[MT] Strategy Backtest Template| Initial Release | | EN |
An update of my old script, this script is designed so that it can be used as a template for all those traders who want to save time when programming their strategy and backtesting it, having functions already programmed that in normal development would take you more time to program, with this template you can simply add your favorite indicator and thus be able to take advantage of all the functions that this template has.
🔴Stop Loss and 🟢Take Profit:
No need to mention that it is a Stop Loss and a Take Profit, within these functions we find the options of: fixed percentage (%), fixed price ($), ATR, especially for Stop Loss we find the Pivot Points, in addition to this, the price range between the entry and the Stop Loss can be converted into a trailing stop loss, instead, especially for the Take Profit we have an option to choose a 1:X ratio that complements very well with the Pivot Points.
📈Heikin Ashi Based Entries:
Heikin Ashi entries are trades that are calculated based on Heikin Ashi candles but their price is executed to Japanese candles, thus avoiding false results that occur in Heikin candlestick charts, this making in certain cases better results in strategies that are executed with this option compared to Japanese candlesticks.
📊Dashboard:
A more visual and organized way to see the results and necessary data produced by our strategy, among them we can see the dates between which our operations are made regardless if you have activated some time filter, usual data such as Profit, Win Rate, Profit factor are also displayed in this panel, additionally data such as the total number of operations, how many were gains and how many losses, the average profit and loss for each operation and finally the maximum profits and losses followed, which are data that will be very useful to us when we elaborate our strategies.
Feel free to use this template to program your own strategies, if you find errors or want to request a new feature let me know in the comments or through my social networks found in my tradingview profile.
| Update 1.1 | | EN |
➕Additions: '
Time sessions filter and days of the week filter added to the time filter section.
Option to add leverage to the strategy.
5 Moving Averages, RSI, Stochastic RSI, ADX, and Parabolic Sar have been added as indicators for the strategy.
You can choose from the 6 available indicators the way to trade, entry alert or entry filter.
Added the option of ATR for Take Profit.
Ticker information and timeframe are now displayed on the dashboard.
Added display customization and color customization of indicator plots.
Added customization of display and color plots of trades displayed on chart.
📝Changes:
Now when activating the time filter it is optional to add a start or end date and time, being able to only add a start date or only an end date.
Operation plots have been changed from plot() to line creation with line.new().
Indicator plots can now be controlled from the "plots" section.
Acceptable and deniable range of profit, winrate and profit factor can now be chosen from the "plots" section to be displayed on the dashboard.
Aesthetic changes in the section separations within the settings section and within the code itself.
The function that made the indicators give inputs based on heikin ashi candles has been changed, see the code for more information.
⚙️Fixes:
Dashboard label now projects correctly on all timeframes including custom timeframes.
Removed unnecessary lines and variables to take up less code space.
All code in general has been optimized to avoid the use of variables, unnecessary lines and avoid unnecessary calculations, freeing up space to declare more variables and be able to use fewer lines of code.
| Lanzamiento Inicial | | ES |
Una actualización de mi antiguo script, este script está diseñado para que pueda ser usado como una plantilla para todos aquellos traders que quieran ahorrar tiempo al programar su estrategia y hacer un backtesting de ella, teniendo funciones ya programadas que en el desarrollo normal te tomaría más tiempo programar, con esta plantilla puedes simplemente agregar tu indicador favorito y así poder aprovechar todas las funciones que tiene esta plantilla.
🔴Stop Loss y 🟢Take Profit:
No hace falta mencionar que es un Stop Loss y un Take Profit, dentro de estas funciones encontramos las opciones de: porcentaje fijo (%), precio fijo ($), ATR, en especial para Stop Loss encontramos los Pivot Points, adicionalmente a esto, el rango de precio entre la entrada y el Stop Loss se puede convertir en un trailing stop loss, en cambio, especialmente para el Take Profit tenemos una opción para elegir un ratio 1:X que se complementa muy bien con los Pivot Points.
📈Entradas Basadas en Heikin Ashi:
Las entradas Heikin Ashi son operaciones que son calculados en base a las velas Heikin Ashi pero su precio esta ejecutado a velas japonesas, evitando así́ los falsos resultados que se producen en graficas de velas Heikin, esto haciendo que en ciertos casos se obtengan mejores resultados en las estrategias que son ejecutadas con esta opción en comparación con las velas japonesas.
📊Panel de Control:
Una manera más visual y organizada de ver los resultados y datos necesarios producidos por nuestra estrategia, entre ellos podemos ver las fechas entre las que se hacen nuestras operaciones independientemente si se tiene activado algún filtro de tiempo, datos usuales como el Profit, Win Rate, Profit factor también son mostrados en este panel, adicionalmente se agregaron datos como el número total de operaciones, cuantos fueron ganancias y cuantos perdidas, el promedio de ganancias y pérdidas por cada operación y por ultimo las máximas ganancias y pérdidas seguidas, que son datos que nos serán muy útiles al elaborar nuestras estrategias.
