the ATR, the stop-loss can be set tighter, closer to the entry point.
3. Conditions for Short Positions
• Downtrend Confirmation: Conversely, a short position signal is indicated when the shorter-term EMA crosses below the longer-term EMA, signaling a downtrend.
• Momentum-RSI Confirmation: The Momentum-RSI should reflect strong bearish momentum, usually seen when the Momentum-RSI is at a low level. This confirms the bearish strength of the market.
• ATR Application: The ATR again plays a role in determining the stop-loss level for the short position. Higher volatility, as indicated by a higher ATR, would warrant a wider stop-loss to accommodate larger market swings.
By adhering to these mechanics, the strategy aims to ensure that each trade is entered with a high probability of success, aligning with the market’s current momentum and trend. The integration of these indicators allows for a holistic market analysis, providing traders with clear and actionable signals for both entering and exiting trades.
Customizable Parameters in the Strategy
Flexibility and adaptability are key features of this trading strategy, achieved through a range of customizable parameters. These parameters allow traders to tailor the strategy to their individual trading style, risk tolerance, and specific market conditions. By adjusting these parameters, users can fine-tune the strategy to optimize its performance and align it with their unique trading objectives. Below are the primary parameters that can be customized within the strategy:
1. Momentum-RSI Settings
• Period: The lookback period for the Momentum-RSI can be adjusted. A shorter period makes the indicator more sensitive to recent price changes, while a longer period smoothens the RSI line, offering a broader view of the momentum.
• Overbought/Oversold Thresholds: Users can set their own overbought and oversold levels, which can help in identifying extreme market conditions more precisely according to their trading approach.
2. EMA Crossover Settings
• Timeframes for EMAs: The strategy uses two EMAs with different timeframes. Traders can modify these timeframes, choosing shorter periods for a more responsive approach or longer periods for a more conservative one.
• Source Data: The choice of price data (close, open, high, low) used in calculating the EMAs can be varied depending on the trader’s preference.
3. ATR Settings
• Lookback Period: Adjusting the lookback period for the ATR impacts how the indicator measures volatility. A longer period may provide a more stable but less responsive measure, while a shorter period offers quicker but potentially more erratic readings.
• Multiplier for Stop-Loss Calculation: This parameter allows traders to set how aggressively or conservatively they want their stop-loss to be in relation to the ATR value.
Here are the standard settings:
EMA Envelope - Signal with Stoploss and Takeprofit LevelsDescription:
This Pine Script indicator implements the EMA Envelope strategy, which utilizes Exponential Moving Averages (EMA) to create an envelope around the price chart. The strategy generates buy and sell signals based on the crossing of the price above and below the upper and lower EMA envelopes, respectively. It also incorporates additional features such as stop-loss and take-profit levels for risk management.
Indicator Settings:
EMA Length: Specifies the period for the short-term Exponential Moving Average.
Long Term EMA Length: Defines the period for the long-term Exponential Moving Average used for signal filtering.
Take Profit Ratio: Determines the ratio for calculating the take-profit levels based on the stop-loss.
Filter Signal on Long Term EMA: Enables or disables the filtering of buy/sell signals using the long-term EMA.
Show only recent signal: When enabled, shows only the most recent buy/sell signals.
Buy and Sell Signals:
The indicator generates buy signals when the price crosses above the upper EMA envelope and the previous low was below the upper EMA envelope. Additionally, you can choose to filter buy signals based on whether the closing price is above the long-term EMA.
Conversely, sell signals are generated when the price crosses below the lower EMA envelope, and the previous high was above the lower EMA envelope. Similar to buy signals, sell signals can also be filtered using the long-term EMA.
Note: Signal works well on Higher Timeframes like Daily/8hrs/4hrs/1hr.
Stop-Loss and Take-Profit Levels:
For buy signals, the stop-loss is set at the lower EMA level, while the take-profit level is calculated by adding a specified ratio of the difference between the low and the stop-loss level to the low price.
For sell signals, the stop-loss is set at the upper EMA level, and the take-profit level is calculated by subtracting a specified ratio of the difference between the stop-loss level and the high price from the high price.
Disclaimer:
This indicator is provided for educational and informational purposes only. Trading involves significant risk, and past performance does not guarantee future results. Users are solely responsible for their trading decisions and should conduct their own research and risk management. The author shall not be held liable for any losses or damages arising from the use of this indicator.
Note: Always test the indicator thoroughly on historical data and consider paper trading before applying it to live trading environments.
Regularized-Moving-Average Oscillator SuiteThe Regularized-MA Oscillator Suite is a versatile indicator that transforms any moving average into an oscillator. It comprises up to 13 different moving average types, including KAMA, T3, and ALMA. This indicator serves as a valuable tool for both trend following and mean reversion strategies, providing traders and investors with enhanced insights into market dynamics.
Methodology:
The Regularized MA Oscillator Suite calculates the moving average (MA) based on user-defined parameters such as length, moving average type, and custom smoothing factors. It then derives the mean and standard deviation of the MA using a normalized period. Finally, it computes the Z-Score by subtracting the mean from the MA and dividing it by the standard deviation.
