DCA EMA Simple Bot [Starbots]
This is a simple idea of DCA trading on EMA crosses. Strategy is not repainting.
The difference between this and any other strategy is, that this script allows you to preset DCA buy triggers at desired levels and customize each DCA order size independently. Alerts are working, this strategy is easily used for automatic trading.
I mainly trade on Cryptohopper, Pionex, 3commas. This was created for community, alerts are working and non-repainting. Should work on any other as well.
Trading Condition:
It's buying when Fast EMA crosses up Slow EMA. Set your paramters.
It's selling if EMA's crosses back, signaling a sell. Optional.
DCA:
You can enter DCA on 20 custom levels or layers. It buys DCA when price hits the plotted blue line on the chart that's set by input % triggers. (buy 1st DCA at 2% drop, buy 2nd DCA at 5% drop,...)
Set your Inital Capital and Pyramiding in Properties tab, Initial Order Size and DCA Order Size (lot1,lot2,lot3,..), Order Type are changed in strategy inputs.
-By default you can see that we buy when EMA's cross up and signal a buy for 10% of equity, if market is dropping you will then place a first DCA order ( 20% equity) at 2% drop (lower) from initial order. If market keeps dropping you have more DCA levels where you can buy and average down your holding position. For selling you can use Take profit and Stop Loss targets that averages down multiple open positions, it will sell it once it reaches your desirable Take Profit and close a deal. You can also close your trade if EMA signals a sell.
Pyramiding - number of orders you can open at a time
Your first buy order is pyramiding 1. To allow it to buy 1 DCA or merge one time, set pyramding to 2.
Want to DCA 10 times? Set pyramiding at 11. (+1 always)
More features:
- Profit Calendar
- Show Balance label before every new trade
- DCA table - visualize how much of your investment is used in trades. If a background of the table is green you are okay, if the background color is red - you are using more money for orders than you actually have.
Buy Orders << Strategy Equity/Capital
- Show / Hide DCA lines - if your chart processing is getting slow you should hide some DCA levels to speed it up
- Backtesting Range - for testing the strategy in different time windows
- Alerts
When all trades are closed on your chart, winning rate of the strategy is 100% actually.
Win rate is shown differently as it's actually closing and opening every trade individually by default in TradingView system. We merge positions together and average it down into one big position to later sell for a profit (DCA).
You use this Trading Algorithm at your own risk. Do not trade before testing or invest something you cannot afford to lose on markets.
Recherche dans les scripts pour "alert"
Bollinger Bands, RSI, and MA StrategyThe "Bollinger Bands, RSI and MA Strategy" is a trend-following strategy that combines the Bollinger Bands indicator, the Relative Strength Index (RSI), and a moving average (MA). It aims to identify potential entry and exit points in the market based on price volatility, momentum, and trend.
The strategy uses two Bollinger Bands with different standard deviations to create price channels. The default settings for the Bollinger Bands are a length of 20 periods and a standard deviation of 2.0. The upper and lower bands of the Bollinger Bands serve as dynamic resistance and support levels, respectively.
The RSI indicator is employed to gauge the strength of price momentum.
The strategy also incorporates a 50-period moving average (MA) to help identify the overall trend direction. When the price is above the MA, it suggests an uptrend, and when the price is below the MA, it suggests a downtrend.
The entry conditions for long trades are when the RSI is above the overbought level and there is no contraction in the Bollinger Bands. For short trades, the entry conditions are when the RSI is below the oversold level and there is no contraction in the Bollinger Bands.
The exit conditions for long trades are when the RSI drops below the overbought level or when the price closes below the 50-period MA.
For short trades, the exit conditions are when the RSI goes above the oversold level or when the price closes above the 50-period MA.
The strategy generates alerts for potential long and short entry signals, as well as for exit signals when the specified conditions are met. These alerts can be used to receive notifications or take further actions, such as placing trades manually or using automated trading systems.
It is important to note that this strategy serves as a starting point and should be thoroughly backtested and validated with historical data before applying it to live trading. Additionally, it is recommended to consider risk management techniques, including setting appropriate stop-loss and take-profit levels, to effectively manage trades.
Kioseff Trading - AI-Optimized RSIAI-Optimized RSI
Introducing AI-Optimized RSI: a streamlined solution for traders of any skill level seeking to rapidly test and optimize RSI. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized RSI learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and RSI straightforward.
Features
Purpose : Uncover optimal RSI settings and entry levels with precision. Say goodbye to random guesses and arbitrary indicator use—this tool provides clear direction based on data.
Target Performance : You set the goal, and AI-RSI seeks it out, whether it's maximizing profits, efficient trading, or achieving the highest win rate.
AI-Powered : With intelligent AI recommendations, the tool dynamically fine-tunes your RSI approach, steering you towards ideal strategy performance.
Rapid Testing : Evaluate thousands of RSI strategies.
Dual Direction : Perfect both long and short RSI strategies with equal finesse.
Deep Insights : Access detailed metrics including profit factor, PnL, win rate, trade counts, and more, all within a comprehensive strategy script.
Instant Alerts : Set alerts and trade.
Full Customization : Test and optimize all RSI settings, including cross levels, profit targets and stop losses.
Simulated Execution : Explore the impact of limit orders and other trade types through simulation.
Integrative Capability : Combine your own custom indicators or others from the TradingView community for a personalized optimization experience.
Flexible Timeframes : Set your optimization and backtesting to any date range.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Direction : This setting controls trade direction: Long or Short.
Entry Condition : Define RSI entry: Select whether to trigger trades on RSI crossunders or crossovers.
RSI Lengths Range : Choose the range of RSI periods to test and find the best one.The AI will find the best RSI period for you.
RSI Cross Range : Set the range for RSI levels where crosses trigger trade signals. The AI will find the best level for you.
Combinations : Select how many RSI strategies to compare.
Optimization Type : Choose the goal for optimization and the AI: profit, win rate, or efficiency.
Profit Target : Set your profit target with this setting.
Stop Loss : Decide your maximum allowable loss (stop loss) per trade.
Limit Order : Specify whether to include limit orders in the strategy.
Stop Type : Choose your stop strategy: a fixed stop loss or a trailing stop.
How to: Find the best RSI for trading
It's important to remember that merely having the AI-Optimized RSI on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal RSI settings and strategy.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for RSI lengths and cross ranges at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
The image above shows our chart prior to any optimization efforts.
Note: the settings shown above in the key settings section will be used to start our demonstration.
