Stochastic StrategyThis strategy is designed to make trading decisions based on the Stochastic Oscillator (Stoch) indicator with settings of (7,2,2). The strategy opens a long (buy) position when the Stoch indicator crosses above the 50 level from below. Conversely, it opens a short (sell) position when the Stoch indicator crosses below the 50 level from above. Additionally, when a long position is opened, any existing short position is closed, and vice versa.
Key Parameters:
Stochastic Oscillator Settings: Length = 7, SmoothK = 2, SmoothD = 2.
Overbought Level: 80.
Oversold Level: 20.
Strategy Description:
The Stochastic Oscillator (Stoch) is calculated based on the closing price, high price, and low price with a period of 7, and both the %K and %D lines are smoothed with periods of 2.
When the %K line crosses above the oversold level (20), it generates a long (buy) signal.
When the %K line crosses below the overbought level (80), it generates a short (sell) signal.
The strategy visually marks long and short signals on the chart using upward and downward triangles, respectively.
The strategy automatically enters long or short positions when the respective conditions are met.
If a long position is opened, any existing short position is closed, and vice versa.
Please note that this is a basic example of a trading strategy and does not take into account all possible risk factors or optimizations. Before using this strategy in live trading, it's essential to thoroughly test and customize it to suit your specific needs, and carefully analyze the results. Trading carries risks, and it's important to use proper risk management techniques when implementing any trading strategy.
Wskaźniki i strategie
Hoffman Heiken BiasThis indicator uses a couple of different things including the Hoffman moving averages applied with heiken ashi bar data and some volatility to help determine when the bias of the market has shifted for the timeframe you are looking at.
MMI Auto Backtesting StrategyDescription:
A strategy based on ATR with auto-backtesting capabilities, Take Profit and Stop Loss (either Normal or Trailing). It allows you to select ranges of values and step for each parameter, and backtest the strategy on a multitude of input combinations at once. You can alternatively use a constant value for each parameter. The backtesting results strive to be as close as possible to those given by Tradingview Strategy Tester.
The strategy displays a table with results for different input combinations. This has columns showing current input combination as well as the following stats: Net Profit, Number of trades, % of Profitable trades, Profit Factor, Max Drawdown, Max Runup, Average Trade and Average number of bars in a trade.
You can sort the table by any column (including sorting by multiple columns at the same time) to find, for example, input combination that gives highest Net Profit (or, if sorting by multiple columns, to find input combination with the best balance of Net Profit and % of Profitable trades). You can filter by any column as well (or multiple columns at the same time), using logical expressions like "< value", "> value", "<= value", ">= value". And you can use logical expressions like "< value%" for Net Profit, Max Drawdown, Max Runup and Average trade to filter by percentage value. You will see a "↓" symbol in column's header if that column is sorted from Highest to Lowest, a "↑" symbol if it's sorted from Lowest to Highest and a "𐕢" symbol if that column is being filtered.
The table has customisable styles (like text color, background color of cells, etc.), and can show the total number of backtested combinations with the time taken to test them. You can also change Initial Capital and Position Size (either Contracts, Currency or % of Equity).
Parameters:
The following parameters are located in the "INPUTS (USUAL STRATEGY)" group, and control the behaviour of strategy itself (not the auto-backtesting functionality):
- Period: ATR Length
- Multiplier: ATR Multiplier
- DPO: length of the filtering moving average
- SL: stop loss
- TP: take profit
- Use Stop Loss: enable stop loss
- Stop Loss Mode: stop loss mode (either Normal or Trailing)
- Use Take Profit: enable take profit
- Wicks: use high & low price, or close price
The strategy also has various parameters separated by different groups:
- INPUTS (AUTO-BACKTESTING): has the same parameters as the "INPUTS (USUAL STRATEGY)" group, but controls the input combinations for auto-backtesting; all the numeric parameters have 3 values: F/V (from), T (to) and S (step); if the checkbox to the left of F/V parameter is off, the value of F/V will indicate the constant value used for that parameter (if the checkbox is on, the values will be from F/V to T using step S)
- STRATEGY: contains strategy related parameters like Initial Capital and Position Size
- BACKTESTING: allows you to display either Percentage, Absolute or Both values in the table and has checkboxes that allow you to exclude certain columns from the table
- SORTING: allows you to select sorting mode (Highest to Lowest or vice versa) and has checkboxes in case you want to sort by multiple columns at the same time
- FILTERING: has a text field for each column of the strategy where you can type logical expressions to filter the values
- TABLE: contains styling parameters
Many parameters have the "(i)" description marker, so hover over it to see more details.
Problems:
- The script works best on lower timeframes and continuous markets (trades 24/7), in other cases the backtesting results may vary from those that Tradingview shows
- The script shows closest results when Take Profit and Stop Loss are not used
- Max Runup percentage value is often wrong
Limitations:
- As we are limited by the maximum time a script can be running (which is 20s for Free plan and 40s for Paid plans), we can only backtest several hundreds of combinations within that timeframe (though it depends on the parameters, market and timeframe of the chart you use)
Strategy Gaussian Anomaly DerivativeConcept behind this Strategy :
Considering a normal "buy/sell" situation, an asset would be bought in average at the median price following a Gaussian like concept. A higher or lower average trend would significate that the current perceived value is respectively higher or lower than the current median price, which mean that the buyers are evaluating the price underpriced or overpriced.
This behaviour would be even more relevent depending on its derivative evolution.
Therefore, this Strategy setup is based on this Gaussian like concept anomaly of average close positionning compare to high-low average derivative, such as the derivative of the following ploted basic signal : 1-(high+low)/(2*close).
This Strategy can actually be used like a trend change and continuation strength indicator aswell.
In the Setup Signal part :
You can define the filtering of the basis signal "1-(high+low)/(2*close)" on EMA or SMA as you wish.
You can define the corresponding period and the threathold as a mutiply of the average 1/3 of all time value of the basis signal.
You can define the SMA filtering period of the Derivative signal and the corresponding threathold on the same mutiply of the average 1/3 of all time value of the derivative.
In the Setup Strategy part :
You can set up your strategy assesment based on Long and/or Short. You can also define the considered period.
The most successful tuned strategies I did were based on the derivative indicator with periods on the basis signal and the derivative under 30, can be 1 to 3 of te derivative and 7 to 21 for the basis signal. The threathold depends on the asset volatility aswell, 1 is usually the most efficient but 0 to 10 can be relevent depending on the situation I met. You can find an example of tuning for this strategy based on Kering's case hereafter.
I hoping that you will enjoy using this Strategy, don't hesitate to comment, to question, to correct or complete it ! I would be very curious about similar famous approaches that would have already been made.
Thank to you !
SOFEX High-End Indicators + BacktestingBINANCE:BTCUSDT.P BINANCE:ETHUSDT.P
Introducing the first publicly available suite of indicators for Bitcoin and Ethereum by Sofex - the High-End Indicators & Backtesting System.
🔬 Trading Philosophy
The High-End Indicators & Backtesting system offers both trend-following and mean-reversal algorithms to provide traders with a deep insight into the highly volatile cryptocurrency markets, known for their market noise and vulnerability to manipulation.