KAMA (Kaufman's Adaptive Moving Average):
KAMA is a unique moving average type that dynamically adjusts its smoothing period based on market volatility. It adapts to changing market conditions, providing a smoother response during periods of low volatility and a quicker response during periods of high volatility. This allows traders to capture trends effectively while reducing noise.
T3 (Tillson's Exponential Moving Average):
T3 is an exponential moving average that incorporates additional smoothing techniques to reduce lag and provide a more responsive indicator. It aims to maintain a balance between responsiveness and smoothness, allowing traders to identify trend reversals with greater accuracy.
ALMA (Arnaud Legoux Moving Average):
ALMA is a moving average type that utilizes a combination of linear regression and exponential moving average techniques. It offers a unique way of calculating the moving average by providing a smoother and more accurate representation of price trends. ALMA reduces lag and noise, enabling traders to identify trend changes and potential entry or exit points more effectively.
Z-Score:
The Z-Score calculation in the Regularized-MA Oscillator Suite standardizes the values of the moving average. It measures the deviation of each data point from the mean in terms of standard deviations. By normalizing the moving average through the Z-Score, the indicator enables traders to assess the relative position of price in relation to its mean and volatility. This information can be valuable for identifying overbought and oversold conditions, as well as potential trend reversals.
Utility:
The Regularized-MA Oscillator Suite with its unique moving average types and Z-Score calculation offers traders and investors powerful analytical tools. It can be used for trend following strategies by analyzing the oscillator's position relative to the midline. Traders can also employ it as a mean reversion tool by identifying peak values above user-defined deviations. These features assist in identifying potential entry and exit points, enhancing trading decisions and market analysis.
Key Features:
Variety of 13 MA types.
Potential reversal point bubbles.
Bar coloring methods - Trend (Midline cross), Extremities, Reversions, Slope
Example Charts:
Mad_MATHLibrary "MAD_MATH"
This is a mathematical library where I store useful kernels, filters and selectors for the different types of computations.
This library also contains opensource code from other scripters.
Future extensions are very likely, there are some functions I would like to add, but I have to wait for approvals so i can include them.
Ehlers_EMA(_src, _length)
Calculates the Ehlers Exponential Moving Average (Ehlers_EMA)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers EMA
Returns: The Ehlers EMA value
Ehlers_Gaussian(_src, _length)
Calculates the Ehlers Gaussian Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers Gaussian Filter
Returns: The Ehlers Gaussian Filter value
Ehlers_supersmoother(_src, _length)
Calculates the Ehlers Supersmoother
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers Supersmoother
Returns: The Ehlers Supersmoother value
Ehlers_SMA_fast(_src, _length)
Calculates the Ehlers Simple Moving Average (SMA) Fast
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers SMA Fast
Returns: The Ehlers SMA Fast value
Ehlers_EMA_fast(_src, _length)
Calculates the Ehlers Exponential Moving Average (EMA) Fast
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers EMA Fast
Returns: The Ehlers EMA Fast value
Ehlers_RSI_fast(_src, _length)
Calculates the Ehlers Relative Strength Index (RSI) Fast
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers RSI Fast
Returns: The Ehlers RSI Fast value
Ehlers_Band_Pass_Filter(_src, _length)
Calculates the Ehlers BandPass Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers BandPass Filter
Returns: The Ehlers BandPass Filter value
Ehlers_Butterworth(_src, _length)
Calculates the Ehlers Butterworth Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers Butterworth Filter
Returns: The Ehlers Butterworth Filter value
Ehlers_Two_Pole_Gaussian_Filter(_src, _length)
Calculates the Ehlers Two-Pole Gaussian Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers Two-Pole Gaussian Filter
Returns: The Ehlers Two-Pole Gaussian Filter value
Ehlers_Two_Pole_Butterworth_Filter(_src, _length)
Calculates the Ehlers Two-Pole Butterworth Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers Two-Pole Butterworth Filter
Returns: The Ehlers Two-Pole Butterworth Filter value
Ehlers_Band_Stop_Filter(_src, _length)
Calculates the Ehlers Band Stop Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers Band Stop Filter
Returns: The Ehlers Band Stop Filter value
Ehlers_Smoother(_src)
Calculates the Ehlers Smoother
Parameters:
_src (float) : The source series for calculation
Returns: The Ehlers Smoother value
Ehlers_High_Pass_Filter(_src, _length)
Calculates the Ehlers High Pass Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers High Pass Filter
Returns: The Ehlers High Pass Filter value
Ehlers_2_Pole_High_Pass_Filter(_src, _length)
Calculates the Ehlers Two-Pole High Pass Filter
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the Ehlers Two-Pole High Pass Filter
Returns: The Ehlers Two-Pole High Pass Filter value
pr(_src, _length)
pr Calculates the percentage rank (PR) of a value within a range.
Parameters:
_src (float) : The source value for which the percentage rank is calculated. It represents the value to be ranked within the range.