2. Follow AI’s suggestions
Optimization Prompt: After loading your strategy, the indicator will prompt you to change the RSI length range and RSI level range to a better performing range.
Continue changing the RSI length range and RSI level range to match the indicator's suggestions until "Best Found" is displayed!
The image above shows results after we applied the tool’s suggestions. New suggestions have appeared, and we will continue to apply them.
Continue to adjust settings as recommended by the optimizer. If no better options are found, the optimizer will suggest increasing the number of combinations. Repeat this process until the optimizer indicates that the optimal setting has been identified.
Success! With the "Best Found" notification, an optimized RSI is now active. The AI will keep refining the strategy based on ongoing performance, ensuring continuous optimization.
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple RSI-based trading strategies using specific metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Adaptive Learning Aggressiveness:
Description: When Adaptive Learning is enabled, the "Adaptive Learning Aggressiveness" setting controls how dynamically the AI adapts to market conditions using selected performance metrics.
Functionality: This setting impacts the AI's responsiveness to shifts in strategy performance. By adjusting this setting, you can control how quickly the AI moves away from strategies that may have been historically successful but are currently underperforming, towards strategies that are showing current promise.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
MACD Optimizer Pro [Kioseff Trading]Massive update! This script now includes 12 different moving averages and 30+ built-in technical indicators to enhance your trading strategy optimization! (:
This script (MACD Optimizer Pro) allows the user to optimize and test hundreds of MACD strategies, simultaneously, in under 40 seconds. Of course, theoretically, an unlimited number of trading strategies can be tested with the MACD Optimizer Pro. After the optimization period - the MACD Optimizer Pro will show the most profitable MACD strategy or, should you choose, the highest win-rate MACD strategy or the most-efficient MACD strategy!
Optimization results can be backtested and verified using the native TradingView backtester - which is included in the MACD Optimizer Pro - and made easy to use! This feature makes settings alerts a simple practice!
Features
Test hundreds of MACD strategies, simultaneously, in under 40 seconds.
Optimize long MACD strategies and short MACD strategies.
12 different built-in moving averages included to improve your MACD strategy.
30+ built-in technical indicators to improve your MACD strategy.
Runs as a strategy script - profit factor, PnL , win-rate, number of trades, max drawdown, equity curve and other pertinent statistics shown.
Alerts
Optimize any MACD setting
Profit targets, trailing stops, fixed stop losses, and a binary MACD strategy can all be tested.
Strategies can be optimized for highest win rate, highest net profit, most efficient profit.
Limit orders can be simulated.
External indicators can be used for optimization i.e. your own, custom-built indicator, an indicator from your favorite author, or almost any publicly available
TradingView indicator.
Date range for optimization and backtesting are configurable.
Explanation
The image above shows a list of configurations for the optimizer. You can
You can test hundreds of different MACD settings in under 40 seconds on any timeframe, asset, etc.
The image above shows additional settings to filter the outcome of your optimization testing. Additionally, you can test an unlimited number of profit targets and stop losses!
You can add one of several built-in TradingView indicators to filter trade entries.
The image above shows all built-in moving averages and TradingView indicators that can be incorporated into your MACD strategy.
Additionally, you can add your own, custom indicator to the optimization test, your favorite indicator by your favorite author or almost any publicly available indicator on TradingView.
The image above shows the settings section in which you can implement this feature.
The image above shows an example of the custom indicator feature! In this instance, I am using the public indicator titled "Self-Optimizing" RSI and requiring it to measure below a level prior to entry! Almost any custom indicator, your favorite indicator, etc. is compatible with this feature!
The MACD Optimizer has improved user friendliness over previous versions. The optimizer can be as simple or complex as you'd like - capable of handling both "easy" and "difficult" tasks at your discretion.
Additionally, you can configure the optimizer to prioritize MACD strategies that earn profit most efficiently!
The image above shows this feature in action.
You can also configure the optimizer to prioritize MACD strategies that achieve the highest win rate!
The image above shows this feature in action.
Instructions
The instructions below show a rudimentary approach to using the optimizer.
1. Build your strategy in the settings.
You should also disable the "Run a Backtest" feature to improve load times during optimization.
The image above shows my custom strategy settings.
Now that you've got some data on your chart - you should try "Freezing" the "Smoothing" setting for MACD . When doing this, the optimizer will test hundreds of MACD settings with a fixed "Smoothing" setting. Try using the best "Smoothing" setting you were able to find for your initial testing.
2. Take the best "Smoothing" setting and test various MACD and Signal Lengths.
The image above shows me configuring the MACD Optimizer to test different MACD line lengths and Signal line lengths with a fixed "smoothing" setting.
From the results, we can see that there are better MACD settings than what was shown in our initial test!
With this information we can execute a TradingView backtest.
3. Execute a TradingView Backtest.
You must enable the "Run a Backtest" feature to perform a TradingView backtest. Additionally, it's advised to enable the "STOP OPTIMIZATION" feature when performing a TradingView backtest. Enabling this feature will improve load times for the backtest to only a few seconds (since the optimizer won't look for the best setting when this feature is enabled).
The image above shows completion of the process!
From here, you can perform further testing, set alerts, etc.
Backtest Settings Shown
Initial Capital: The initial capital used for the shown backtests is $3,500 USD. Set the initial capital to replicate your true starting capital (: PnL for the MACD strategies (listed in table) is calculated using a starting capital of $10,000 USD.
Slippage: The slippage settings for the displayed backtest was set to 2 ticks.
Commission: Commission was adjusted to 0.1%.
Verify Price for Limit Orders was set to 2 ticks.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Thanks for checking this out!
Kioseff Trading - AI-Optimized Supertrend
AI-Optimized Supertrend
Introducing AI-Optimized Supertrend: a streamlined solution for traders of any skill level seeking to rapidly test and optimize Supertrend. Capable of analyzing thousands of strategies, this tool cuts through the complexity to identify the most profitable, reliable, or efficient approaches.
Paired with TradingView's native backtesting capabilities, the AI-Optimized Supertrend learns from historical performance data. Set up is easy for all skill levels, and it makes fine-tuning trading alerts and Supertrend straightforward.
Features
Rapid Supertrend Strategy Testing : Quickly evaluate thousands of Supertrend strategies to find the most effective ones.
AI-Assisted Optimization : Leverage AI recommendations to fine-tune strategies for superior results.
