MACD Trend Trading with Dynamic Position Sizing // AlgoFyreThe MACD Trend Trading with Dynamic Position Sizing strategy combines MACD and trend indicators for trend trading. It uses MACD crossovers to identify entry points and a trend source for directional bias. The strategy incorporates risk management through dynamic position sizing based on a fixed risk amount. It allows for both long and short positions with customizable stop-loss and take-profit levels. The script includes visualization options for entry, stop-loss, and take-profit levels, enhancing trade analysis.
TABLE OF CONTENTS
🔶 ORIGINALITY
🔸Dynamic Position Sizing
🔸Trend-MACD Combination
🔸Customizable Risk Management
🔶 FUNCTIONALITY
🔸Indicators
🞘 Trend Indicator
🞘 Moving Average Convergence Divergence (MACD)
🔸Conditions
🞘 Long Entry
🞘 Short Entry
🔶 INSTRUCTIONS
🔸Step-by-Step Guidelines
🞘 Setting Up the Strategy
🞘 Alerts
🔸Customize settings
🔶 CONCLUSION
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🔶 ORIGINALITY The MACD Trend Trading with Dynamic Position Sizing strategy uniquely combines MACD indicators with trend analysis to optimize entry and exit points. Unlike static trading strategies, it employs dynamic position sizing based on a fixed risk amount, ensuring consistent risk management. This approach allows traders to adapt to varying market conditions by adjusting position sizes according to predefined risk parameters, enhancing both flexibility and control in trading decisions. The strategy's integration of customizable stop-loss and take-profit levels further refines its risk management capabilities, making it a robust tool for both trending and volatile markets.
🔸Dynamic Position Sizing This strategy calculates position sizes dynamically, based on a fixed risk amount, allowing traders to maintain consistent risk exposure across trades.
🔸Trend-MACD Combination By combining trend direction with MACD crossovers, the strategy enhances the accuracy of entry signals, aligning trades with prevailing market trends.
🔸Customizable Risk Management Traders can set flexible risk-reward ratios and adjust stop-loss and take-profit levels, tailoring the strategy to their risk tolerance and market conditions.
🔶 FUNCTIONALITY The MACD Trend Trading with Dynamic Position Sizing strategy leverages a combination of trend indicators and the MACD to identify optimal trading opportunities. This strategy is designed to capitalize on short-term price movements by dynamically adjusting position sizes based on predefined risk parameters. It allows traders to manage risk effectively while taking advantage of both long and short positions.
🔸Indicators 🞘 Trend Indicator: Utilizes the trend source to determine market direction, ensuring trades align with prevailing trends.
Recommendation: We recommend using the Adaptive MAs (Hurst, CVaR, Fractal) indicator with the following settings for trend detection. However, you can use any trend indicator that suits your trading style.
🞘 Moving Average Convergence Divergence (MACD): Employs MACD crossovers to generate entry signals, enhancing the accuracy of trade execution. Use the "Moving Average Convergence Divergence" Indicator with the following settings:
🔸Conditions 🞘 Long Entry: Initiates a long position when the price is above the trend source, and a MACD crossover occurs with both MACD and signal lines below zero.
🞘 Short Entry: Initiates a short position when the price is below the trend source, and a MACD crossunder occurs with both MACD and signal lines above zero.
🔶 INSTRUCTIONS
The MACD Trend Trading with Dynamic Position Sizing strategy can be set up by adding it to your TradingView chart and configuring parameters such as the MACD source, trend source, and risk management settings. This strategy is designed to capitalize on short-term price movements by dynamically adjusting position sizes based on predefined risk parameters. Enhance the accuracy of signals by combining this strategy with additional indicators like trend-following or momentum-based tools. Adjust settings to better manage risk and optimize entry and exit points.
🔸Step-by-Step Guidelines
🞘 Setting Up the Strategy
Adding the Strategy to the Chart:
Go to your TradingView chart.
Click on the "Indicators" button at the top.
Search for "MACD Trend Trading with Dynamic Position Sizing" in the indicators list.
Click on the strategy to add it to your chart.
Configuring the Strategy:
Open the strategy settings by clicking on the gear icon next to its name on the chart.