_length (simple int) : The _length of the range over which the percentage rank is calculated. It determines the number of bars considered for the calculation.
Returns: The percentage rank (PR) of the source value within the range, adjusted by adding 50 to the result.
smma(_src, _length)
Calculates the SMMA (Smoothed Moving Average)
Parameters:
_src (float) : The source series for calculation
_length (simple int)
Returns: The SMMA value
hullma(_src, _length)
Calculates the Hull Moving Average (HullMA)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The _length of the HullMA
Returns: The HullMA value
tma(_src, _length)
Calculates the Triple Moving Average (TMA)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The _length of the TMA
Returns: The TMA value
dema(_src, _length)
Calculates the Double Exponential Moving Average (DEMA)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The _length of the DEMA
Returns: The DEMA value
tema(_src, _length)
Calculates the Triple Exponential Moving Average (TEMA)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The _length of the TEMA
Returns: The TEMA value
w2ma(_src, _length)
Calculates the Normalized Double Moving Average (N2MA)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The _length of the N2MA
Returns: The N2MA value
wma(_src, _length)
Calculates the Normalized Moving Average (NMA)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The _length of the NMA
Returns: The NMA value
nma(_open, _close, _length)
Calculates the Normalized Moving Average (NMA)
Parameters:
_open (float) : The open price series
_close (float) : The close price series
_length (simple int) : The _length for finding the highest and lowest values
Returns: The NMA value
lma(_src, _length)
Parameters:
_src (float)
_length (simple int)
zero_lag(_src, _length, gamma1, zl)
Calculates the Zero Lag Moving Average (ZeroLag)
Parameters:
_src (float) : The source series for calculation
_length (simple int) : The length for the moving average
gamma1 (simple int) : The coefficient for calculating 'd'
zl (simple bool) : Boolean flag for applying Zero Lag
Returns: An array containing the ZeroLag Moving Average and a boolean flag indicating if it's flat
copyright HPotter, thanks for that great function
chebyshevI(src, len, ripple)
Calculates the Chebyshev Type I Filter
Parameters:
src (float) : The source series for calculation
len (int) : The length of the filter
ripple (float) : The ripple factor for the filter
Returns: The output of the Chebyshev Type I Filter
math from Pafnuti Lwowitsch Tschebyschow (1821–1894)
Thanks peacefulLizard50262 for the find and translation
chebyshevII(src, len, ripple)
Calculates the Chebyshev Type II Filter
Parameters:
src (float) : The source series for calculation
len (int) : The length of the filter
ripple (float) : The ripple factor for the filter
Returns: The output of the Chebyshev Type II Filter
math from Pafnuti Lwowitsch Tschebyschow (1821–1894)
Thanks peacefulLizard50262 for the find
wavetrend(_src, _n1, _n2)
Calculates the WaveTrend indicator
Parameters:
_src (float) : The source series for calculation
_n1 (simple int) : The period for the first EMA calculation
_n2 (simple int) : The period for the second EMA calculation
Returns: The WaveTrend value
f_getma(_type, _src, _length, ripple)
Calculates various types of moving averages
Parameters:
_type (simple string) : The type of indicator to calculate
_src (float) : The source series for calculation
_length (simple int) : The length for the moving average or indicator
ripple (simple float)
Returns: The calculated moving average or indicator value
f_getfilter(_type, _src, _length)
Calculates various types of filters
Parameters:
_type (simple string) : The type of indicator to calculate
_src (float) : The source series for calculation
_length (simple int) : The length for the moving average or indicator
Returns: The filtered value
f_getoszillator(_type, _src, _length)
Calculates various types of Deviations and other indicators
Parameters:
_type (simple string) : The type of indicator to calculate
_src (float) : The source series for calculation
_length (simple int) : The length for the moving average or indicator
Returns: The calculated moving average or indicator value
Exponential ADR with Price TargetsThis script is designed to help you analyze price movements in the financial markets by calculating the Average Daily Range (ADR), adjusting it based on exponentiality and generating price targets based on that range.
The ADR represents the average range between the highest and lowest prices of a trading instrument during a specific period. It gives you an idea of how much the price typically moves in a day. In this script, we calculate the ADR using Simple Moving Averages (SMA) of the high and low prices over a certain length of time. You can customize this length according to your preference.
To make the ADR smoother and more responsive to recent price changes, we apply an Exponential Moving Average (EMA) to the ADR values. The EMA places more weight on recent data, giving you a more up-to-date measure of the ADR. The length of the EMA is also adjustable.
Once we have the Exponential ADR, we can generate price targets based on it. Price targets are potential levels where the price may reach in the future. We calculate these targets by adding or subtracting a certain multiple of the Exponential ADR from the current closing price. The multiple is determined by a parameter called the "Target Multiplier." You can adjust this value to control the distance of the price targets from the closing price.
In addition to plotting the Exponential ADR as a histogram on the chart, we create a table that displays the price targets. The table shows three bullish (positive) targets and three bearish (negative) targets. The targets are labeled as "Bull Target" or "Bear Target" followed by a number indicating the target's order. For each target, we display the corresponding price level.