With these factors in mind, our indicators are designed to sidestep most potentially false signals. This is facilitated further by the "middle-ground" time frame (1 Hour) we use. Our focus is on the two largest cryptocurrencies: Bitcoin and Ethereum , which provide high liquidity, necessary for reliable trading.
Therefore, we recommend using our suite on these markets.
The backtesting version of the Sofex High-End Indicators includes mainly trend-following indicators. This is because our trading vision is that volatility in cryptocurrency markets is a tool that should be used carefully, and many times avoided. Furthermore, mean-reversal trading can lead to short-term profits, but we have found it less than ideal for long-term trading.
The script does not aim to make a lot of trades, or to always remain in a position and switch from long to short. Many times there is no direction and the market is in "random walk mode", and chasing trades is futile.
Based on our experience, it is preferable if traders remain neutral the majority of the time and only enter trades that can be exited in the foreseeable future. Trading just for the sake of it ultimately leads to loss in the long-run.
Expectations of performance should be realistic.
We also focus on a balanced take-profit to stop-loss ratio. In the default set-up of the script, that is a 2% : 2% (1:1) ratio. A relatively low stop loss and take profit build onto our idea that positions should be exited promptly. There are many options to edit these values, including enabling trailing take profit and stop loss. Traders can also completely turn off TP and SL levels, and rely on opposing signals to exit and enter new trades.
Extreme scenarios can happen on the cryptocurrency markets, and disabling stop-loss levels completely is not recommended. The position size should be monitored since all of it is at risk with no stop-loss.
We take pride in presenting this comprehensive suite of trading indicators, designed for both manual and automated use. Although automated use leads to increased efficiency, traders are free to incorporate any of our indicators into their own manual trading strategy.
⚙️ Indicators
By default, all indicators are enabled for both Long and Short trades.
Extreme Trend Breakouts
The Extreme Trend Breakouts indicator seeks to follow breakouts of support and resistance levels, while also accounting for the unfortunate fact that false signals can be generated on these levels. The indicator combines trend-breakout strategies with various other volatility and direction measurements. It works best in the beginning of trends.
Underpinning this indicator are renowned Perry Kaufman's Adaptive Moving Averages (PKAMA) alongside our proprietary adaptive moving averages. These dynamic indicators adjust their parameters based on recent price movements, attempting to catch trends while maintaining consistent performance in the long run.
In addition, our modification of the TTM Squeeze indicator further enhances the Extreme Trend Breakouts indicator, making it more responsive, especially during the initial stages of trends and filtering of "flat" markets.
High-Volatility Trend Follower
The High-Volatility Trend Follower indicator is based around the logic of evading market conditions where volatility is low (choppy markets) and aggressively following confirmed trends. The indicator works best during strong trends, however, it has the downside of entering trades at trend tops or bottoms.
This indicator also leverages our proprietary adaptive moving averages to identify and follow high-volatility trends effectively. Furthermore, it uses the Average Directional Index, Aroon Oscillator, ATR and a modified version of VWAP, to categorize trends into weak or strong ones. The VWAP indicator is used to identify the monetary (volume) inflow into a given trend, further helping to avoid short-term manipulations.
Low-Volatility Reversal
The Low-Volatility Reversal aims at plugging the holes that trend-following indicators ignore. It specifically looks for choppy markets. Using proven concepts such as Relative Strength Index and volume measurements, among others, this indicator finds local tops and bottoms with good accuracy. It works best in choppy markets with low to medium volatility. It has a downside that all reversals have, losing trades at the end of choppy markets and in the beginning of big trends.
This indicator, like the others, employs PKAMA in conjunction with our proprietary adaptive moving averages, and an Average PSAR indicator to seek out "sideways" markets. Furthermore, Bollinger Bands with an adaptive basis line is used, with the idea of trading against the short-term trends by looking at big deviations in price movement. The above mentioned indicators attempt to catch local tops and bottoms in markets.
Adaptive Trend Convergence
The Adaptive Trend Convergence aims at following trends while avoiding entering positions at local bottoms and tops. It does so by comparing a number of adaptive moving averages and looking for convergence among them. Adaptive filtering techniques for avoiding choppy markets are also used.
This indicator utilizes our proprietary adaptive moving averages, and an Average Price Range indicator to identify trend convergence and divergence effectively, preventing false signals during volatile market phases. It also makes use of Bollinger Bands with an adaptive moving average basis line and price-action adjusted deviation. Contrasting to the Low-Volatility Reversal condition described above, the Bollinger Bands used here attempt to follow breakouts outside of the lower and upper bands.
Double-Filtered Channel Breakouts
The Double-Filtered Channel Breakouts indicator is made out of adaptive channel-identifying indicators. The indicator then follows trends that significantly diverge from the established channels. This aims at following extreme trends, where rapid, continuous movements in either direction occur. This indicator works best in very strong trends and follows them relentlessly. However, these strong trends can end in strong reversals, and the indicator can be stopped out on the last trade.
Our Double-Filtered Channel Breakouts indicator is built on a foundation of adaptive channel indicators. We've harnessed the power of Keltner Channels and Bollinger Band Channels, with a similar approach used in the Adaptive Trend Convergence indicator. The basis and upper/lower bands of the channels do not rely on fixed deviation parameters, rather on adaptive ones, based on price action and volatility. This combination seeks to identify and follows extreme trends.
Direction Tracker
The Direction Tracker indicator is made out of a central slower, adaptive moving average that clearly recognizes global, long-term trends. Combined with direction and range indicators, among others, this indicator excels at finding the long-term trend and ignoring temporary pullbacks in the opposite direction. It works best at the beginning and middle of long and strong trends. It can fail at the end of trends and on very strong historical resistance lines (where sharp reversals are common).
Our Direction Tracker indicator integrates an adaptive SuperTrend indicator into its core, alongside our proprietary adaptive moving averages, to accurately identify and track long-term trends while mitigating temporary pullbacks. Furthermore, it uses Average True Range, ADX and other volatility indicators to attempt to catch unusual moves on the market early-on.
📟 Parameters Menu
To offer traders flexibility, our system comes with a comprehensive parameter menu:
Preset Selection : Choose between Bitcoin or Ethereum presets to tailor the indicators to your preferred cryptocurrency market.
Global Signal Direction: Set the global signal direction as Long, Short, or Both, depending on your trading strategy.
Global Sensitivity Parameter : Adjust the system's sensitivity to adapt to different trend-following conditions, particularly beneficial during higher-strength trends.
Source of Signals : Toggle individual indicators on or off according to your preference. By default, all indicators are enabled. Customize the indicators to trade Long, Short, or Both, aligning them with your desired market exposure.
Confirmation of Signals : Set the minimum number of confirmed signals on the same bar, ensuring signals are generated only when specific confirmation criteria are met. The default value is one, and it can be adjusted for both Long and Short signals.
Exit of Signals : You have options regarding Take-Profit (TP) and Stop-Loss (SL) levels. Enable TP/SL levels to exit trades at predetermined levels, or disable them to rely on direction changes for exits. Be aware that removing stop losses can introduce additional risk, and position sizing should be carefully monitored.