Multi-Objective Optimization : Prioritize Supertrend based on your preference for the highest win rate, maximum profit, or efficiency.
Comprehensive Analytics : The strategy script provides an array of statistics such as profit factor, PnL, win rate, trade counts, max drawdown, and an equity curve to gauge performance accurately.
Alerts Setup : Conveniently set up alerts to be notified about critical trade signals or changes in performance metrics.
Versatile Stop Strategies : Experiment with profit targets, trailing stops, and fixed stop losses.
Binary Supertrend Exploration : Test binary Supertrend strategies.
Limit Orders : Analyze the impact of limit orders on your trading strategy.
Integration with External Indicators : Enhance strategy refinement by incorporating custom or publicly available indicators from TradingView into the optimization process.
Key Settings
The image above shows explanations for a list of key settings for the optimizer.
Set the Factor Range Limits : The AI suggests optimal upper and lower limits for the Factor range, defining the sensitivity of the Supertrend to price fluctuations. A wider range tests a greater variety, while a narrower range focuses on fine-tuning.
Adjust the ATR Range : Use the AI's recommendations to establish the upper and lower bounds for the Average True Range (ATR), which influences the Supertrend's volatility threshold.
ATR Flip : This option lets you interchange the order of ATR and Factor values to quicky test different sequences, giving you the flexibility to explore various combinations and their impact on the Supertrend indicator's performance.
Strategies Evaluated : Adjust this setting to determine how many Supertrend strategies you want to assess and compare.
Enable AI Mode : Turn this feature on to allow the AI to determine and employ the optimal Supertrend strategy with the desired performance metric, such as the highest win rate or maximum profitability.
Target Metric : Adjust this to direct the AI towards optimizing for maximum profit, top win rates, or the most efficient profits.
AI Mode Aggressiveness : Set how assertively the AI pursues the chosen performance goal, such as highest profit or win rate.
Strategy Direction : Choose to focus the AI's testing and optimization on either long or short Supertrend strategies.
Stop Loss Type : Specify the stop loss approach for optimization—fixed value, a trailing stop, or Supertrend direction changes.
Limit Order : Decide if you want to execute trades using limit orders for setting your profit targets, stop losses, or apply them to both.
Profit Target : Define your desired profit level when using either a fixed stop loss or a trailing stop.
Stop Loss : Define your desired stop loss when using either a fixed stop loss or a trailing stop.
How to: Find the best Supertrend for trading
It's important to remember that merely having the AI-Optimized Supertrend on your chart doesn't automatically provide you with the best strategy. You need to follow the AI's guidance through an iterative process to discover the optimal Supertrend settings and strategy.
Optimizing Supertrend involves adjusting two key parameters: the Factor and the Average True Range (ATR). These parameters significantly influence the Supertrend indicator's sensitivity and responsiveness to price movements.
Factor : This parameter multiplies the ATR to determine the distance of the Supertrend line from the price. Higher values will create a wider band, potentially leading to fewer trade signals, while lower values create a narrower band, which may result in more signals but also more noise.
ATR (Average True Range) : ATR measures market volatility. By using the ATR, the Supertrend adapts to changing market volatility; a higher ATR value means a more volatile market, so the Supertrend adjusts accordingly.
During the optimization process, these parameters are systematically varied to determine the combination that yields the best performance based on predefined criteria such as profitability, win rate, or risk management efficiency. The optimization aims to find the optimal Factor and ATR settings.
1.Starting Your Strategy Setup
Begin by deciding your goals for each trade: your profit target and stop loss, or if all trades exit when Supertrend changes direction. You'll also choose how to manage your stops – whether they stay put (fixed) or move with the price (trailing), and whether you want to exit trades at a specific price (limit orders). Keep the initial settings for Supertrend Factor Range and Supertrend ATR Range at their default to give the tool a broad testing field. The AI's guidance will refine these settings to pinpoint the most effective ones through a process of comprehensive testing.
Demonstration Start: We'll begin with the settings outlined in the key settings section, using Supertrend's direction change to the downside as our exit signal for all trades.
2. Continue applying the AI’s suggestions
Keep updating your optimization settings based on the AI's recommendations. Proceed with this iterative optimization until the "Best Found" message is displayed, signaling that the most effective strategy has been identified.
While following the AI's suggestions, we've been prompted with a new suggestion: increase the
number of strategies evaluated. Keep following the AI's new suggestions to evaluate more strategies. Do this until the "Best Found" message shows up.
Success! We continued to follow the AI’s suggestions until “Best Found” was indicated!
AI Mode
AI Mode incorporates Heuristic-Based Adaptive Learning to fine-tune trading strategies in a continuous manner. This feature consists of two main components:
Heuristic-Based Decision Making: The algorithm evaluates multiple Supertrend-based trading strategies using metrics such as Profit and Loss (PNL), Win Rate, and Most Efficient Profit. These metrics act as heuristics to assist the algorithm in identifying suitable strategies for trade execution.
Online Learning: The algorithm updates the performance evaluations of each strategy based on incoming market data. This enables the system to adapt to current market conditions.
Incorporating both heuristic-based decision-making and online learning, this feature aims to provide a framework for trading strategy optimization.
AI Mode Settings
AI Mode Aggressiveness:
Description: The "AI Mode Aggressiveness" setting allows you to fine-tune the AI's trading behavior. This setting ranges from “Low” to “High”, with “High” indicating a more assertive trading approach.
Functionality: This feature filters trading strategies based on a proprietary evaluation method. A higher setting narrows down the strategies that the AI will consider, leaning towards more aggressive trading. Conversely, a lower setting allows for a more conservative approach by broadening the pool of potential strategies.
Optimization
Trading system optimization is immensely advantageous when executed with prudence.
Technical-oriented, mechanical trading systems work when a valid correlation is methodical to the extent that an objective, precisely-defined ruleset can consistently exploit it. If no such correlation exists, or a technical-oriented system is erroneously designed to exploit an illusory correlation (absent predictive utility), the trading system will fail.
Evaluate results practically and test parameters rigorously after discovery. Simply mining the best-performing parameters and immediately trading them is unlikely a winning strategy. Put as much effort into testing strong-performing parameters and building an accompanying system as you would any other trading strategy. Automated optimization involves curve fitting - it's the responsibility of the trader to validate a replicable sequence or correlation and the trading system that exploits it.