To estimate the potential price levels, we used a formula that takes into account the current closing price and a value called the Exponential Average Daily Range (Exponential ADR). The Exponential ADR represents the average range of price movement over a specific period.
To calculate the price targets, we multiplied the Exponential ADR by a user-defined value called the target multiplier. This target multiplier allows traders to control the distance of the price targets from the current price. The resulting value indicates the desired distance from the current price for each target level.
For bullish targets, we added the calculated value to the current closing price. This suggests potential upward movement in the price. On the other hand, for bearish targets, we subtracted the calculated value from the current closing price. This indicates potential downward movement in the price.
By providing multiple target levels, such as level 1, level 2, and level 3, traders can assess different scenarios and potential price outcomes. These target levels help traders identify possible price levels where they might consider taking profit or adjusting their trading positions.
It's important to note that these price targets are not guaranteed to be reached, but they serve as reference points based on historical price behavior and the Exponential ADR. Traders can use them as part of their overall trading strategy and decision-making process.
Adjust the input parameters according to your desired settings, such as the ADR length, EMA length, target multiplier, table position, and table style. The indicator will then calculate and display the Exponential ADR and price targets on the chart, helping you identify potential levels of support and resistance for your trading decisions.
Fast EMA above Slow EMA with MACD (by Coinrule)An exponential moving average ( EMA ) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average . An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average ( SMA ), which applies an equal weight to all observations in the period.
Moving average convergence divergence ( MACD ) is a trend-following momentum indicator that shows the relationship between two moving averages of a security’s price. The MACD is calculated by subtracting the 26-period exponential moving average ( EMA ) from the 12-period EMA .
The result of that calculation is the MACD line. A nine-day EMA of the MACD called the "signal line," is then plotted on top of the MACD line, which can function as a trigger for buy and sell signals. Traders may buy the coin when the MACD crosses above its signal line and sell—or short—the security when the MACD crosses below the signal line. Moving average convergence divergence ( MACD ) indicators can be interpreted in several ways, but the more common methods are crossovers, divergences, and rapid rises/falls.
The Strategy enters and closes the trade when the following conditions are met:
LONG
The MACD histogram turns bullish
EMA8 is greater than EMA26
EXIT
Price increases 3% trailing
Price decreases 1% trailing
This strategy is back-tested from 1 January 2022 to simulate how the strategy would work in a bear market and provides good returns.
Pairs that produce very strong results include AXSUSDT on the 5-minute timeframe. This short timeframe means that this strategy opens and closes trades regularly.
Additionally, the trailing stop loss and take profit conditions can also be changed to match your needs.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
Catching the Bottom (by Coinrule)This script utilises the RSI and EMA indicators to enter and close the trade.
The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. The RSI is displayed as an oscillator (a line graph) on a scale of zero to 100. The RSI can do more than point to overbought and oversold securities. It can also indicate securities that may be primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average (SMA), which applies an equal weight to all observations in the period.
The strategy enters and exits the trade based on the following conditions.
ENTRY
RSI has a decrease of 3.
RSI <40.
EMA100 has crossed above the EMA50.
EXIT
RSI is greater than 65.
EMA9 has crossed above EMA50.
This strategy is back tested from 1 April 2022 to simulate how the strategy would work in a bear market and provides good returns.
Pairs that produce very strong results include ETH on the 5m timeframe, BNB on 5m timeframe, XRP on the 45m timeframe, MATIC on the 30m timeframe and MATIC on the 2H timeframe.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
SUPER MACD📈 MACD Indicator Update - Version 2
🔹 New Features and Improvements:
1️⃣ New MACD Calculation Options:
Users can now choose from various Moving Averages to calculate the MACD. The default options are SMA (Simple Moving Average) and EMA (Exponential Moving Average), but there are 14 other versions available to experiment with:
SMA (Simple Moving Average)
EMA (Exponential Moving Average)
WMA (Weighted Moving Average)
RMA (Smoothed Moving Average)
HMA (Hull Moving Average)
JMA (Jurik Moving Average)
DEMA (Double Exponential Moving Average)
TEMA (Triple Exponential Moving Average)
LSMA (Least Squares Moving Average)
VWMA (Volume-Weighted Moving Average)
SMMA (Smoothed Moving Average)
KAMA (Kaufman’s Adaptive Moving Average)
ALMA (Arnaud Legoux Moving Average)
FRAMA (Fractal Adaptive Moving Average)
VIDYA (Variable Index Dynamic Average)
2️⃣ Improved Input Visibility and Organization:
We’ve reorganized the inputs so that the most commonly used ones are now placed at the beginning for quicker and more convenient configuration.
3️⃣ Bug Fixes and Code Improvements:
Minor bugs have been fixed, and the code has been optimized for better stability and performance. The code is now cleaner and fully functional in version 6.
4️⃣ Cometreon Public Library Integration:
To lighten the code and improve its modularity, we’ve integrated the Cometreon public library. This makes the code more efficient and reduces the need to duplicate common functions.