By enabling Trailing TP/SL, the system switches to a trailing approach, allowing you to:
- Place an initial customizable SL.
- Specify a level (%) for the Trailing SL to become active.
- When the activation level is reached, the system moves the trailing stop by a given Offset (%).
Additionally, you can enable exit at break-even, where the system places an exit order when the trail activation level is reached, accounting for fees and slippage.
Alert Messages : Define the fields for alert messages based on specific conditions. You can set up alerts to receive email, SMS, and in-app notifications. If you use webhooks for alerts, exercise caution, as these alerts can potentially execute trades without human supervision.
Backtesting : Default backtesting parameters are set to provide realistic backtesting performance:
- 0.04% Commission per trade (for both entries and exits)
- 3 ticks Slippage (highly dependent on exchange)
- Initial capital of $1000
- Order size of $1000
While the order size is equal to the initial capital, the script employs a 2% stop-loss order to limit losses and attempts to prevent risky trades from creating big losses. The order size is a set dollar value, so that the backtesting performance is linear, instead of using % of capital which may result in unrealistic backtesting performance.
Risk Disclaimer
Please be aware that backtesting results, while valuable for statistical overview, do not guarantee future performance in any way. Cryptocurrency markets are inherently volatile and risky. Always trade responsibly and do not risk more than you can afford to lose.
Market Breadth Strategy/Introduction
The Market Breadth Strategy (MBS) is a versatile strategy for trading the US stock market. MBS is suitable for traders with low, medium and high risk tolerance who prefer trading equities as an asset class on the 1 day timeframe. It combines mean reversion with trend following to keep you participating in the stock market for as long as is profitable.
/Signals
The strategy is long only. Four different signals are generated to ensure all opportunities the market presents are seized for profit. The first category of signals are triggered after a prolonged period of falling prices; usually during a bear market or severe correction, open your largest positions on this signal. The second category of signals are triggered at the end of the bear market, early in the recovery. They ensure you do not miss out on an early entry if you get stopped out of your initial positions, size them equal to the first category signal positions. The third category of signals are triggered late in the recovery from a bear market, severe correction or deep pullback. Open your smallest positions on this signal. The fourth category of signals are triggered at all times when the market experiences a significant pullback or time correction, these positions should be medium sized.
For optimum performance, whenever signals are triggered, traders are advised to open at least, a new long position. Buying the index is recommended for traders with low risk tolerance, buying sector, industry or thematic ETFs (after sufficient analysis) is recommended for traders with medium risk tolerance, while buying stocks (after sufficient analysis) is recommended for traders who want to take on higher risk for higher returns. Such traders may also combine positions in indices, groups and individual stocks for better performance.
/Interpretation
MBS will display an upward blue arrow signifying a buy signal after the candle closes. A label below the arrow will describe which signal was triggered and a number depicting the number of positions (they can be deactivated in the style settings). MBS will also display a downwards pink arrow above the candle, after a specified decline from the high, again when the candle closes. All open positions will be closed on this signal, it is the risk management feature of the strategy.
/Construction
The strategy is built using market breadth data from the US Exchanges where stocks are listed, it is not a mash-up of different indicators. A combination of the following data is used:
(i) the number of advancing and declining issues
(ii) the number of issues reaching new highs
(iii) the closing prices of issues relative to key moving averages
This data is analysed and used to generate the four categories of signals described previously, they are named;
(i) Bottom Signal - for buying at the market's potential bottom
(ii) Follow-Up Signal - for ensuring you do not miss the bottom
(iii)Follow-Through Signal - for buying strength after a downtrend
(iv) Buy-The-Dip Signal - for buying throwbacks in uptrends and pullbacks in downtrends
/Settings
This strategy works best with the default settings. Although the input parameters can be changed to suit your needs, it is not advisable to do so as it may affect the strategy's performance.
(i) The market regime filter checks to see if the market is in a regime of rising prices (bull market) or falling prices (bear market), long signals are avoided in bear market conditions.
(ii) The risk size is equivalent to a stop loss. It triggers an exit when price declines by a certain amount.
(iii) 'Downside' measures the participation of issues to the downside during a decline while 'Upside' measures the participation of issues to the upside after the decline; this is called 'follow through'.
(iv) The bottom interval determines the frequency of bottom signals issued in days.
(v) Dip size quantifies the dip to determine if it is large enough for a buy signal, the lower the number, the larger the dip.
(vi) Following interval sets the duration for following up on the bottom.
(vii) Bottoming interval resets the bottom for the next follow-up
/Strategy Results
The backtest results are based on a starting capital of $13,700 (convenient amount for retail traders) with $1000 position size (7% of equity and enough for two shares of SPY) and pyramiding of 10 consecutive positions. Commissions of 0.03% and slippage of 2 ticks are used to ensure the results are representative of real world trading conditions. The backtest results are available to view at the bottom of this page.
Note that past results are not indicative of future results. The strategy is backtested in ideal conditions, it has no predictive abilities and results from live trading may not achieve the 2.235 profit factor shown here as each trader may introduce subjectivity or interfere with its performance or market conditions might change significantly. Since the strategy was designed for the US stock market, it has been backtested on the SPY (representative of the US stock market) ETF (for consistency in price across brokers).
/Tickers
This strategy should be used preferably with the SPY ticker which is the ETF for the S&P500. Alternatively, it could be used with VOO and several other S&P500 ETFs or a CFD ticker such as SPX500USD and several others which are based on the futures product. The strategy may not be suitable for futures tickers like ES according to TradingView.
/Access
The MBS is an Invite-Only script hence, traders interested in this strategy should contact me privately to request access.
Time Session Filter - MACD exampleTime Session Filter in TradingView Strategy: A Comprehensive Guide
Welcome to this educational TradingView blog where we dive deep into the functionality and utility of the time session filter in trading strategies. It's interesting to note that the time session filter is a commonly overlooked feature in Pine Script, often not integrated into overall trading strategies. Yet, when used wisely, this tool can significantly enhance your trading approach. In essence, the session filter ensures that trades are only made within a specific, user-defined time frame. By incorporating this often-neglected building block, you can make your strategy more adaptable to various market conditions and trading preferences.
What is a Time Session Filter?
A time session filter is designed to:
Select Times of the Day to Trade: The filter allows you to choose specific hours during the day in which trades are allowed to be excecuted.
Toggle Days to Trade: You can decide which days of the week you want to trade, giving you the flexibility to avoid days that are historically not profitable for your strategy.
Close Trade When Session Ends: The filter can automatically close any open trade once the specified time session concludes, reducing the risk associated with holding positions outside your chosen time frame.
The user interface is streamlined, taking minimal space for the input sections, making it convenient to integrate with other indicators in your overall strategy script. In addition the script colors the background of the chart green when the timesession filter is on and makes the background red when the filter doesn't allow any trades. This helps you to visualise the selected timeframes in relation to chart patterns.