MACD: Select the MACD from the MACD Indicator.
MACD Signal: Select the MACD Signal from the MACD Indicator.
Trend Source: Choose the trend source to determine market direction. If you use the Adaptive MAs (Hurst, CVaR, Fractal) with our settings shown above, choose the MA1 Smoothing Line.
Stop Loss Percentage: Set the stop loss distance from the trend source as a percentage.
Risk/Reward Ratio: Define the desired risk/reward ratio for trades.
Backtesting and Practice:
Backtest the strategy on historical data to understand how it performs in various market environments.
Practice using the strategy on a demo account before implementing it in live trading.
Market Awareness:
Keep an eye on market news and events that might cause extreme price movements. The strategy reacts to price data and might not account for news-driven events that can cause large deviations.
🔶 CONCLUSION
The MACD Trend Trading with Dynamic Position Sizing strategy provides a robust framework for capitalizing on short-term market trends by combining the MACD indicator with dynamic position sizing. This strategy leverages MACD crossovers to identify entry points and utilizes a trend source for directional bias, ensuring trades align with prevailing market conditions. By incorporating dynamic position sizing based on a fixed risk amount, traders can effectively manage risk and adapt to varying market conditions. The strategy's customizable stop-loss and take-profit levels further enhance its risk management capabilities, making it a versatile tool for both trending and volatile markets. With its strategic blend of technical indicators and risk management, the MACD Trend Trading strategy offers traders a comprehensive approach to optimizing trade execution and maximizing potential returns.
Movingavarage
Intramarket Difference Index StrategyHi Traders !!
The IDI Strategy:
In layman’s terms this strategy compares two indicators across markets and exploits their differences.
note: it is best the two markets are correlated as then we know we are trading a short to long term deviation from both markets' general trend with the assumption both markets will trend again sometime in the future thereby exhausting our trading opportunity.
📍 Import Notes:
This Strategy calculates trade position size independently (i.e. risk per trade is controlled in the user inputs tab), this means that the ‘Order size’ input in the ‘Properties’ tab will have no effect on the strategy. Why ? because this allows us to define custom position size algorithms which we can use to improve our risk management and equity growth over time. Here we have the option to have fixed quantity or fixed percentage of equity ATR (Average True Range) based stops in addition to the turtle trading position size algorithm.
‘Pyramiding’ does not work for this strategy’, similar to the order size input togeling this input will have no effect on the strategy as the strategy explicitly defines the maximum order size to be 1.
This strategy is not perfect, and as of writing of this post I have not traded this algo.
Always take your time to backtests and debug the strategy.
🔷 The IDI Strategy:
By default this strategy pulls data from your current TV chart and then compares it to the base market, be default BINANCE:BTCUSD . The strategy pulls SMA and RSI data from either market (we call this the difference data), standardizes the data (solving the different unit problem across markets) such that it is comparable and then differentiates the data, calling the result of this transformation and difference the Intramarket Difference (ID). The formula for the the ID is
ID = market1_diff_data - market2_diff_data (1)
Where
market(i)_diff_data = diff_data / ATR(j)_market(i)^0.5,
where i = {1, 2} and j = the natural numbers excluding 0
Formula (1) interpretation is the following
When ID > 0: this means the current market outperforms the base market
When ID = 0: Markets are at long run equilibrium
When ID < 0: this means the current market underperforms the base market
To form the strategy we define one of two strategy type’s which are Trend and Mean Revesion respectively.
🔸 Trend Case:
Given the ‘‘Strategy Type’’ is equal to TREND we define a threshold for which if the ID crosses over we go long and if the ID crosses under the negative of the threshold we go short.
The motivating idea is that the ID is an indicator of the two symbols being out of sync, and given we know volatility clustering, momentum and mean reversion of anomalies to be a stylised fact of financial data we can construct a trading premise. Let's first talk more about this premise.