☄️ With this update, the MACD indicator becomes even more versatile and user-friendly, offering a wide range of calculation methods and an improved interface!
EMA curvesPlot EMAs for lengths 9, 21, 55 ,100, 200
An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average (SMA), which applies an equal weight to all observations in the period.
KAIRI RELATIVE INDEXAn old but gold Japanese indicator for Mean Reverting strategies and ideal for Pairs Trading...
The Kairi Relative Index measures the distance between closing prices and a Moving Average in percent value (generally SMA).
Extreme reading in the KRI are considered buy and sell signals.
Extreme readings will vary by asset, with more volatile assets reaching much higher and lower extremes that more sedate assets.
The KRI is not an accurate timing signal, and therefore, should be combined with other forms of analysis to generate trade signals.
You can calculate percent difference between the price and 10 different types of Moving Averages in this version of KAIRI as:
SMA : Simple Moving Average
EMA : Exponential Moving Average
WMA : Weighted Moving Average
TMA : Triangular Moving Average
VAR : Variable Index Dynamic Moving Average a.k.a. VIDYA
WWMA : Welles Wilder's Moving Average
ZLEMA : Zero Lag Exponential Moving Average
TSF : True Strength Force
HULL : Hull Moving Average
VWMA: Volume Veighted Moving Average
Personal advice: try using bigger length of Moving Averages like 50-100-200 for pairs and mean reversion strategies
STD Stepped Ehlers Optimal Tracking Filter MTF w/ Alerts [Loxx]STD Stepped Ehlers Optimal Tracking Filter MTF w/ Alerts is the traditional Ehlers Optimal Tracking Filter but with stepped price levels, access to multiple time frames, and alerts.
What is Ehlers Optimal Tracking Filter?
From "OPTIMAL TRACKING FILTERS" by John Ehlers:
"Dr. R.E. Kalman introduced his concept of optimum estimation in 1960. Since that time, his technique has proven to be a powerful and practical tool. The approach is particularly well suited for optimizing the performance of modern terrestrial and space navigation systems. Many traders not directly involved in system analysis have heard about Kalman filtering and have expressed an interest in learning more about it for market applications. Although attempts have been made to provide simple, intuitive explanations, none has been completely successful. Almost without exception, descriptions have become mired in the jargon and state-space notation of the “cult”.
Surprisingly, in spite of the obscure-looking mathematics (the most impenetrable of which can be found in Dr. Kalman’s original paper), Kalman filtering is a fairly direct and simple concept. In the spirit of being pragmatic, we will not deal with the full-blown matrix equations in this description and we will be less than rigorous in the application to trading. Rigorous application requires knowledge of the probability distributions of the statistics. Nonetheless we end with practically useful results. We will depart from the classical approach by working backwards from Exponential Moving Averages. In this process, we introduce a way to create a nearly zero lag moving average. From there, we will use the concept of a Tracking Index that optimizes the filter tracking for the given uncertainty in price movement and the uncertainty in our ability to measure it."
Included:
-Standard deviation stepping filter, price is required to exceed XX deviations before the moving average line shifts direction
-Selection of filtering based on source price, the moving average, or both; you can also set the Filter deviations to 0 for no filtering at all
-Toggle on/off bar coloring
-Toggle on/off signals
-Long/Short alerts
AMASling - All Moving Average Sling ShotThis indicator modifies the SlingShot System by Chris Moody to allow it to be based on 'any' Fast and Slow moving average pair. Open Long / Close Long / Open Short / Close Short alerts can be generated for automated bot trading based on the SlingShot strategy:
• Conservative Entry = Fast MA above Slow MA, and previous bar close below Fast MA, and current price above Fast MA
• Conservative Entry = Fast MA below Slow MA, and previous bar close above Fast MA, and current price below Fast MA
• Aggressive Entry = Fast MA above Slow MA, and price below Fast MA
• Aggressive Exit = Fast MA below Slow MA, and price above Fast MA
Entries and exits can also be made based on moving average crossovers, I initially put this in to make it easy to compare to a more standard strategy, but upon backtesting combining crossovers with the SlingShot appeared to produce better results on some charts.
Alerts can also be filtered to allow long deals only when the fast moving average is above the slow moving average (uptrend) and short deals only when the fast moving average is below the slow moving averages (downtrend).
If you have a strategy that can buy based on External Indicators you can use the 'Backtest Signal' which plots the values set in the 'Long / Short Signals' section.
The Fast, Slow and Signal Moving Averages can be set to:
• Simple Moving Average (SMA)
• Exponential Moving Average (EMA)
• Weighted Moving Average (WMA)
• Volume-Weighted Moving Average (VWMA)
• Hull Moving Average (HMA)
• Exponentially Weighted Moving Average (RMA) (SMMA)
• Linear regression curve Moving Average (LSMA)
• Double EMA (DEMA)
• Double SMA (DSMA)
• Double WMA (DWMA)
• Double RMA (DRMA)
• Triple EMA (TEMA)
• Triple SMA (TSMA)
• Triple WMA (TWMA)
• Triple RMA (TRMA)
• Symmetrically Weighted Moving Average (SWMA) ** length does not apply **
• Arnaud Legoux Moving Average (ALMA)
• Variable Index Dynamic Average (VIDYA)
• Fractal Adaptive Moving Average (FRAMA)
'Backtest Signal' and 'Deal State' are plotted to display.none, so change the Style Settings for the chart if you need to see them for testing.