Best Practices for Time Selection
From my personal trading experience I share some input settings you can try to play around with:
Stocks: Trading stocks sometimes yield better results if you only trade in the mornings until lunchtime. This is the period when markets are generally more active, and traders are keenly participating.
Cryptocurrencies: For cryptocurrencies, it sometimes makes sense to avoid trading on Fridays, a day when futures contracts often expire. Various other market-moving events also typically occur on Fridays.
Random Selection: Interestingly, sometimes choosing a random selection of times and days can improve the script's performance, adding an element of unpredictability that might outperform more systematic approaches.
Strategy Overview
This strategy script incorporates various elements, including risk position size and MACD indicator, to provide a comprehensive trading strategy. For a detailed explanation of risk position sizing, please refer to this article:
For a complete understanding of the MACD indicator utilized, visit the following explanation:
Additionally, for high time frame trend filters, consult this resource for more info:
Educational Purposes and Risks
Please note that this script is for educational purposes and serves merely as an example of how to incorporate a time session filter into a trading strategy for pinescript. It is a simplified strategy without a fixed stop-loss, which can result in higher exposure to significant losses. The time session filter can be a powerful addition to your trading strategy, providing you with the tools to tailor your approach according to time-specific market conditions. By understanding its functionalities and best practices, you can make more informed trading decisions, but always remember that trading carries inherent risks.
Happy trading!
Doji Trading StrategyA doji names a trading session in which a security has an open and close that are virtually equal, which resembles a candlestick on a chart. The word doji comes from the Japanese phrase meaning “the same thing.” A doji candlestick is a neutral indicator that provides little information.
3kilos BTC 15mThe "3kilos BTC 15m" is a comprehensive trading strategy designed to work on a 15-minute timeframe for Bitcoin (BTC) or other cryptocurrencies. This strategy combines multiple indicators, including Triple Exponential Moving Averages (TEMA), Average True Range (ATR), and Heikin-Ashi candlesticks, to generate buy and sell signals. It also incorporates risk management features like take profit and stop loss.
Indicators
Triple Exponential Moving Averages (TEMA): Three TEMA lines are used with different lengths and sources:
Short TEMA (Red) based on highs
Long TEMA 1 (Blue) based on lows
Long TEMA 2 (Green) based on closing prices
Average True Range (ATR): Custom ATR calculation with EMA smoothing is used for volatility measurement.
Supertrend: Calculated using ATR and a multiplier to determine the trend direction.
Simple Moving Average (SMA): Applied to the short TEMA to smooth out its values.
Heikin-Ashi Close: Used for additional trend confirmation.
Entry & Exit Conditions
Long Entry: Triggered when the short TEMA is above both long TEMA lines, the Supertrend is bullish, the short TEMA is above its SMA, and the Heikin-Ashi close is higher than the previous close.
Short Entry: Triggered when the short TEMA is below both long TEMA lines, the Supertrend is bearish, the short TEMA is below its SMA, and the Heikin-Ashi close is lower than the previous close.
Take Profit and Stop Loss: Both are calculated as a percentage of the entry price, and they are set for both long and short positions.
Risk Management
Take Profit: Set at 1% above the entry price for long positions and 1% below for short positions.
Stop Loss: Set at 3% below the entry price for long positions and 3% above for short positions.
Commission and Pyramiding
Commission: A 0.07% commission is accounted for in the strategy.
Pyramiding: The strategy does not allow pyramiding.
Note
This strategy is designed for educational purposes and should not be considered as financial advice. Always do your own research and consider consulting a financial advisor before engaging in trading.
Currency Pair Strategy [ICEALGO]Indicator for trading with currency pairs
Get Access to ICEALGO indicators: icealgo.com
All scripts & content provided by ICEALGO are for informational & educational purposes only. Past performance does not guarantee future results.
SIMPLE LIMIT ORDER STRAT vSEAPHOSS
I'm in the process of refining a promising strategy and could use some help to optimize its potential. P&L is great, the various ratios have revealed opportunities to minimize the downside. I've kept the script private for now, prioritizing its enhancement first. If you're interested in collaborating and offering some insights, kindly send me a direct message.
Based RSI (BullDozz)Installation: To use this script, open TradingView and create a new Pine Script strategy. You can paste the code provided into the Pine Script editor.
Customizable Inputs: The script includes various input parameters that you can customize to fit your trading preferences. These parameters are defined using the input function and include values like length, TPPercent, and others. You can adjust these values based on your trading strategy.
Strategy Signals: The script generates buy and sell signals based on the conditions specified in the buySignal and sellSignal variables. These signals are derived from the analysis of the oscillator (osc) and the Relative Strength Index (rsi). When a buy signal occurs, the script enters a long position, and when a sell signal occurs, it enters a short position.
Take Profit: The script includes a take profit feature (useTP) that allows you to enable or disable take profit orders. When enabled, it calculates take profit levels based on the specified percent (TPPercent) and attaches them to the open positions.
Plotting: The script also visualizes the oscillator (osc) and a midline (0) on the chart using histogram-style bars. The colors of these bars change based on the oscillator's direction.
Risk Management and Positionsize - MACD exampleMastering Risk Management
Risk management is the cornerstone of successful trading, and it's often the difference between turning a profit and suffering a loss. In light of its importance, I share a risk management tool which you can use for your trading strategies. The script not only assists in position sizing but also comes with built-in technical features that help in market timing. Let's delve into the nitty-gritty details.
Input Parameter: MarginFactor
One of the key features of the script is the MarginFactor input parameter. This element lets you control the portion of your equity used for placing each trade. A MarginFactor of -0.5 means 50% of your total equity will be deployed in placing the position size. Although Tradingview has a built-in option to adjust position sizing in a same way, I personally prefer to have the logic in my pinecode script. The main reason is userexperience in managing and testing different settings for different charts, timeframes and instruments (with the same strategy).
Stoploss and MarginFactor
If your strategy has a 4% stop-loss, you can choose to use only 50% of your equity by setting the MarginFactor to -0.5. In this case, you are effectively risking only 2% of your total capital per trade, which aligns well with the widely-accepted rule of thumb suggesting a 1-2% risk per trade. Similar if your stoploss is only 1% you can choose to change the MarginFactor to 1, resulting in a positionsize of 200% of your equity. The total risk would be again 2% per trade if your stoploss is set to 1%.
Max Drawdown and MarginFactor
Your MarginFactor setting can also be aligned with the maximum drawdown of your strategy, seen during a backtested period of 2-3 years. For example, if the max drawdown is 15%, you could calibrate your MarginFactor accordingly to limit your risk exposure.
Option to Toggle Number of Contracts
The script offers the option to toggle between using a percentage of equity for position sizing or specifying a fixed number of contracts. Utilizing a percentage of equity might yield unrealistic backtest results, especially over longer periods. This occurs because as the capital grows, the absolute position size also increases, potentially inflating the accumulated returns generated by the backtester. On the other hand, setting a fixed number of contracts as your position size offers a more stable and realistic ROI over the backtested period, as it removes the compounding effect on position sizes.