Quantitative mean reversion v4The code uses the concept of mean reversion. Mean reversion suggests that price over a period of time reverts back to its statistical mean. In simple terms, it means if a price has drifted apart from the statistical mean, after a certain amount of time, it will revert back to its statistical mean. This drift is measured via z-score. When the z-score value is high, the price is expected to revert. Besides, the higher the time frame you use, the lesser the drift is, so reduce the z-score in the tabs if you use higher time frames, else, vice-versa.
Based on the parameters, the code will provide a trade signal - both long and short, and entry and exit. You can use notifications for alerts. Please use the parameters in the options to find the best combinations for your stocks.
In the properties, you can use your own brokers commission, capital, to see if the strategy is profitable for your ticker in the long run or not. This code has been tested for profits for various assets in both crypto - Bitcoin futures , Ethereum futures -, and stocks - AMD , Apple , MSFT , etc.
This is not get rich quick scheme, and you have to be patient with it for the long run.
If you have any query, please feel free to ask in the comments sections.
If you want some new changes, please feel free to suggest
Currently, I am optimising the maximum time for holding a trade. Till that's completed, use this and please feel free to leave a feedback to make it better
Trend Follower Intraday [ Adjustable TF ]Trend Follower Intraday for 3 minute Time-Frame (Adjustable) , that has the time condition for Indian Markets as well.
Unlike the Free Scripts - Risk Management , Position Sizing , Partial Exit etc. are also included .
Send us a Message to know more about the strategy.
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The Timing can be changed to fit other markets, scroll down to "TIME CONDITION" to know more.
The commission is also included in the strategy .
The basic idea is when ,
1) EMA1 crosses above EMA2 , is a Long condition .
2) EMA1 crosses below EMA2 , is a Short condition .
3) Green Section indicates Long position.
4) Red Section indicates Short position.
5) Allowed hours specifies the trade entry timing.
6) ATR STOP is the stop-loss value on chart , can be adjusted in INPUTS.
7) Target 1 is the 1st target value on chart , can be adjusted in INPUTS.
8) RISK is Maximum Risk per trade for the intraday trade can be changed .
9) Total Capital used can be adjusted under INPUTS.
10) ATR TRAIL is used for trailing after entry, as mentioned in the inputs below.
11) Check trades under the list of trades .
12) Trade only in liquid stocks .
13) Risk only 1-5% of total capital.
14) Inputs can be changed for better back-test results, but also manually check the trades before setting alerts
15) SQUARE OFF TIME - As you change the time frame , also change the square-off time to the candle's closing time.
Eg: For 3min Time-frame , Hour = 2Hrs | Minute = 57min
16) Strategy stops for the day if you have a loss .
17) COMMISSION value is set to 20Rs and SLIPPAGE value is set to 2 . Go to properties to change it .
*The input values and the results are mentioned under "BACKTEST RESULTS" below*
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// ————————> RISK MANAGEMENT <——————— //
// ══════════════════════════════ //
Risk management is done based on max loss per trade and can be adjusted in the INPUTS.
// ═══════════════════════════ //
// ————————> POSITION SIZE <——————— //
// ═══════════════════════════ //
Quantity of each trade is different based on the loss
// ═════════════════════════ //
// ————————> PROPERTIES <——————— //
// ═════════════════════════ //
COMMISSION , SLIPPAGE ,RECALCULATE is already mentioned in the code.
COMMISSION can be charges , based on the broker charges.
// ═══════════════════════════════//
// ————————> TIME CONDITION <————————— //
// ═══════════════════════════════//
The time can be changed in the INPUT.
The Indian Markets open at 9:15am and closes at 3:30pm.
The 'Allowed hours' under Inputs specifies the time at which Entries should happen .
"Close All" function closes all the trades before 3pm , at the open of the next candle.
To change the time to close all trades , check INPUT.
All open trades get closed by 3pm , because some brokers don't allow you to place fresh intraday orders after 3pm .
// ═══════════════════════════════════════════════ //
// ————————> BACKTEST RESULTS ( 123 CLOSED TRADES ) <————————— //
// ═══════════════════════════════════════════════ //
INPUTS can be changed for better Back-Test results.
The strategy applied to NSE:JSWENERGY (3 min Time-Frame and with a capital of 3,00,000 ) gives us 81% profitability , as shown below
It was tested for a period a 6 months with a Profit Factor of 1.957 ,net Profit of 43,000Rs .
Sharpe Ratio = 0.745
Sortino Ratio = 2.091
No strategy in the world promises 100% profits in all market conditions , so always define your risk before trading.
Also check Back-Test results manually ,before setting Alerts
The Graph has a Linear Curve with Consistent Profits.
The INPUTS are as follows,
1) EMA1 ————————————————> 38
2) EMA2 ————————————————> 118
3) ALLOWED HRS ———————————> 9:35 TO 14:30
4) ATR STOP ——————————————> 3.2
5) RISK ——————————————————> 3000
6) ATR TRAIL ———————————————> 2.6
7) TARGET 1 ————————————————> 2.4
8) MAX POSITION VALUE ——————————> 3,00,000
8) MAX DRAWDOWN —————————————> 9,000
8) SQUARE-OFF ————————————————> 14:57
NSE:JSWENERGY
Apply it to your charts Now !
NSE:JSWENERGY
Send us a message for FREE TRIALS | Instant Access
Thank You ☺
APIBridge Nifty Options Algo StrategyUsing Pinescript, we will use charts of Cash/Future to trade in Options. Note this strategy works well with even the free version of TradingView.
The Relative Strength Index ( RSI ). Is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Increasing RSI shows increasing bullish momentum. Decreasing RSI shows increasing bearish momentum. We take RSI upper bound as 80 to indicate bullish momentum and RSI lower bound as 20 to indicate bearish momentum.
We use the above premise to create options buy-only strategy which trades in ATM strikes by default. This strategy requires very less margin (Minimum Rs . 15000).
Since this strategy uses underlying data (cash/future) to place trades in Options, please ignore the backtest of this strategy given by TradingView. TradingView does not provide options data but this strategy bypasses it.
Strategy Premise
The Relative Strength Index (RSI) is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Increasing RSI shows increasing bullish momentum. Decreasing RSI shows increasing bearish momentum. We take RSI upper bound as 80 to indicate bullish momentum and RSI lower bound as 20 to indicate bearish momentum.
We use the above premise to create options buy-only strategy which trades in ATM strikes by default. This strategy requires very less margin (Rs. 15000 should be sufficient).