Yes I did choose the name because 'It's Amasling!'
Any RibbonThis indicator displays a ribbon of two individually configured Fast and Slow and Moving Averages for a fixed time frame. It also displays the last close price of the configured time frame, colored green when above the band, red below and blue when interacting. A label shows the percentage distance of the current price from the band, (again red below, green above, blue interacting), when the price is within the band it will show the percentage distance from median of the band.
The Fast and Slow Moving Averages can be set to:
Simple Moving Average (SMA)
Exponential Moving Average (EMA)
Weighted Moving Average (WMA)
Volume-Weighted Moving Average (VWMA)
Hull Moving Average (HMA)
Exponentially Weighted Moving Average (RMA) (SMMA)
Linear regression curve Moving Average (LSMA)
Double EMA (DEMA)
Double SMA (DSMA)
Double WMA (DWMA)
Double RMA (DRMA)
Triple EMA (TEMA)
Triple SMA (TSMA)
Triple WMA (TWMA)
Triple RMA (TRMA)
Symmetrically Weighted Moving Average (SWMA) ** length does not apply **
Arnaud Legoux Moving Average (ALMA)
Variable Index Dynamic Average (VIDYA)
Fractal Adaptive Moving Average (FRAMA)
I wrote this script after identifying some interesting moving average bands with my AMACD indicator and wanting to see them on the price chart. As an example look at the interactions between ETHBUSD 4hr and the band of VIDYA 32 Open and VIDYA 39 Open. Or start from the good old BTC Bull market support band, Weekly EMA 21 and SMA 20 and see if you can get a better fit. I find the Double RMA 22 a better fast option than the standard EMA 21.
[SS]_TrendAVGZones_and_GoldenRatioMAThe _TrendAVGZones_and_GoldenRatioMA is an indicator that is composed first of a channel made of three price averages ( base average, middle lower and middle upper ) in red is the previous corrections average and in green the previous rises average. So that way we the setting of stop loss targets and price targets can be set up at first glance. It adjusts to any timeframe so no worries 'bout that.
Also I added two exponential moving averages ( white and silver lines ) on the chart which I modified their equations by multiplying as it follows :
is the simple modification I added to fine tune it's precision and after some trials and errors I finally found a perfect spot. Now I tried it with historical data of Bitcoin and when the two Golden Ratio EMA crosses there's a big move coming imminently : if the white one is on top of the silver one the trend is bullish inversely the white one finds itself under the silver line then it needs to cross to expect a reversal.
rphi = 0.6180339887498948 = is the conjugate root of the golden ratio also called the silver ratio
phi = 1.6180339887498948 = golden ratio
It should be used to find short to mid term price targets selling as well as buying ones. If you're a long term trader I suggest using trend lines analysis in combination with it.
I hope to make this indicator a community owned indicator so don't hesitate to perfect it so we can build the best tool traders can hope for ! Together we will no longer ask wen lambo? we will get it!
IF you've got any question you can always DM me
take care of yourselves you future millionaires :D
-SS
Exponentially Deviating Moving Average (MZ EDMA)Exponentially Deviating Moving Average (MZ EDMA) is derived from Exponential Moving Average to predict better exit in top reversal case.
EDMA Philosophy
EDMA is calculated in following steps:
In first step, Exponentially expanding moving line is calculated with same code as of EMA but with different smoothness (1 instead of 2).
In 2nd step, Exponentially contracting moving line is calculated using 1st calculated line as source input and also using same code as of EMA but with different smoothness (1 instead of 2).
In 3rd step, Hull Moving Average with 3/2 of EDMA length is calculated using final line as source input. This final HMA will be equal to Exponentially Deviating Moving Average.
EDMA Advantages
EDMA's main advantage is that in case of top price reversal it deviates from conventional EMA of 2*Length. This benefits in using EDMA for EMA cross with quick signals avoiding unnecessary crossovers. EDMA's deviation in case of top reversal can be seen as below:
EDMA presents better smoothened curve which acts as better Support and resistance. EDMA coparison with conventional EMA of 2*length of EDMA is as follows.
Additional Features
EMA Band: EMA band is shown on chart to better visualize EMA cross with EDMA.
Dynamic Coloring: Chikou Filter library is used for derivation of dynamic coloring of EDMA and its band.
Alerts: Alerts are provided of all trade signals. Weak buy/sell would trigger if EMA of 2*EDMA_length crosses EDMA. Strong buy/sell would trigger if EMA of same length as of EDMA crosses EDMA.
Trade Confirmation with Chikou Filter: Trend filteration from Chikou filter library is used as an option to enhance trades signals accuracy.