Key Features Strategy
MACD High Time Frame Entry and Exit Logic
The strategy employs a high time frame MACD (Moving Average Convergence Divergence) to make entry and exit decisions. You can easily adjust the timeframe settings and MACD settings in the inputsection to trade on lower timeframes. For more information on the HTF MACD with dynamic smoothing see:
Moving Average High Time Frame Filter
To reduce market 'noise', the strategy incorporates a high time frame moving average filter. This ensures that the trades are aligned with the dominant market trend (trading the trend). In the inputsection traders can easily switch between different type of moving averages. For more information about this HTF filter see:
Dynamic Smoothing
The script includes a feature for dynamic smoothing. The script contains The timeframeToMinutes(tf) function to convert any given time frame into its equivalent in minutes. For example, a daily (D) time frame is converted into 1440 minutes, a weekly (W) into 10,080 minutes, and so forth. Next the smoothing factor is calculated by dividing the minutes of the higher time frame by those of the current time frame. Finally, the script applies a Simple Moving Average (SMA) over the MACD, SIGNAL, and HIST values, MA filter using the dynamically calculated smoothing factor.
User Convenience: One of the major benefits is that traders don't need to manually adjust the smoothing factor when switching between different time frames. The script does this dynamically.
Visual Consistency: Dynamic smoothing helps traders to more accurately visualize and interpret HTF indicators when trading on lower time frames.
Time Frame Restriction: It's crucial to note that the operational time frame should always be lower than the time frame selected in the input sections for dynamic smoothing to function as intended.
By incorporating this dynamic smoothing logic, the script offers traders a nuanced yet straightforward way to adapt High Time Frame indicators for lower time frame trading, enhancing both adaptability and user experience.
Limitations: Exit Strategy
It's crucial to note that the script comes with a simplified exit strategy, devoid of features like a stop-loss, trailing stop-loss or multiple take profits. This means that while the script focuses on entries and risk management, it might result in higher losses if market conditions unexpectedly turn unfavorable.
Conclusion
Effective risk management is pivotal for trading success, and this TradingView script is designed to give you a better idea how to implement positions sizing with your preferred strategy. However, it's essential to note that this tool should not be considered financial advice. Always perform your due diligence and consult with financial advisors before making any trading decisions.
Feel free to use this risk management tool as building block in your trading scripts, Happy Trading!
Dual-Supertrend with MACD - Strategy [presentTrading]## Introduction and How it is Different
The Dual-Supertrend with MACD strategy offers an amalgamation of two trend-following indicators (Supertrend 1 & 2) with a momentum oscillator (MACD). It aims to provide a cohesive and systematic approach to trading, eliminating the need for discretionary decision-making.
Key advantages over traditional single-indicator strategies:
- Dual Supertrend Validation: Utilizes two Supertrend indicators with different ATR periods and factors to confirm the trend direction. This double-check mechanism minimizes false signals.
- Momentum Confirmation: The MACD histogram acts as a momentum filter, confirming entries and exits, thus adding an extra layer of validation.
- Objective Entry and Exit: The strategy generates buy and sell signals based on a combination of trend direction and momentum, leaving no room for subjective interpretation.
- Automated Trade Management: The strategy includes built-in settings for commission, slippage, and initial capital, automating the trade execution process.
- Adaptability: The strategy allows for easy customization of all its parameters, adapting to a trader's specific needs and varying market conditions.
BTCUSD 8hr chart Long Condition
BTCUSD 6hr chart Long Short Condition
## Strategy, How it Works
The strategy operates on a set of clearly defined rules, primarily focusing on the trend direction confirmed by the Dual-Supertrend and the momentum as indicated by the MACD histogram.
### Entry Rules
- Long Entry: When both Supertrend indicators are bullish and the MACD histogram is above zero.
- Short Entry: When both Supertrend indicators are bearish and the MACD histogram is below zero.
### Exit Rules
- Exit long positions when either of the Supertrends turn bearish or the MACD histogram drops below zero.
- Exit short positions when either of the Supertrends turn bullish or the MACD histogram rises above zero.
### Trade Management
- The strategy uses a fixed commission rate and slippage in its calculations.
- Automated risk management features are integrated to avoid overexposure.
## Trade Direction
The strategy allows for trading in both bullish and bearish markets. Users can select their preferred trading direction ("long", "short", or "both") to align with their market outlook and trading objectives.
## Usage
- The strategy is best applied on timeframes where the trend is evident.
- Users can modify the ATR periods, factors for Supertrends, and MACD settings to suit their trading needs.
## Default Settings
- ATR Period for Supertrend 1: 10
- Factor for Supertrend 1: 3.0
- ATR Period for Supertrend 2: 20
- Factor for Supertrend 2: 5.0
- MACD Fast Length: 12
- MACD Slow Length: 26
- MACD Signal Smoothing: 9
- Commission: 0.1%
- Slippage: 1 point
- Trading Direction: Both
The strategy comes with these default settings to offer a balanced trading approach but can be customized according to individual trading preferences.
Strategy:Reversal-CatcherWhat
This is a plain and vanilla reversal based strategy for intraday (15m) timeframe on Futures prices of the assets.
Now what all it comprises of?
It finds out the dynamic support & resistance from Bollinger Band (20 period, 1.5 std dev).
It finds out the potential divergence of price deviation from 5 period exponential moving average (EMA).
If the previous candle (N-1) shows a divergence it confirms the reversal by checking the present candle (N) to be closed inside the Bollinger Band.
It confirms the momentum by checking RSI shows a crossover/crossunder to oversold (30) / overbought (70) region.
It also confirms whether the trend is up (then only reversal trade to short) or down (then only reversal trade to long). The trend is checked with EMA-21 and EMA-50.
Re-affirmation Condition : It re-affirms the position of two successive candles called as `hhLLong` and `hhLLShort` in the script.
Why
In Indian context, retail participants are pre-dominantly (yes- 80% of Indian daily volume) Options buyers mainly in weekly indices (Nifty, BankNifty, FinNifty, CNXMidcap, Sensex, Bankx .. well everyday is expiry now in India, except -- Thank God -- Saturday & Sunday).
And in Index Options the momentum plays a big role.
If one can catch a good reversal point the potential of high Risk-to-Reward trade (hence earn handsomely) is very likely (please note: there is no holy grail in trading. Nothing works 100%).
So this is the attempt to catch a reversal.
Re-affirmation of Reversal
hhLLong : It's a reversal point after an uptrend. It checks the relative positioning of current candle compared to that of previous candle. [The details are in the script. Check for variable hhLLong in script.
hhLLShort : It's a reversal point after a downtrend. It checks the relative positioning of current candle compared to that of previous candle. [The details are in the script. Check for variable hhLLShort in script.
Unique-ness
What's unique in it? Why we decided to publicly share this:
Already given the context of The Great Indian Options Buyers community. It should be helpful to them, we believe.
It takes Very Less Number of Trades with High Accuracy . Please check the result in NSE:NIFTY1! in 15m timeframe. 71% accuracy with roughly a trade in a month.
There is no point giving brokers' the brokerages taking 10 trades a day and ending not-so-good EoD. Better lets take less trades with better result possibility. .
Mention
There are many people uses this variation of Bolling Band, 5EMA
Many people use RSI, trends and relative positioning of candles.