NSE Options Algo Strategy Logic
Long Entry: When RSI goes above 80, send LE in an auto-calculated option strike Call. When RSI goes below 20, send LE in auto-calculated option strike Put.
Long Exit: When we hit Stop loss or Target. In case SL/TGT does not hit and reverse RSI goes above 80 send Long Exit in auto-calculated option. Put as per last trade; RSI goes below 20, send LX in auto-calculated option call as per last trade.
For Long and Short entry the order is fired in the option buying side with auto strike price selection.
Option Strategy Parameters for TraingView Charts
RSI Length(Mandatory): Number of bars used to calculated RSI.
Upper Band(Mandatory): To specify upper band of RSI.
Lower Band(Mandatory): For specifying lower band of RSI.
Use reversal from Upper Band (Optional): This will enable short entry when RSI is falling below 80 from upper band. Recommended to keep unchecked initially.
Use reversal from Lower Band (Optional): This will enable long entry when RSI is raising above 20 from lower band. Recommended to keep unchecked initially.
Quantity: We use this specify the trade quantity (for Nifty min 75)
Custom Stop Loss in Points: Movement in chart price against the momentum which will trigger exit in options positions
Custom Target in Points: Movement in chart price against the momentum which will trigger exit in options positions
Base symbol: This is the base instrument symbol like NIFTY or BANK NIFTY.
Strike distance from ATM: Our default strike selection is considered as first ATM option (with nearest distance, only 100s are considered ). This strike distance allows to calculate ATM options which are at fixed distance.
Expiry: Expiry of option. Weekly and monthly both expiry are allowed.
Instrument: For index instrument will be OPTIDX, for stock instrument will be OPTSTK
Strategy Tag: The Strategy of Nifty options configured in Api bridge.
Setting Up Alert
Before setting up the alert make sure that you have selected desired script, time frame, strategy settings, and APIbridge configuration. Click in settings add alert and paste {{strategy.order.comment}} in message box.
Important: Do not change any settings during live trading. It may break the sequence of exit for the correct call/put.
[XRP][1h] Chanu Delta inspired — Breakeven StrategyHello, this is my first TV contribution. I usually don't publish anything but the script is a quick review of an other contributor (Chanu Delta V3 script )
I reverse engineered this indicator today as I wanted to test it on other contracts. The original version (which aims to be traded on BTC) has been ported to XRP (as btc and xrp prices are narrowly correlated) then modified with a couple of what I believe are improvements:
- No backtest bias even with `security` function.
- Extra backtest bias validation, always trading on next bar as Crossover/under bias is confirmed
- Backtest with 2 ajustable TP, ajustable equity and breakeven option
- The current version is not design to use pyramiding as it would require extra logic to monitor the lifecycle of the position in the context of a study.
- Commented alerts examples with variables available in script scope so you can use them in alerts (just replace strategy with indicator and remove backtest related code block).
- Trade filling assumption set to 10, fees to 0.02 as the are default bybit maker fees and I advice to enter with trailing orders using a max of 2 ticks as offset to lower fees rather than a market order!
- Backtest and Alerts happen on barclose.
- No repaint guaranteed.
There are a thousand ways to improve it (adx/bb based dynamic TP/SL, order lifecycle, pyramiding...) but it seems to be a cool starting point.
Don't forget to have fun!
TTP Kent Strat PROKent Strat PRO trades breakouts using Bollinger Bands together with SuperTrend.
PRO features:
- 3commas bot alerts for long/short bots
- Custom JSON bots alerts
Features:
- Risk/reward ratio parameter
- Longs, shorts and combined positions.
- Breakout settings
- Trailing SL, trailing TP
- Use of latest candles to place the SL using a lookback parameter (how many candles to look back for a low/high price)
- Select your SL between the ATR trendline and the latest candle: the closest or furthest away value
- Show the trendline
- Backtest mode for accurate backtests
- Signal mode for live price accurate signals
- Date range backtesting
Filters:
- EMA 200 filter and timeframe selector. This filter can be used to trade with the trend: open longs on an uptrend and shorts on a downtrend.
- ADX filter using threshold. This filter can be used to filter entries where the trend is not very strong.
- ADX pointing up. ADX values pointing up and above certain threshold can improve entries.
- Relative volume filter based on the volume being X% above the MA of the Volume. Trading with volume can help filtering out bad trades.
Example setup:
1) pick BINANCE:ETHUSDT chart, 15 min chart
2) trade longs + shorts
3) pick ratio 3
4) trailing SL checked
5) trailing TP unchecked
7) stop loss "furthest"
8) candle loopback 30
9) BB period 21, dev 1, ATR filter on, atr period 5
10) EMA filter on, 15 min
11) ADX off
12) Volume filter on set to 60%
RSI Mean Reversion StrategyThis is a scalping strategy designed to be used for crypto trading. It uses an Exponential Moving Average with a default length of 100 in order to identify the trend of the market. If the price is trading above 100, it will only take long trades, and vice versa for shorts. It places long orders when the RSI value closes below 40, and the price is also above the 100 EMA. It places short orders when the RSI value is above 60, and the price is below the 100 EMA.
*Note: for custom alert messages to be read, "{{strategy.order.alert_message}}" must be placed into the alert dialogue box when the alert is set.
STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT [Loxx]STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones BT is the backtest strategy for "STD-Filterd, R-squared Adaptive T3 w/ Dynamic Zones " seen below:
Included:
This backtest uses a special implementation of ATR and ATR smoothing called "True Range Double" which is a range calculation that accounts for volatility skew.
You can set the backtest to 1-2 take profits with stop-loss
Signals can't exit on the same candle as the entry, this is coded in a way for 1-candle delay post entry
This should be coupled with the INDICATOR version linked above for the alerts and signals. Strategies won't paint the signal "L" or "S" until the entry actually happens, but indicators allow this, which is repainting on current candle, but this is an FYI if you want to get serious with Pinescript algorithmic botting
You can restrict the backtest by dates
It is advised that you understand what Heikin-Ashi candles do to strategies, the default settings for this backtest is NON Heikin-Ashi candles but you have the ability to change that in the source selection
This is a mathematically heavy, heavy-lifting strategy with multi-layered adaptivity. Make sure you do your own research so you understand what is happening here. This can be used as its own trading system without any other oscillators, moving average baselines, or volatility/momentum confirmation indicators.
What is the T3 moving average?
Better Moving Averages Tim Tillson
November 1, 1998
Tim Tillson is a software project manager at Hewlett-Packard, with degrees in Mathematics and Computer Science. He has privately traded options and equities for 15 years.