For some markets (cryptocurrency markets - synthetic symbols in TV) the stylised fact of momentum is true, this means that higher momentum is followed by higher momentum, and given we know momentum to be a vector quantity (with magnitude and direction) this momentum can be both positive and negative i.e. when the ID crosses above some threshold we make an assumption it will continue in that direction for some time before executing back to its long run equilibrium of 0 which is a reasonable assumption to make if the market are correlated. For example for the BTCUSD - ETHUSD pair, if the ID > +threshold (inputs for MA and RSI based ID thresholds are found under the ‘‘INTRAMARKET DIFFERENCE INDEX’’ group’), ETHUSD outperforms BTCUSD, we assume the momentum to continue so we go long ETHUSD.
In the standard case we would exit the market when the IDI returns to its long run equilibrium of 0 (for the positive case the ID may return to 0 because ETH’s difference data may have decreased or BTC’s difference data may have increased). However in this strategy we will not define this as our exit condition, why ?
This is because we want to ‘‘let our winners run’’, to achieve this we define a trailing Donchian Channel stop loss (along with a fixed ATR based stop as our volatility proxy). If we were too use the 0 exit the strategy may print a buy signal (ID > +threshold in the simple case, market regimes may be used), return to 0 and then print another buy signal, and this process can loop may times, this high trade frequency means we fail capture the entire market move lowering our profit, furthermore on lower time frames this high trade frequencies mean we pay more transaction costs (due to price slippage, commission and big-ask spread) which means less profit.
By capturing the sum of many momentum moves we are essentially following the trend hence the trend following strategy type.
Here we also print the IDI (with default strategy settings with the MA difference type), we can see that by letting our winners run we may catch many valid momentum moves, that results in a larger final pnl that if we would otherwise exit based on the equilibrium condition(Valid trades are denoted by solid green and red arrows respectively and all other valid trades which occur within the original signal are light green and red small arrows).
another example...
Note: if you would like to plot the IDI separately copy and paste the following code in a new Pine Script indicator template.
indicator("IDI")
// INTRAMARKET INDEX
var string g_idi = "intramarket diffirence index"
ui_index_1 = input.symbol("BINANCE:BTCUSD", title = "Base market", group = g_idi)
// ui_index_2 = input.symbol("BINANCE:ETHUSD", title = "Quote Market", group = g_idi)
type = input.string("MA", title = "Differrencing Series", options = , group = g_idi)
ui_ma_lkb = input.int(24, title = "lookback of ma and volatility scaling constant", group = g_idi)
ui_rsi_lkb = input.int(14, title = "Lookback of RSI", group = g_idi)
ui_atr_lkb = input.int(300, title = "ATR lookback - Normalising value", group = g_idi)
ui_ma_threshold = input.float(5, title = "Threshold of Upward/Downward Trend (MA)", group = g_idi)
ui_rsi_threshold = input.float(20, title = "Threshold of Upward/Downward Trend (RSI)", group = g_idi)
//>>+----------------------------------------------------------------+}
// CUSTOM FUNCTIONS |
//<<+----------------------------------------------------------------+{
// construct UDT (User defined type) containing the IDI (Intramarket Difference Index) source values
// UDT will hold many variables / functions grouped under the UDT
type functions
float Close // close price
float ma // ma of symbol
float rsi // rsi of the asset
float atr // atr of the asset
// the security data
getUDTdata(symbol, malookback, rsilookback, atrlookback) =>
indexHighTF = barstate.isrealtime ? 1 : 0
= request.security(symbol, timeframe = timeframe.period,
expression = [close , // Instentiate UDT variables
ta.sma(close, malookback) ,
ta.rsi(close, rsilookback) ,
ta.atr(atrlookback) ])
data = functions.new(close_, ma_, rsi_, atr_)
data
// Intramerket Difference Index
idi(type, symbol1, malookback, rsilookback, atrlookback, mathreshold, rsithreshold) =>
threshold = float(na)
index1 = getUDTdata(symbol1, malookback, rsilookback, atrlookback)
index2 = getUDTdata(syminfo.tickerid, malookback, rsilookback, atrlookback)
// declare difference variables for both base and quote symbols, conditional on which difference type is selected
var diffindex1 = 0.0, var diffindex2 = 0.0,