Defaults
Currently default EDMA and EMA1 length is set to 20 period which I've found better for higher timeframes but this can be adjusted according to user's timeframe. I would soon add Multi Timeframe option in script too. Chikou filter's period is set to 25.
MACD Alert [All MA in one] [Smart Crypto Trade (SCT)]This code is a gift from "Smart Crypto Trade (SCT)" group
MACD indicator contains 3 EMA, I think one of the best usage of MACD is trend detection and divergences.
In our indicator, you can select the type of Moving averages that used in macd.
You can using "MACD" based on several types of moving averages including:
Exponential Moving Average ( EMA )
Volume-Weighted Moving Average ( VWMA )
Simple Moving Average ( SMA )
Weighted Moving Average ( WMA )
Exponentially Weighted Moving Average (RMA) that used in RSI
Smoothed Moving Average ( SMMA )
Arnaud Legoux Moving Average ( ALMA )
Double EMA ( DEMA )
Double SMA (DSMA)
Double WMA (DWMA)
Double RMA (DRMA)
Triple EMA ( TEMA )
Triple SMA (TSMA)
Triple WMA (TWMA)
Triple RMA (TRMA)
Linear regression curve Moving Average ( LSMA )
Variable Index Dynamic Average ( VIDYA )
Fractal Adaptive Moving Average ( FRAMA )
In other words we tried to collect all the most popular MAs in our MACD indicator.
In addition, you can use four types of alert or alarm conditions for detection LONG or SHORT positions and trends. For this, you must set an alert in alert tab and set the condition based on four defaults conditions.
Enjoy
Multi-X by HamidBoxHello to All, introducing Multi Types Crossover strategy,
simply the best way of trading with Crossover and Crossunder Strategy, How it Works???
I added 5 types of Moving Averages,
1: Simple Moving Average ( SMA )
2: Exponential Moving Average ( EMA )
3: Weighted Moving Average ( WMA )
4: Volume Weighted Moving Average ( VWMA )
5: Relative Moving Average (RMA)
With this indicator, you can do scalping, You can trade not only with similar types of Moving Average indicators but also with different types of Moving Average indicators.
what is mean? like: Normal Condition is:
( Simple Moving Average ) Crossover to ( Simple Moving Average ), SMA x SMA
( Exponential Moving Average ) Crossover to ( Exponential Moving Average ), EMA x EMA
But we can Crossover with:
( Exponential Moving Average ) Crossover to ( Simple Moving Average ), EMA x SMA
( Simple Moving Average ) Crossover to ( Weighted Moving Average ), SMA x WMA
( Weighted Moving Average ) Crossover to ( Weighted Moving Average ), WMA x WMA
( Simple Moving Average ) Crossover to (Relative Moving Average), WMA x RMA
and also I added Moving Average ZONE in this tool, What does it work???
The zone will tell us what type of direction the market has, if the market is above the zone, it's mean we have a Bullish Trend, and if the market is below the zone, it means the market has a Bearish Trend,
so if you want to play on the safe side, never trade when the market is in Bearish Trend, and if you want to play on aggressive mood, you can skip Moving Average Zone section.
CPR, Camarilla & Moving AverageThis script is created primarily for Intraday trading but can also be used for short and long term trading. This is a combination of Central Pivot Range (CPR), Moving Averages and Camarilla Pivot levels (with inner levels). This helps you to combine the strategies of CPR and Moving Averages to identify the best trading opportunities with greater edge. Central Pivot Range and Camarilla pivots are taken from PivotBoss by Franc Ochoa.
Key features:
# Daily CPR levels
# Weekly CPR levels
# Monthly CPR levels
# Previous Day High and Lows
# Previous Week Highs and Lows
# Previous Month Highs and Lows
# Camarilla Pivots with inner Levels
# CPR Levels for the next Day, Week and Month
# 5 Simple moving averages and 5 Exponential Moving Averages
What separates this script from other scripts with CPR and Moving averages?
# One of the few indicators (if not the only one) which combines the 2 types of Moving Averages, CPR and also Camarilla Pivots.
# CPR Levels for not just the next Day, but for next Week(Weekly CPR) and Month(Monthly CPR) also.
# Hide the previous day's levels according to your wish. This is the most unique feature of this indicator. You can set the number of Daily CPR levels you want to load in the chart. This is not just for the Daily CPR but also for the Weekly and Monthly CPR also. This makes the chart less cluttered and prevents the candles from getting buried in the indicators. Please notice how the previous day's CPR levels are hidden in the displayed demo chart on the script page. In the chart, only one trading day's data is shown(by default).
# This script is OPEN SOURCE.
Strategies :
For CPR & Camarilla Strategies for intraday trading and swing trading refer to the book 'Secrets of a Pivot Boss: Revealing Proven Methods for Profiting in the Market' by Franklin O. Ochoa.
Moving averages strategies :
Moving averages can be combined and also used individually for several strategies
* 9 EMA can be used as trailing stop loss for strong moving trends that helps you to catch big moves.