Introduction
"Digital filtering includes the process of smoothing, predicting, differentiating, integrating, separation of signals, and removal of noise from a signal. Thus many people who do such things are actually using digital filters without realizing that they are; being unacquainted with the theory, they neither understand what they have done nor the possibilities of what they might have done."
This quote from R. W. Hamming applies to the vast majority of indicators in technical analysis . Moving averages, be they simple, weighted, or exponential, are lowpass filters; low frequency components in the signal pass through with little attenuation, while high frequencies are severely reduced.
"Oscillator" type indicators (such as MACD , Momentum, Relative Strength Index ) are another type of digital filter called a differentiator.
Tushar Chande has observed that many popular oscillators are highly correlated, which is sensible because they are trying to measure the rate of change of the underlying time series, i.e., are trying to be the first and second derivatives we all learned about in Calculus.
We use moving averages (lowpass filters) in technical analysis to remove the random noise from a time series, to discern the underlying trend or to determine prices at which we will take action. A perfect moving average would have two attributes:
It would be smooth, not sensitive to random noise in the underlying time series. Another way of saying this is that its derivative would not spuriously alternate between positive and negative values.
It would not lag behind the time series it is computed from. Lag, of course, produces late buy or sell signals that kill profits.
The only way one can compute a perfect moving average is to have knowledge of the future, and if we had that, we would buy one lottery ticket a week rather than trade!
Having said this, we can still improve on the conventional simple, weighted, or exponential moving averages. Here's how:
Two Interesting Moving Averages
We will examine two benchmark moving averages based on Linear Regression analysis.
In both cases, a Linear Regression line of length n is fitted to price data.
I call the first moving average ILRS, which stands for Integral of Linear Regression Slope. One simply integrates the slope of a linear regression line as it is successively fitted in a moving window of length n across the data, with the constant of integration being a simple moving average of the first n points. Put another way, the derivative of ILRS is the linear regression slope. Note that ILRS is not the same as a SMA ( simple moving average ) of length n, which is actually the midpoint of the linear regression line as it moves across the data.
We can measure the lag of moving averages with respect to a linear trend by computing how they behave when the input is a line with unit slope. Both SMA (n) and ILRS(n) have lag of n/2, but ILRS is much smoother than SMA .
Our second benchmark moving average is well known, called EPMA or End Point Moving Average. It is the endpoint of the linear regression line of length n as it is fitted across the data. EPMA hugs the data more closely than a simple or exponential moving average of the same length. The price we pay for this is that it is much noisier (less smooth) than ILRS, and it also has the annoying property that it overshoots the data when linear trends are present.
However, EPMA has a lag of 0 with respect to linear input! This makes sense because a linear regression line will fit linear input perfectly, and the endpoint of the LR line will be on the input line.
These two moving averages frame the tradeoffs that we are facing. On one extreme we have ILRS, which is very smooth and has considerable phase lag. EPMA has 0 phase lag, but is too noisy and overshoots. We would like to construct a better moving average which is as smooth as ILRS, but runs closer to where EPMA lies, without the overshoot.
A easy way to attempt this is to split the difference, i.e. use (ILRS(n)+EPMA(n))/2. This will give us a moving average (call it IE /2) which runs in between the two, has phase lag of n/4 but still inherits considerable noise from EPMA. IE /2 is inspirational, however. Can we build something that is comparable, but smoother? Figure 1 shows ILRS, EPMA, and IE /2.
Filter Techniques
Any thoughtful student of filter theory (or resolute experimenter) will have noticed that you can improve the smoothness of a filter by running it through itself multiple times, at the cost of increasing phase lag.
There is a complementary technique (called twicing by J.W. Tukey) which can be used to improve phase lag. If L stands for the operation of running data through a low pass filter, then twicing can be described by:
L' = L(time series) + L(time series - L(time series))
That is, we add a moving average of the difference between the input and the moving average to the moving average. This is algebraically equivalent to:
2L-L(L)
This is the Double Exponential Moving Average or DEMA , popularized by Patrick Mulloy in TASAC (January/February 1994).
In our taxonomy, DEMA has some phase lag (although it exponentially approaches 0) and is somewhat noisy, comparable to IE /2 indicator.
We will use these two techniques to construct our better moving average, after we explore the first one a little more closely.
Fixing Overshoot
An n-day EMA has smoothing constant alpha=2/(n+1) and a lag of (n-1)/2.
Thus EMA (3) has lag 1, and EMA (11) has lag 5. Figure 2 shows that, if I am willing to incur 5 days of lag, I get a smoother moving average if I run EMA (3) through itself 5 times than if I just take EMA (11) once.
This suggests that if EPMA and DEMA have 0 or low lag, why not run fast versions (eg DEMA (3)) through themselves many times to achieve a smooth result? The problem is that multiple runs though these filters increase their tendency to overshoot the data, giving an unusable result. This is because the amplitude response of DEMA and EPMA is greater than 1 at certain frequencies, giving a gain of much greater than 1 at these frequencies when run though themselves multiple times. Figure 3 shows DEMA (7) and EPMA(7) run through themselves 3 times. DEMA^3 has serious overshoot, and EPMA^3 is terrible.
The solution to the overshoot problem is to recall what we are doing with twicing:
DEMA (n) = EMA (n) + EMA (time series - EMA (n))
The second term is adding, in effect, a smooth version of the derivative to the EMA to achieve DEMA . The derivative term determines how hot the moving average's response to linear trends will be. We need to simply turn down the volume to achieve our basic building block:
EMA (n) + EMA (time series - EMA (n))*.7;
This is algebraically the same as:
EMA (n)*1.7-EMA( EMA (n))*.7;
I have chosen .7 as my volume factor, but the general formula (which I call "Generalized Dema") is:
GD (n,v) = EMA (n)*(1+v)-EMA( EMA (n))*v,
Where v ranges between 0 and 1. When v=0, GD is just an EMA , and when v=1, GD is DEMA . In between, GD is a cooler DEMA . By using a value for v less than 1 (I like .7), we cure the multiple DEMA overshoot problem, at the cost of accepting some additional phase delay. Now we can run GD through itself multiple times to define a new, smoother moving average T3 that does not overshoot the data:
T3(n) = GD ( GD ( GD (n)))
In filter theory parlance, T3 is a six-pole non-linear Kalman filter. Kalman filters are ones which use the error (in this case (time series - EMA (n)) to correct themselves. In Technical Analysis , these are called Adaptive Moving Averages; they track the time series more aggressively when it is making large moves.