// declare Intramarket Difference Index based on series type, note
// if > 0, index 2 outpreforms index 1, buy index 2 (momentum based) until equalibrium
// if < 0, index 2 underpreforms index 1, sell index 1 (momentum based) until equalibrium
// for idi to be valid both series must be stationary and normalised so both series hae he same scale
intramarket_difference = 0.0
if type == "MA"
threshold := mathreshold
diffindex1 := (index1.Close - index1.ma) / math.pow(index1.atr*malookback, 0.5)
diffindex2 := (index2.Close - index2.ma) / math.pow(index2.atr*malookback, 0.5)
intramarket_difference := diffindex2 - diffindex1
else if type == "RSI"
threshold := rsilookback
diffindex1 := index1.rsi
diffindex2 := index2.rsi
intramarket_difference := diffindex2 - diffindex1
//>>+----------------------------------------------------------------+}
// STRATEGY FUNCTIONS CALLS |
//<<+----------------------------------------------------------------+{
// plot the intramarket difference
= idi(type,
ui_index_1,
ui_ma_lkb,
ui_rsi_lkb,
ui_atr_lkb,
ui_ma_threshold,
ui_rsi_threshold)
//>>+----------------------------------------------------------------+}
plot(intramarket_difference, color = color.orange)
hline(type == "MA" ? ui_ma_threshold : ui_rsi_threshold, color = color.green)
hline(type == "MA" ? -ui_ma_threshold : -ui_rsi_threshold, color = color.red)
hline(0)
Note it is possible that after printing a buy the strategy then prints many sell signals before returning to a buy, which again has the same implication (less profit. Potentially because we exit early only for price to continue upwards hence missing the larger "trend"). The image below showcases this cenario and again, by allowing our winner to run we may capture more profit (theoretically).
This should be clear...
🔸 Mean Reversion Case:
We stated prior that mean reversion of anomalies is an standerdies fact of financial data, how can we exploit this ?
We exploit this by normalizing the ID by applying the Ehlers fisher transformation. The transformed data is then assumed to be approximately normally distributed. To form the strategy we employ the same logic as for the z score, if the FT normalized ID > 2.5 (< -2.5) we buy (short). Our exit conditions remain unchanged (fixed ATR stop and trailing Donchian Trailing stop)
🔷 Position Sizing:
If ‘‘Fixed Risk From Initial Balance’’ is toggled true this means we risk a fixed percentage of our initial balance, if false we risk a fixed percentage of our equity (current balance).
Note we also employ a volatility adjusted position sizing formula, the turtle training method which is defined as follows.
Turtle position size = (1/ r * ATR * DV) * C
Where,
r = risk factor coefficient (default is 20)
ATR(j) = risk proxy, over j times steps
DV = Dollar Volatility, where DV = (1/Asset Price) * Capital at Risk
🔷 Risk Management:
Correct money management means we can limit risk and increase reward (theoretically). Here we employ
Max loss and gain per day
Max loss per trade
Max number of consecutive losing trades until trade skip
To read more see the tooltips (info circle).
🔷 Take Profit:
By defualt the script uses a Donchain Channel as a trailing stop and take profit, In addition to this the script defines a fixed ATR stop losses (by defualt, this covers cases where the DC range may be to wide making a fixed ATR stop usefull), ATR take profits however are defined but optional.
ATR SL and TP defined for all trades
🔷 Hurst Regime (Regime Filter):
The Hurst Exponent (H) aims to segment the market into three different states, Trending (H > 0.5), Random Geometric Brownian Motion (H = 0.5) and Mean Reverting / Contrarian (H < 0.5). In my interpretation this can be used as a trend filter that eliminates market noise.
We utilize the trending and mean reverting based states, as extra conditions required for valid trades for both strategy types respectively, in the process increasing our trade entry quality.
🔷 Example model Architecture:
Here is an example of one configuration of this strategy, combining all aspects discussed in this post.
Future Updates
- Automation integration (next update)
Versatile Moving Average StrategyVersatile Moving Average Strategy (VMAS)
Overview:
The Versatile Moving Average Strategy (VMAS) is designed to provide traders with a flexible approach to trend-following, utilizing multiple types of moving averages. This strategy allows for customization in choosing the moving average type and length, catering to various market conditions and trading styles.