* 20sma can be used not just trailing stop loss but also for taking re-entry to the trend.
* Golden cross - The golden cross occurs when a short-term moving average crosses over a major long-term moving average to the upside. This indicates a bullish turn in the market. Eg: 50 SMA cuts 200 SMA from below.
* Death Cross - The death cross occurs when the short term moving average crosses the long-term average from above. This indicates a bearish turn in the market. Eg: 50 SMA cuts 200 SMA from above.
* When 20 SMA is above 50 SMA and 20 SMA and 50 SMA are angling up like parallel lines, then it denotes bullish strength. If this happens right after Golden Cross, big moves to the upside can be expected.
* When 20 SMA is below 50 SMA and 20 SMA and 50 SMA are angling down like parallel lines, then it denotes bearish strength. If this happens right after Death Cross, big moves to the downside can be expected.
* When 20SMA and 50 SMA are going flat and crossing each other, then it denotes sideways sentiment.
Moving average strategies are taken from the book 'How to Make Money in Intraday Trading' by Ashwani Gujral. For learning more about how to combine CPR and Moving averages in your trading please refer to this book.
Pinescript v4 - The Holy Grail (Trailing Stop)After studying several other scripts, I believe I have found the Holy Grail! (Or perhaps I've just found a bug with Tradingview's Pinescript v4 language) Anyhow, I'm publishing this script in the hope that someone smarter than myself could shed some light on the fact that adding a trailing stop to any strategy seems to make it miraculously...no that's an understatement...incredulously, stupendously, mind-bendingly profitable. I'm talking about INSANE profit factors, higher than 200x, with drawdowns of <10%. Sounds too good to be true? Maybe it is...or you could hook it up to your LIVE broker, and pray it doesn't explode. This is an upgraded version of my original Pin Bar Strategy.
Recommended Chart Settings:
Asset Class: Forex
Time Frame: H1
Long Entry Conditions:
a) Exponential Moving Average Fan up trend
b) Presence of a Bullish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Short Entry Conditions:
a) Exponential Moving Average down trend
b) Presence of a Bearish Pin Bar
c) Pin Bar pierces the Exponential Moving Average Fan
Exit Conditions:
a) Trailing stop is hit
b) Moving Averages cross-back (optional)
c) It's the weekend
Default Robot Settings:
Equity Risk (%): 3 //how much account balance to risk per trade
Stop Loss (x*ATR, Float): 0.5 //stoploss = x * ATR, you can change x
Stop Loss Trail Points (Pips): 1 //the magic sauce, not sure how this works
Stop Loss Trail Offset (Pips): 1 //the magic sauce, not sure how this works
Slow SMA (Period): 50 //slow moving average period
Medium EMA (Period): 18 //medium exponential moving average period
Fast EMA (Period): 6 //fast exponential moving average period
ATR (Period): 14 // average true range period
Cancel Entry After X Bars (Period): 3 //cancel the order after x bars not triggered, you can change x
Backtest Results (2019 to 2020, H1, Default Settings):
AUDUSD - 1604% profit, 239.6 profit factor, 4.9% drawdown (INSANE)
NZDUSD - 1688.7% profit, 100.3 profit factor, 2.5% drawdown
GBPUSD - 1168.8% profit, 98.7 profit factor, 0% drawdown
USDJPY - 900.7% profit, 93.7 profit factor, 4.9% drawdown
USDCAD - 819% profit, 31.7 profit factor, 8.1% drawdown
EURUSD - 685.6% profit, 26.8 profit factor, 5.9% drawdown
USDCHF - 1008% profit, 18.7 profit factor, 8.6% drawdown
GBPJPY - 1173.4% profit, 16.1 profit factor, 7.9% drawdown
EURAUD - 613.3% profit, 14.4 profit factor, 9.8% drawdown
AUDJPY - 1619% profit, 11.26 profit factor, 9.1% drawdown
EURJPY - 897.2% profit, 6 profit factor, 13.8% drawdown
EURGBP - 608.9% profit, 5.3 profit factor, 9.8% drawdown (NOT TOO SHABBY)
As you can clearly see above, this forex robot is projected by the Tradingview backtester to be INSANELY profitable for all common forex pairs. So what was the difference between this strategy and my previous strategies? Check my code and look for "trail_points" and "trail_offset"; you can even look them up in the PineScript v4 documentation. They specify a trailing stop as the exit condition, which automatically closes the trade if price reverses against you.
I however suspect that the backtester is not properly calculating intra-bar price movement, and is using a simplified model. With this simplfied approach, the trailing stop code becomes some sort of "holy grail" generator, making every trade entered profitable.
Risk Warning:
This is a forex trading strategy that involves high risk of equity loss, and backtest performance will not equal future results. You agree to use this script at your own risk.
Hint:
To get more realistic results, and *maybe* overcome the intrabar simulation error, change the settings to: "Stop Loss Trail Points (pips)": 100
I am not sure if this eradicates the bug, but the entries and exits look more proper, and the profit factors are more believable.