--> We are grateful to all of them. It's really difficult to mention everyone's name. But all people somehow influence the thought process. Thanks for all of them.
Statutory Disclaimer
There is no silver bullet / holy grail in trading. Nothing works 100% time. One has to be careful about the loss (s)he can bear in case of the trade goes against.
We, as the author of this script, is not responsible for any trading or position decision one is taken based on the outcome of this.
It is our sole discretion to change, add, delete the portion or withdraw the whole script without any prior notice or intimation.
In Indian Context : We are not SEBI registered, will never be SEBI registered.
Linear Cross Trading StrategyLinear Cross Trading Strategy
The Linear Cross trading strategy is a technical analysis strategy that uses linear regression to predict the future price of a stock. The strategy is based on the following principles:
The price of a stock tends to follow a linear trend over time.
The slope of the linear trend can be used to predict the future price of the stock.
The strategy enters a long position when the predicted price crosses above the current price, and exits the position when the predicted price crosses below the current price.
The Linear Cross trading strategy is implemented in the TradingView Pine script below. The script first calculates the linear regression of the stock price over a specified period of time. The script then plots the predicted price and the current price on the chart. The script also defines two signals:
Long signal: The long signal is triggered when the predicted price crosses above the current price.
Short signal: The short signal is triggered when the predicted price crosses below the current price.
The script enters a long position when the long signal is triggered and exits the position when the short signal is triggered.
Here is a more detailed explanation of the steps involved in the Linear Cross trading strategy:
Calculate the linear regression of the stock price over a specified period of time.
Plot the predicted price and the current price on the chart.
Define two signals: the long signal and the short signal.
Enter a long position when the long signal is triggered.
Exit the long position when the short signal is triggered.
The Linear Cross trading strategy is a simple and effective way to trade stocks. However, it is important to note that no trading strategy is guaranteed to be profitable. It is always important to do your own research and backtest the strategy before using it to trade real money.
Here are some additional things to keep in mind when using the Linear Cross trading strategy:
The length of the linear regression period is a key parameter that affects the performance of the strategy. A longer period will smooth out the noise in the price data, but it will also make the strategy less responsive to changes in the price.
The strategy is more likely to generate profitable trades when the stock price is trending. However, the strategy can also generate profitable trades in ranging markets.
The strategy is not immune to losses. It is important to use risk management techniques to protect your capital when using the strategy.
I hope this blog post helps you understand the Linear Cross trading strategy better. Booost and share with your friend, if you like.
Linear On MACDUnlocking the Magic of Linear Regression in TradingView
In the ever-evolving world of financial markets, traders and investors seek effective tools to gauge price movements, make informed decisions, and achieve their financial goals. One such tool that has proven its worth over time is linear regression, a mathematical concept that has found its way into technical analysis and trading strategies. In this blog post, we will explore the magic behind linear regression, delve into its history, and understand how it's widely used as a technical indicator.
The Birth of Linear Regression: From Mathematics to Trading
Linear regression is a statistical method that aims to model the relationship between two variables by fitting a linear equation to observed data. The formula for a linear regression line is typically expressed as y = a + bx, where y is the dependent variable, x is the independent variable, a is the intercept, and b is the slope.
While the roots of linear regression trace back to the field of statistics, it didn't take long for traders and investors to recognize its potential in the financial world. By applying linear regression to historical price data, traders can identify trends, assess the relationship between variables, and even predict potential future price levels.
The Linear On MACD Strategy
Let's take a closer look at a powerful example of how linear regression is employed in a trading strategy right within TradingView. The "Linear On MACD" strategy harnesses the potential of linear regression in conjunction with the Moving Average Convergence Divergence (MACD) indicator. The goal of this strategy is to generate buy and sell signals based on the interactions between the predicted stock price and the MACD indicator.
Here's a breakdown of the strategy's components:
Calculation of Linear Regression: The strategy begins by calculating linear regression coefficients for the historical stock price based on volume. This helps predict potential future price levels.
Predicted Stock Price: The linear regression results are then used to plot the predicted stock price on the chart. This provides a visual representation of where the price could trend based on historical data.
Buy and Sell Signals: The strategy generates buy signals when certain conditions are met. These conditions include the predicted stock price being between the open and close prices, a rising MACD, and other factors that suggest a potential bullish trend. On the other hand, sell signals are generated based on MACD trends and predicted price levels.
Risk Management: The strategy also incorporates risk tolerance levels to determine entry and exit points. This ensures that traders take into account their risk appetite when making trading decisions.
Embracing the Magic of Linear Regression
As we explore the "Linear On MACD" strategy, we uncover the power of linear regression in aiding traders and investors. Linear regression, a mathematical marvel, seamlessly merges with technical analysis to provide insights into potential price movements. Its historical significance in statistics blends perfectly with the demands of modern financial markets.
Whether you're a seasoned trader or a curious investor, the Linear On MACD strategy exemplifies how a robust mathematical concept can be harnessed to make informed trading decisions. By embracing the magic of linear regression, you're tapping into a tool that continues to evolve alongside the financial world it empowers.
Disclaimer: The information provided in this blog post is for educational purposes only and does not constitute financial advice. Trading and investing carry risks, and it's important to conduct thorough research and consider seeking professional advice before making any trading decisions.
Trend Confirmation StrategyThe profitability and uniqueness of a trading strategy depend on various factors including market conditions, risk management, and the strategy's ability to capitalize on price movements. I'll describe the strategy provided and highlight its potential benefits and differences compared to other strategies:
Strategy Overview:
The provided strategy combines three technical indicators: Supertrend, MACD, and VWAP. It aims to identify potential entry and exit points by confirming trend direction and considering the proximity to the VWAP level. The strategy also incorporates stop-loss and take-profit mechanisms, as well as a trailing stop.
Unique Aspects and Potential Benefits:
Trend Confirmation: The strategy uses both Supertrend and MACD to confirm the trend direction. This dual confirmation can increase the likelihood of accurate trend identification and filter out false signals.
VWAP Confirmation: The strategy considers the proximity of the price to the VWAP level. This dynamic level can act as a support or resistance and provide additional context for entry decisions.
Adaptive Stop Loss: The strategy sets a stop-loss range, which helps provide some tolerance for minor price fluctuations. This adaptive approach considers market volatility and helps prevent premature stop-loss triggers.
Trailing Stop: The strategy incorporates a trailing stop mechanism to lock in profits as the trade moves in the desired direction. This can potentially enhance profitability during strong trends.
Partial Profit Booking: While not explicitly implemented in the provided code, you could consider booking partial profits when the MACD shows a crossover in the opposite direction. This aspect could help secure gains while still keeping exposure to potential further price movements.
Key Differences from Other Strategies:
Dual Indicator Confirmation: The combination of Supertrend and MACD for trend confirmation is a unique aspect of this strategy. It adds an extra layer of filtering to enhance the accuracy of entry signals.
Dynamic VWAP: Incorporating the VWAP level into the decision-making process adds a dynamic element to the strategy. VWAP is often used by institutional traders, and its inclusion can provide insights into the market sentiment.