Key Features:
- Multiple Moving Average Types: Choose from SMA, EMA, SMMA (RMA), WMA, VWMA, HULL, LSMA, and ALMA to best suit your trading needs.
- Customizable Inputs: Adjust the moving average length, source of price data, and stop-loss source to fine-tune the strategy.
- Target Percent: Set the percentage difference between successive profit targets to manage your risk and rewards effectively.
- Position Management: Enable or disable long and short positions, allowing for versatility in different market conditions.
- Commission and Slippage: The strategy includes realistic commission settings to ensure accurate backtesting results.
Strategy Logic:
1. Moving Average Calculation: The selected moving average is calculated based on user-defined parameters.
2. Entry Conditions:
- A long position is entered when the entry source crosses over the moving average, if long positions are enabled.
- A short position is entered when the entry source crosses under the moving average, if short positions are enabled.
3. Stop-Loss: Positions are closed if the stop-loss source crosses the moving average in the opposite direction.
4. Profit Targets: Multiple profit targets are defined, with each target set at an incremental percentage above (for long positions) or below (for short positions) the entry price.
Default Properties:
- Account Size: $10000
- Commission: 0.01% per trade
- Risk Management: Positions are sized to risk 80% of the equity per trade, because we get very tight stoploss when position is open.
- Sample Size: Backtesting has been conducted to ensure a sufficient sample size of trades, ideally more than 100 trades.
How to Use:
1. Configure Inputs: Set your preferred moving average type, length, and other input parameters.
2. Enable Positions: Choose whether to enable long, short, or both types of positions.
3. Backtest and Analyze: Run backtests with realistic settings and analyze the results to ensure the strategy aligns with your trading goals.
4. Deploy and Monitor: Once satisfied with the backtesting results, deploy the strategy in a live environment and monitor its performance.
This strategy is suitable for traders looking to leverage moving averages in a versatile and customizable manner. Adjust the parameters to match your trading style and market conditions for optimal results.
Note: Ensure the strategy settings used for publication are the same as those described here. Always conduct thorough backtesting before deploying any strategy in a live trading environment.
Entry Fragger - Strategy
For basic instructions please visit my other script "Entry Fragger".
The Signal Logic is explained there.
v1.4:
- Added advanced backtesting with fully customizable entries.
- Fully automated Buy Signals (profitable).
- Adjustable timeframes for signal logic. (requested)
Every setting affects the accuracy and profitability greatly now, based on settings applied.
The strategy performs best on high timeframes with larger capital and no leverage.
Useless for Forex, but absolutely smashes stocks and crypto on mid to high timeframes.
Please read through my other scripts description.
Set values as preferred and try your assets.
It does NOT work on low timeframes and forex!
Hint: BTC 4H, Custom Timeframe 1h, Moon Mode and Show Sell Signals enabled, R2R: 2.
Alligator + MA Trend Catcher [TradeDots]The "Alligator + MA Trend Catcher" is a trading strategy that integrates the William Alligator indicator with a Moving Average (MA) to establish robust entry and exit conditions, optimized for capturing trends.
HOW IT WORKS
This strategy combines the traditional William Alligator set up with an additional Moving Average indicator for enhanced trend confirmation, creating a user-friendly backtesting tool for traders who prefer the Alligator method.
The original Alligator strategy can frequently present fluctuations, even in well-established trends, leading to potentially premature exits. To mitigate this, we incorporate a Moving Average as a secondary confirmation measure to ensure the market trend has indeed shifted.
Here’s the operational flow for long orders:
Entry Signal: When the price rises above the Moving Average, it confirms a bullish market state. Enter if Alligator spread in an upward direction. The trade remains active even if the Alligator indicator suggests a trend reversal.
Exit Signal: The position is closed when the price falls below the Moving Average, and the Alligator spreads in the downward direction. This setup helps traders to maintain positions through the entirety of the trend for maximum gain.
APPLICATION
This strategy is tailored for assets with significant, well-defined trends, such as Bitcoin and Ethereum, which are known for their high volatility and substantial price movements.