What is R-squared Adaptive?
One tool available in forecasting the trendiness of the breakout is the coefficient of determination ( R-squared ), a statistical measurement.
The R-squared indicates linear strength between the security's price (the Y - axis) and time (the X - axis). The R-squared is the percentage of squared error that the linear regression can eliminate if it were used as the predictor instead of the mean value. If the R-squared were 0.99, then the linear regression would eliminate 99% of the error for prediction versus predicting closing prices using a simple moving average .
R-squared is used here to derive a T3 factor used to modify price before passing price through a six-pole non-linear Kalman filter.
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading lev- els. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
Included:
Bar coloring
Signals
Alerts
Loxx's Expanded Source Types
Miyagi (10 in 1) + DCA StrategyMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
Miyagi (10 in 1) + DCA Strategy built for the Miyagi (10 in 1) + Alerts found here:
The DCA Strategy was designed to help visualize, backtest and improve users' DCA strategies and overall profitability.
Users can backtest different trading timeframes using the start and end date inputs.
Users can backtest different take profit and stoploss percents, both long and short.
Users can choose whether or not to use DCA on the backtester via a selectable input.
Input the DCA as you would normally using the Wick Hunter bot.
Happy trading!
Miyagi (4 in 1) + DCA StrategyMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
Miyagi (4 in 1) + DCA Strategy built for the Miyagi (4 in 1) + Alerts found here:
The DCA Strategy was designed to help visualize, backtest and improve users' DCA strategies and overall profitability.
Users can backtest different trading timeframes using the start and end date inputs.
Users can backtest different take profit and stoploss percents, both long and short.
Users can choose whether or not to use DCA on the backtester via a selectable input.
Input the DCA as you would normally using the Wick Hunter bot.
Happy trading!
Miyagi STrend StrategyMiyagi: The attempt at mastering something for the best results.
Miyagi indicators combine multiple trigger conditions and place them in one toolbox for traders to easily use, produce alerts, backtest, reduce risk and increase profitability.
Miyagi STrend was created to allow traders the ability to both scalp and swing trade from as singular indicator. STrend aims to help traders catch more of the move.
STrend Strategy built for the Miyagi STrend found here:
It would be best suited to utilize a stoploss when trading with Miyagi STrend to minimize risk.
Alerts are meant to fire on "Once per Bar Close" to confirm entry and exit signals.
Happy Trading!
Market First - Relative Strength/Weakness (the ZenBot strategy)This market-first trading strategy gives BUY, SHORT, and CLOSE signals based on volume, trend, and relative strength or weakness to the market (SPY by default, can be customized). This indicator is useful for signaling day-trade entries and exits for tickers that are strong (or weak) against the market.
Stocks that are showing relative strength (or weakness) to the market, are trending, and have decent movement generate a buy (or short) signal. When the trend runs out, a CLOSE signal is fired.
Potential profit (based on ATR) and actual profit is calculated, predicting the type of move expected
Unique 'stay in trade' logic helps prevent unnecessary CLOSE signals if a trend is likely to continue
A colored plot indicates the strength of the current trend and turns orange/red when the strength is weakened.
Crypto traders can uncheck 'Trade during market hours' for 24-hour trading, and should change the comparison ticker from SPY to BTCUSD or something similar for their market.
Enjoy!
KEY CONCEPTS
The three- and five-minute timeframes are used to establish and verify trend ( ADX /DI with custom logic)
Entries and exits are based on Parabolic SAR and confirmed on multiple timeframes, trend, and relative volume
Relative strength /weakness to the market compares ticker to SPY
Chop is avoided at all costs. I've experimented with choppiness indicator below 38, but found that the ADX DI+/- readings work even better.
Trend is established using ADX DI+/- readings over 20, confirmed by EMA 5/13 crossover and EMA5 slope
Signals will fire only if the average volume for the current 5-min bar is above normal
Only tickers with a five-bar / 13 period ATR of 1% the ticker's price generate signal.
Only longs above daily-anchored VWAP , shorts below daily-anchored VWAP
Signals fire on bar close to prevent repainting / look-ahead bias
Indicator labels and alerts generated
SIGNALS
BUY: up-trending tickers showing relative strength are bought on the three-minute PSAR
SELL: when the close price falls below the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
SHORT: down-trending tickers with relative weakness are shorted on the three-minute PSAR
COVER: when the close price moves above the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
ALERTS
Alerts are generated on BUY, SELL, SHORT, and COVER signals, as well as optional LOST RELATIVE STRENGTH and LOST RELATIVE WEAKNESS
INPUTS
Use relative strength /weakness comparison with the market : trigger trades based on the ticker's strength or weakness to the selected comparison ticker (usually SPY for equities or BTCUSD for crypto)
[* ]Comparison Ticker for relative strength /weakness : Ticker to compare against for relative strength /weakness
Trade during market hours only : Take buy/sells during specified hours. Disable this for crypto trading.
[* ]Market hours (market time) : Customize market hours - defaults to 9:30 to 16:00 EST
[* ]"Only trade very strong trends" : take trades only if an established trend is very strong ( ADX over 40 ) (DEFAULT = ON)
"Limit trade direction to VWAP" : Long trades only above VWAP , shorts below (DEFAULT = ON)
"Limit trade direction to Market direction" : Long trades only if SPY (or selected comparison ticker) is up, shorts if the market is down. (DEFAULT= ON)
"Limit trades based on a ticker's green/red status for the day" : Long trades if the ticker is green for the day, shorts if red. (DEFAULT = ON)
BEST Strategy Template AutoviewHello Traders
I've build a strategy template building for you the AUTOVIEW commands
I made this template based on this documentation: use.autoview.with.pink
You can select whether you want to use an SL or not, a TP or not, using the borrow/repay feature (only for Binance), ... and it will build dynamically the Autoview commands and will send them when entry/exit alerts trigger.
The template accept SL/TP in percentage or pips/USD distance from the entry price
MAGICAL !!!! (not really, just some dumb coding)
Users will have to specify from the settings:
- the Autoview account name
- the symbol name: I couldn't capture it from the chart because sometimes the symbol name on the broker side is different than the one from the TradingView side
- the position size
- the broker name (Tradovate, Binance, Bitmex, FTX, ...)