Adaptive Stop Loss and Trailing: The strategy's use of an adaptive stop-loss range and a trailing stop can help manage risk and protect profits more effectively during changing market conditions.
Partial Profit Booking: The suggestion to consider partial profit booking upon MACD crossovers in the opposite direction is a practical approach to secure gains while staying in the trade.
Caution and Considerations:
Backtesting: Before deploying any strategy in real trading, it's crucial to thoroughly backtest it on historical data to understand its performance under various market conditions.
Risk Management: While the strategy has built-in risk management mechanisms, it's essential to carefully manage position sizes and overall portfolio risk.
Market Conditions: No strategy works well in all market conditions. It's important to be flexible and adjust the strategy or refrain from trading during particularly volatile or unpredictable periods.
Continuous Monitoring: Even though the strategy includes automated components, continuous monitoring of the trades and market conditions is necessary.
Adaptability: Markets can change over time. Traders need to be prepared to adapt the strategy as necessary to stay aligned with evolving market dynamics.
Broadview Algorithmic StudioWelcome! This is the writeup for the Broadview Algorithmic Studio.
There are many unique features in this script.
- Broadview Underpriced & Overpriced
- Broadview Blackout Bollinger Bands
- Trailing Take Profit Suite
- Algorithmic Weights
- VSA Score
- Pip Change Log
- Activation Panel
- Weight Scanner
There are 116 primary inputs that allow users to algorithmically output unique DCA signal-sets. There are 85 inputs that allow users to control individual lengths, levels, thresholds, and multiplicative weights of the script. You will not find any other script with this many inputs, properly strung together for you to produce unlimited strategies for any market. The entire premise for the Broadview Algorithmic Studio is for users to be able to have extensive-cutting-edge features that allow them to produce more strategies, having control over every element that outputs a signal set. The number of unique strategies you can output with this script is VAST, and each continues to follow a safe DCA methodology.
This script is ready for use with 3Commas, interactive brokers, and other means of automation. It provides detailed information on Base Orders and Safety Orders, giving the number, cumulative spending, position average, and remaining balance for each SO in the series. Using this script we will explore the depths of strategic volume scaling, and the algorithms we use to determine spending.
Let me first start by saying the number of safe DCA-friendly signal-sets this script can output is absolutely staggering.
Let's limit the scope just to the Broadview Underpriced & Overpriced and Broadview Dominance indicators.
Each band of the Dominance Suite can be controlled individually with unique lengths, levels, and weights. This means the Dominance Suite can establish Bearish or Bullish dominance, in any market condition, and give it a unique overloading weight. The Broadview Underpriced & Overpriced indicator finally gives us the ability to establish these "market conditions" first with cycles. Of all the cycles this indicator establishes, the two primary are Underpriced & Overpriced. We determine this using a composite Overbought & Oversold with an Exponential Moving Average. So the script can now know, what cycle it is in, who is dominant during that cycle, and exactly how much weight in volume scaling the order should have.
Brand new is the ability for indicators of this level to be able to talk together in a single script. The Broadview Underpriced & Overpriced indicator and the Broadview Dominance indicator can inform one another across multiple vectors, create a unique market snapshot, and give that snapshot a unique weight every bar. The unique weight is compiled in the volume scaling math, thus giving us an automated-strategic-safe and quite efficient volume scaling for every order. In our coming updates we will explore this synergy to its very deepest layers. These indicators can be laced together in many ways, called vectors.
Only in the Algorithmic Studio do we explore these depths and yield those findings, features, and inputs to the user.
Let me take a quick break to explain another area-of-opportunity for our research and development.
The VSA Score is something we've tried before, but until the creation of the Broadview Blackout Bollinger Bands Auto Indicator it was not possible. The concept we want to explore is "Positional Honing". Over time we want users and the script itself to be able to understand the difference between a script-config that produces a high number of Hits, from a configuration that produces a high number of "Misses". The Volume Scaling Accuracy Score uses the BBB Auto Indicator as a heavily reliable, non-repainting, method of determining what the very-best signals for increased volume-scaling are.
Increased volume scaling is denoted by the near-white highlighter line running vertically. This line will either fall inside the BBB Auto Indicator bands (which are hidden), or, they will fall below and outside the BBB Auto bands. If increased spending happens inside the bands it's a "Miss". If increased spending happens below and outside the bands, it's a Hit. Oftentimes misses are actually pretty good spots for extra spending, which helps lower your position average, but Hits are always better. The Hits that the BBB Auto Indicator provides are extremely good.
Let's talk about the Trailing Take Profit Suite. This suite allows us to set a trailing take profit which is a feature that lets one maximize their profits. If the trailing take profit is engaged, then when the regular take profit is hit, it will trigger, denoted in red vertical lines, and the trailing take profit will look for a specified rate of change before it actually takes profit. This usually helps traders in those times when their regular take profit was set too low, allowing them to maximize their profits with a Trailing Take Profit.
For the moment, let's think about our scores. In the dashboard you'll notice a score beginning the Pip Change Log, the VSA Score, and the Activation Panel.
These scores use a new kind of logistic correlation formula where 4 digits are given to activation, rather than 1. This is to allow room for a future concept in AI we call "Deadzones" or you can think of it as impedance. This is not a bias in logistic regression. It's an entirely different concept. A neuron, which a perceptron attempts to mimic, has a bias.. but it also has a sort of electrical resistance. This is because a neuron is individually-alive entity. So a perceptron, as it were, would need to have both a bias and a natural resistance, or deadzone.
It is a lot of fun to watch the scores and how they react during playback. They tend to smooth trends but are also quite quick to correct to accuracy. In the future we will add the deadzones and biases to the scores. This should help both users and the script produce better signal sets. The Pip Change Log is an indicator that measures Rate of Change in Pips. This is one that I am particularly excited to study, as I am a huge fan of ROC. The Activation Panel shows these scores for 4 primary indicators: On Balance Volume, Relative Strength Index, Average Directional Index, and Average True Range.
Having the Pip Change Log, VSA Score, and Activation Panel up on the dashboard with their logistic correlation scores allows traders to study markets and setups quite intimately. The weight scanner at the bottom allows users to track the cumulative applied multiplicative weights during playback. The massive number of inputs, connected vectors of indicators, input-weights, lengths, levels, and thresholds sets up all the algorithmic infrastructure for powerusers to explore every idea and strategy output they could imagine. Also with the connected vector infrastructure we can deepen our indicators in a way where, "How they talk to each other.", comes first in every development conversation.
The Algorithmic Studio is for the Power-user.
These are not basic equations coming together to determine spending. This is a massive multi-layered-perceptron with everything from Trailing-Take-Profits to strategic-automatic algorithmic downscaling. The Broadview Algorithmic Studio gives a home to the poweruser who wants access to everything in a trading and investing AI, right up until the backpropagation. The Broadview Algorithmic Studio, gives users the ability to sit in the chair of the would-be AI.
Thank you.
Financial Ratios Fundamental StrategyWhat are financial ratios?