This strategy offers a low win-rate but high reward configuration, making asset selection critical for long-term profitability. If you choose assets that lack strong price momentum, there's a high chance that this strategy may not be effective.
For traders seeking to maximize gains from large trends without exiting prematurely, this strategy provides an aggressive yet controlled approach to riding out substantial market waves.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Ahsan Tufail Precise MA Crossover Filter for Reliable SignalsIntroduction:
In the ever-evolving world of Forex trading, strategies that provide a competitive edge are highly sought after. The Moving Average (MA) crossover technique is a popular long-term approach, but its vulnerability to false signals can lead to potential losses. To overcome this challenge, we introduce a game-changing MA crossover filter designed to weed out false signals and unlock the full potential of this strategy. In this article, we delve into the mechanics of this filter, providing a comprehensive analysis of its components and how it enhances the accuracy of buy and sell signals.
The Power of the MA Crossover Filter:
The essence of our MA crossover filter lies in the integration of a specialized indicator that operates on a scale of 0 to 100. This ingenious indicator dynamically measures the distance between the middle Bollinger band and either the upper or lower Bollinger band. By analyzing the values of the last 504 candlesticks, it maps the range from 50 to 100 for the largest and smallest distances between the middle and upper Bollinger bands. Similarly, for values ranging from 0 to 50, it measures the distance between the middle and lower Bollinger bands.
Unveiling the Signal Execution Process:
The brilliance of this filter is revealed in its meticulous execution of buy and sell signals, which significantly reduces false crossovers. Let's explore the process step-by-step:
Buy Signal Precision:
To initiate a buy signal, the price must be positioned above the 200-period Simple Moving Average (SMA).
The filter validates the crossover by checking the indicator's value, ensuring it falls below the threshold of 25.
Sell Signal Accuracy:
For a sell signal, the price must be below the 200-period Simple Moving Average (SMA).
The filter confirms the crossover by verifying the indicator's value, which should exceed the threshold of 75.
This selective approach ensures that only high-confidence crossovers are considered, maximizing the potential for profitable trades.
Fine-Tuning the Filter for Optimal Performance:
While the MA crossover filter exhibits its prowess in GBPUSD and EURUSD currency pairs, it may require adjustments for other pairs. Currency pairs possess unique characteristics, and adapting the filter to specific behavior is crucial for its success.
To fine-tune the filter for alternative currency pairs, traders should conduct rigorous backtesting and analyze historical price data. By experimenting with indicator threshold values, traders can calibrate the filter to accurately match the dynamics of the target currency pair. This iterative process allows for customization, ultimately resulting in a finely-tuned filter that aligns with the unique behavior of the selected market.
Conclusion:
The MA crossover filter represents a paradigm shift in long-term Forex trading strategies. By intelligently filtering false signals, this precision tool unleashes the true potential of the MA crossover technique, elevating its profitability and enhancing overall trading performance. While no strategy guarantees absolute success, incorporating this filter empowers traders with a heightened level of confidence in their buy and sell signals. Embracing the power of this innovative filter can be a transformative step towards mastering Forex profits and staying ahead in the dynamic world of currency trading.
twisted SMA strategy [4h] Hello
I would like to introduce a very simple strategy that uses a combination of 3 simple moving averages ( SMA 4 , SMA 9 , SMA 18 )
this is a classic combination showing the most probable trend directions
Crosses were marked on the basis of the color of the candles (bulish cross - blue / bearish cross - maroon)
ma 100 was used to determine the main trend, which is one of the most popular 4-hour candles
We define main trend while price crosses SMA100 ( for bullish trend I use green candle color )
The long position strategy was created in combination of 3 moving averages with Kaufman's adaptive moving average by alexgrover
The strategy is very accurate and is easy to use indicators
the strategy uses only Buy (Long) signals in a combination of crossovers of the SMA 4, SMA 9, SMA 18 and the Kaufman Adaptive Moving Average.
As a signal to close a long position, only the opposite signal of the intersection of 3 different moving averages is used
the current strategy is recommended for higher time zones (4h +) due to the strength of the closing candles, which translates into signal strength
works fascinatingly well for long-term bullish market assets (for example 4h Apple, Tesla charts)
Enjoy and trade safe ;)
3c Ultimate reversal strategy With scanner and backtester v2This might just be the ultimate strategy to identify reversals.