- if you want to send the alerts to your DEMO or LIVE account
- a debug mode to check if your alerts are well formatted
- and a few other interesting options...
If you want to use it, you'll have to update the dummy entries logic lines 97-98 and replacing those two lines by your own stuff
I'll make the ProfitView and 3Commas and Alertatron versions shortly.
Basically the same script but with the commands built for those 3 automation third-parties.
Best regards
Dave
Gap Reversion StrategyToday I am releasing to the community an original short-term, high-probability gap trading strategy, backed by a 20 year backtest. This strategy capitalizes on the mean reverting behavior of equity ETFs, which is largely driven by fear in the market. The strategy buys into that fear at a level that has historically mean reverted within ~5 days. Larry Connors has published useful research and variations of strategies based on this behavior that I would recommend any quantitative trader read.
What it does:
This strategy, for 1 day charts on equity ETFs, looks for an overnight gap down when the RSI is also in/near an oversold position. Then, it places a limit order further below the opening of the gapped-down day. It then exits the position based on a higher RSI level. The limit buy order is cancelled if the price doesn't reach your limit price that day. So, the larger you make the gap and limit %, the less signals you will have.
Features:
Inputs to allow the adjustment of the limit order %, the gap %, and the RSI entry/exit levels.
An option to have the limit order be based on a % of ATR instead of a % of asset price.
An optional filter that can turn-off trades when the VIX is unusually high.
A built in stop.
Built in alerts.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
[TH] Volatility BreakoutVolatility Breakout Strategy for TradingHook.
This strategy is not for backtesting but for forward-testing starting when added to chart.
It can make and send a formatted message string for buy and sell order using alert.
Pullback Strategy (Candle Analysis) New VersionFollowing on from the previous Pullback Candle which smashed over 100 likes - here we have the strategy behind the indicator.
Signal = Pullback Candle (This will alert on all timeframes and markets when selecting the Alert function for the Signal
Entry = When the Pullback Candle is confirmed ie 16:00 - the strategy will enter within the next two candles.
Stop loss = 0.25 ATR multiple which means we have a tighter stop loss - if greater than 1 then the stop loss will be more in percentages!!!
Take Profit = 1.5 Risk to Reward Model
Ema filter - There is a function to modify when looking into trades so as this is a bullish setup we want trades to be over the ema and using this filter will only show trades above the 200ema
Time filter - If you want to backtest Uptrends - locate the time of the start and the end of the uptrend - input this data into the settings and this will bring up the trades in that time period.
Most efficient to use this script is only in uptrends and this signal is a bullish signal - when using a ema filter we wont get trades under this so narrow down good trades for automation.
!!!!TO ENHANCE THE SYSTEM - USE TECHNICAL ANANLYSIS FOR CONFLUENCES
Most inefficient way to use the script is when price is in a downtrend and the win rate falls dramatically.
The pullback candle has a R-Expectancy of R5 so profits can be elongated when trading manually.
As the pullback candle occurs often in a trend we could pyramided trades to say have 5 trades in the same direction but the way i would trade this is to alert R1.5 then look to R2 and above to take profits manually.
(((P.S coders.... i need help to work on a profit extension code where exit long when price is below the 9ema (this seems simple but proving difficult) - this would be included onto the script if received.
TWAP + MA crossover Strategy [Dynamic Signal Lab]Dear TV'ers,
Hereby the strategy script for the TWAP/moving average crossover, with unique taking profit options. moving averages include: EMA , WMA , DEMA , TEMA , VAR, WWMA, ZLEMA , TSF , HULL and TILL.
Use the TWAP as the slow moving average and use another moving average as the faster/more responsive moving average. Finally, you can use a green fill to visualize how much you are in profit from your entry point.
Good strategies always involve gradual taking profit, which is also possible in this script.
You can gradually take profit (and set how much%), using the following criteria:
* minimum consecutive green/red candles
* minimum amount of green/red candles in the last 2-8 candles
* both of the above criteria.
The current default properties should be modified to make this strategy cost-effective, but typically 15minutes and higher timeframes (up to 6hr) seem to work well for larger (top10 cap) crypto projects. Don't use this script for small-caps as it will get you rekt.
Additionally, you'll also be able to continuously take profit, making sure you lock in all those sweet profits. Use this script for backtesting and the indicator compagnon to fire your alerts.
[Fedra Algotrading Strategy 2tp+L&S] Futures Long or ShortStrategy for crypto market, designed for automatic algorithmic trading with bots.
Can place long and short orders
Calculates your entries based on the breakout of the simple deviation of the linear regression of the last X periods.
Configures TP (green line) and SL (red line) percentages, the TP is a trailing TP.
Optionally, you can set a first TP (white line) that sells half of the position.
Advanced trend filter to not open trades against the market. SMA (yellow line), WMA (blue line) and secret sauce
Includes an advanced system to control the backtest period (choose how many days to backtest).
Risk management by volume of capital or amount of losing trades (kill switches that will exit the trade and stop the script)
The script includes default commissions of 0.2% per trade (configurable).
- Dinamic table with Price positions to plan your limit orders if you are trading manually
- Highly customizable and optimizable.
If you want to trade longs and shorts, it is advisable to create 2 different alerts. In most cases, the optimal parameters for longs are not the same as for shorts. In a forthcoming update I will enable separate configurations.
For better performance the script uses real time price information, for this reason Tradingview may warn you that there is "repainting", as the backtest information does not contain the information of each tick but only the open, close, high and low values of each candle.
To avoid this, you can disable the "calculate on every tick" option from the strategy settings panel.
Dynamic length MA Strategy [Dynamic Signal Lab]Dear TV'ers,
Hereby the strategy for the dynamic moving average crossover, with some flexible taking profit options.
All moving averages have the option to dynamically change lengths and different source options. They include:
* Hull MA
* volume-weighted Hull MA
* Simple MA
* Money Flow Index
* Chande Momentum Oscilator
* Arnaud Legoux MA
* Weighted MA
* Linear regression
What makes this strategy special is the fact that you can dynamically shorten the length of moving average length depending on how much you are in profit. The more you are in profit, the shorter the length of the MA will become.
The current default properties should be modified to make this strategy cost-effective, but typically 30minutes and higher timeframes seem to work well for larger (top10 cap) crypto projects. Dont use this script for small-caps as it will get you rekt.
Additionally, you'll also be able to continuously take profit, making sure you lock in all those sweet profits.
Use this script for doing backtesting and the indicator compagnon to fire your alerts.