Financial ratios are basic calculations using quantitative data from a company’s financial statements. They are used to get insights and important information on the company’s performance, profitability, and financial health.
Common financial ratios come from a company’s balance sheet, income statement, and cash flow statement.
Businesses use financial ratios to determine liquidity, debt concentration, growth, profitability, and market value.
The common financial ratios every business should track are
1) liquidity ratios
2) leverage ratios
3)efficiency ratio
4) profitability ratios
5) market value ratios.
Initially I had a big list of 20 different ratios for testing, but in the end I decided to stick for the strategy with these ones :
Current ratio: Current Assets / Current Liabilities
The current ratio measures how a business’s current assets, such as cash, cash equivalents, accounts receivable, and inventories, are used to settle current liabilities such as accounts payable.
Interest coverage ratio: EBIT / Interest expenses
Companies generally pay interest on corporate debt. The interest coverage ratio shows if a company’s revenue after operating expenses can cover interest liabilities.
Payables turnover ratio: Cost of Goods sold (or net credit purchases) / Average Accounts Payable
The payables turnover ratio calculates how quickly a business pays its suppliers and creditors.
Gross margin: Gross profit / Net sales
The gross margin ratio measures how much profit a business makes after the cost of goods and services compared to net sales.
With this data, I have created the long and long exit strategy:
For long, if any of the 4 listed ratios,such as current ratio or interest coverage ratio or payable turn ratio or gross margin ratio is ascending after a quarter, its a potential long entry.
For example in january the gross margin ratio is at 10% and in april is at 15%, this is an increase from a quarter to another, so it will get a long entry trigger.
The same could happen if any of the 4 listed ratios follow the ascending condition since they are all treated equally as important
For exit, if any of the 4 listed ratios are descending after a quarter, such as current ratio or interest coverage ratio or payable turn ratio or gross margin ratio is descending after a quarter, its a potential long exit.
For example in april we entered a long trade, and in july data from gross margin comes as 12% .
In this case it fell down from 15% to 12%, triggering an exit for our trade.
However there is a special case with this strategy, in order to make it more re active and make use of the compound effect:
So lets say on july 1 when the data came in, the gross margin data came descending (indicating an exit for the long trade), however at the same the interest coverage ratio came as positive, or any of the other 3 left ratios left . In that case the next day after the trade closed, it will enter a new long position and wait again until a new quarter data for the financial is being published.
Regarding the guidelines of tradingview, they recommend to have more than 100 trades.
With this type of strategy, using Daily timeframe and data from financials coming each quarter(4 times a year), we only have the financial data available since 2016, so that makes 28 quarters of data, making a maximum potential of 28 trades.
This can however be "bypassed" to check the integrity of the strategy and its edge, by taking for example multiple stocks and test them in a row, for example, appl, msft, goog, brk and so on, and you can see the correlation between them all.
At the same time I have to say that this strategy is more as an educational one since it miss a risk management and other additional filters to make it more adapted for real live trading, and instead serves as a guiding tool for those that want to make use of fundamentals in their trades
If you have any questions, please let me know !
Elliott Wave with Supertrend Exit - Strategy [presentTrading]## Introduction and How it is Different
The Elliott Wave with Supertrend Exit provides automated detection and validation of Elliott Wave patterns for algorithmic trading. It is designed to objectively identify high-probability wave formations and signal entries based on confirmed impulsive and corrective patterns.
* The Elliott part is mostly referenced from Elliott Wave by @LuxAlgo
Key advantages compared to discretionary Elliott Wave analysis:
- Wave Labeling and Counting: The strategy programmatically identifies swing pivot highs/lows with the Zigzag indicator and analyzes the waves between them. It labels the potential impulsive and corrective patterns as they form. This removes the subjectivity of manual wave counting.
- Pattern Validation: A rules-based engine confirms valid impulsive and corrective patterns by checking relative size relationships and fib ratios. Only confirmed wave counts are plotted and traded.
- Objective Entry Signals: Trades are entered systematically on the start of new impulsive waves in the direction of the trend. Pattern failures invalidate setups and stop out positions.
- Automated Trade Management: The strategy defines specific rules for profit targets at fib extensions, trailing stops at swing points, and exits on Supertrend reversals. This automates the entire trade lifecycle.
- Adaptability: The waveform recognition engine can be tuned by adjusting parameters like Zigzag depth and Supertrend settings. It adapts to evolving market conditions.
ETH 1hr chart
In summary, the strategy brings automation, objectivity and adaptability to Elliott Wave trading - removing subjective interpretation errors and emotional trading biases. It implements a rules-based, algorithmic approach for systematically trading Elliott Wave patterns across markets and timeframes.
## Trading Logic and Rules
The strategy follows specific trading rules based on the detected and validated Elliott Wave patterns.
Entry Rules
- Long entry when a new impulsive bullish (5-wave) pattern forms
- Short entry when a new impulsive bearish (5-wave) pattern forms
The key is entering on the start of a new potential trend wave rather than chasing.
Exit Rules
- Invalidation of wave pattern stops out the trade
- Close long trades on Supertrend downturn
- Close short trades on Supertrend upturn
- Use a stop loss of 10% of entry price (configurable)
Trade Management
- Scale out partial profits at Fibonacci levels
- Move stop to breakeven when price reaches 1.618 extension
- Trail stops below key swing points
- Target exits at next Fibonacci projection level
Risk Management
- Use stop losses on all trades
- Trade only highest probability setups
- Size positions according to chart timeframe
- Avoid overtrading when no clear patterns emerge
## Strategy - How it Works
The core logic follows these steps:
1. Find swing highs/lows with Zigzag indicator
2. Analyze pivot points to detect impulsive 5-wave patterns:
- Waves 1, 3, and 5 should not overlap
- Waves 3 and 5 must be longer than wave 1
- Confirm relative size relationships between waves
3. Validate corrective 3-wave patterns:
- Look for overlapping, choppy waves that retrace the prior impulsive wave
4. Plot validated waves and Fibonacci retracement levels
5. Signal entries when a new impulsive wave pattern forms
6. Manage exits based on pattern failures and Supertrend reversals
Impulsive Wave Validation
The strategy checks relative size relationships to confirm valid impulsive waves.
For uptrends, it ensures:
```
Copy code- Wave 3 is longer than wave 1
- Wave 5 is longer than wave 2
- Waves do not overlap
```
Corrective Wave Validation
The strategy identifies overlapping corrective patterns that retrace the prior impulsive wave within Fibonacci levels.
Pattern Failure Invalidation
If waves fail validation tests, the strategy invalidates the pattern and stops signaling trades.
## Trade Direction
The strategy detects impulsive and corrective patterns in both uptrends and downtrends. Entries are signaled in the direction of the validated wave pattern.
## Usage
- Use on charts showing clear Elliott Wave patterns
- Start with daily or weekly timeframes to gauge overall trend
- Optimize Zigzag and Supertrend settings as needed
- Consider combining with other indicators for confirmation
## Default Settings
- Zigzag Length: 4 bars
- Supertrend Length: 10 bars
- Supertrend Multiplier: 3
- Stop Loss: 10% of entry price
- Trading Direction: Both