This strategy includes a scanner, a backtester and ability to connect it with you 3 commas bot(See adviced settings below)
Strategy:
-Signals reversal that happened in the last bar. This signal DO NOT repaint.
-Identifies potential reversal that might happen in the current bar but can also not happen depending upon the timeframe closing price.
-The strategy combines the Moving Average Trend Changer, SuperTrend (ATR price detection) and ADX.
-It reduces the number of false signals in sideways market conditons and give more reliable trade signals.
-The signal does not repaint and can be used in any market condition. It determines the trend with high precision.
Take profit:
-Set 2 separate TP conditions.
-You can take profit using percentage, ATR, or RR(Risk Reward), aswell as using Trailing Take Profit.
- Use sell signal from the strategy(I often find way better results using that)
Stoploss:
-You can use either ATR, Percentage or sell signal from the strategy
(For now to let the strategy itself decide when to TP or SL, just set these parameters really high.)
Scanner:
-Identifies coins that are currently in the sell zone
-Identifies coins that are currently in the buy zone
-Screener explores up to 20 pairs in current graph's time frame.
-Optimize the strategy to your liking and use the built in backtester to see if it is a viable strategy.
3commas settings:
-For now you can only use simple bots.
-Create LONG and SHORT bots for the coins you like to trade and set up alerts(You can send long and short signal from the same alert)
-Set TP to 50% the strategy will handle buys and exits based on your inputs.
-Set safety orders to 0. I might add DCA to the strategy if testing proves that to be a good solution.
-When you have made the bots input the bot ID and token adress in the settings of the strategy.
-When creating the alert use this webhook :https://3commas.io/trade_signal/trading_view
-In the message field you use {{strategy.order.alert_message}} as the placeholder.
In the future this signal might make it to the 3commas marketplace. You can then subscribe to that signal where I have cherrypicked coins based on thorough backtesting and optimization.
Session Breakout Strategy [Kintsugi Trading]What is Session Breakout Strategy?
This premium indicator was inspired by my desire to find and place high probability forex trades in any market, direction, or time of day.
Why Forex?
The Forex markets operate 24 hours, 5.5 days a week
Access to meaningful leverage
Ability to easily trade long or short
High liquidity
How to use it!
----- First, start by choosing a Range Session and Trade Session. -----
- Range Session = Period of time you believe will be predictive of future price action. I like to overlay 'FX Market Sessions' by boitoki to target timeframes in which forex market sessions overlap. This period is indicative of buyers and sellers battling it out in an effort to choose a direction.
- Trade Session = Period of time you believe the price will act on the information gathered from the range session.
----- Next, we filter the Range Session by information that will help further predict price movement in the Trade Session. -----
Our tools are:
Moving Average Filter
Average RSI Filter
RSI Decision Level
None
Each parameter is customizable.
----- Next, we set our risk. -----
Enter the "stop pip size' you desire.
Enter the 'target profit pip size' you desire.
We can set out ($) dollar risk-per-trade in the inputs. The script does not know your actual account balance, so if you want to risk $100/trade you would enter '$10,000' in the account balance section and '1' in the Risk Per Trade% section.
For example, an '8' stop and an '8' tp would indicate a 1:1 risk-reward. *Note - although you may generate a higher win percent with a less favorable RR, it is rarely worth it.
----- Finally, we backtest our ideas. -----
After using the 'Strategy Tester' tab to thoroughly backtest your predictions you are ready to take it to the next level - Automated Trading!
This was my whole reason for creating the script. If you work a full-time job, live in a time zone that is hard to trade, or just don't have the patience, this will be a game-changer for you as it was for me.
Auto-Trading
When it comes to auto-trading this strategy I have included two options in the script that utilize the alert messages generated by TradingView.
*Note: Please trade on a demo account until you feel comfortable enough to use real money, and then please stick to 1%-2% of your total account value in risk per trade.*
AutoView
PineConnector
Good luck with your trading!