Siéntete libre de usar esta plantilla para programar tus propias estrategias, si encuentras errores o quieres solicitar una nueva función házmelo saber en los comentarios o a través de mis redes sociales que se encuentran en mi perfil de tradingview.
| Actualización 1.1 | | ES |
➕Añadidos:
Filtro de sesiones de tiempo y filtro de días de la semana agregados al apartado de filtro de tiempo.
Opción para agregar apalancamiento a la estrategia.
5 Moving Averages, RSI, Stochastic RSI, ADX, y Parabolic Sar se han agregado como indicadores para la estrategia.
Puedes escoger entre los 6 indicadores disponibles la forma de operar, alerta de entrada o filtro de entrada.
Añadido la opción de ATR para Take Profit.
La información del ticker y la temporalidad ahora se muestran en el dashboard.
Añadido personalización de visualización y color de los plots de indicadores.
Añadido personalización de visualización y color de los plots de operaciones mostradas en grafica.
📝Cambios:
Ahora al activar el filtro de tiempo es opcional añadir una fecha y hora de inicio o fin, pudiendo únicamente agregar una fecha de inicio o solamente una fecha de fin.
Los plots de operaciones han cambiados de plot() a creación de líneas con line.new().
Los plots de indicadores ahora se pueden controlar desde el apartado "plots".
Ahora se puede elegir el rango aceptable y negable de profit, winrate y profit factor desde el apartado "plots" para mostrarse en el dashboard.
Cambios estéticos en las separaciones de secciones dentro del apartado de configuraciones y dentro del propio código.
Se ha cambiado la función que hacía que los indicadores dieran entradas en base a velas heikin ashi, mire el código para más información.
⚙️Arreglos:
El dashboard label ahora se proyecta correctamente en todas las temporalidades incluyendo las temporalidades personalizadas.
Se han eliminado líneas y variables innecesarias para ocupar menos espacio en el código.
Se ha optimizado todo el código en general para evitar el uso de variables, líneas innecesarias y evitar los cálculos innecesarios, liberando espacio para declarar más variables y poder utilizar menos líneas de código.
Backtest
Catching the Bottom (by Coinrule)This script utilises the RSI and EMA indicators to enter and close the trade.
The relative strength index (RSI) is a momentum indicator used in technical analysis. RSI measures the speed and magnitude of a security's recent price changes to evaluate overvalued or undervalued conditions in the price of that security. The RSI is displayed as an oscillator (a line graph) on a scale of zero to 100. The RSI can do more than point to overbought and oversold securities. It can also indicate securities that may be primed for a trend reversal or corrective pullback in price. It can signal when to buy and sell. Traditionally, an RSI reading of 70 or above indicates an overbought situation. A reading of 30 or below indicates an oversold condition.
An exponential moving average (EMA) is a type of moving average (MA) that places a greater weight and significance on the most recent data points. The exponential moving average is also referred to as the exponentially weighted moving average. An exponentially weighted moving average reacts more significantly to recent price changes than a simple moving average simple moving average (SMA), which applies an equal weight to all observations in the period.
The strategy enters and exits the trade based on the following conditions.
ENTRY
RSI has a decrease of 3.
RSI <40.
EMA100 has crossed above the EMA50.
EXIT
RSI is greater than 65.
EMA9 has crossed above EMA50.
This strategy is back tested from 1 April 2022 to simulate how the strategy would work in a bear market and provides good returns.
Pairs that produce very strong results include ETH on the 5m timeframe, BNB on 5m timeframe, XRP on the 45m timeframe, MATIC on the 30m timeframe and MATIC on the 2H timeframe.
The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance.
[fpemehd] Strategy TemplateHello Guys! Nice to meet you all!
This is my fourth script!
This is the Strategy Template for traders who wants to make their own strategy.
I made this based on the open source strategies by jason5480, kevinmck100, myncrypto. Thank you All!
### StopLoss
1. Can Choose Stop Loss Type: Percent, ATR, Previous Low / High.
2. Can Chosse inputs of each Stop Loss Type.
### Take Profit
1. Can set Risk Reward Ratio for Take Profit.
- To simplify backtest, I erased all other options except RR Ratio.
- You can add Take Profit Logic by adding options in the code.
2. Can set Take Profit Quantity.
### Risk Manangement
1. Can choose whether to use Risk Manangement Logic.
- This controls the Quantity of the Entry.
- e.g. If you want to take 3% risk per trade and stop loss price is 6% below the long entry price,
then 50% of your equity will be used for trade.
2. Can choose How much risk you would take per trade.
### Plot
1. Added Labels to check the data of entry / exit positions.
2. Changed and Added color different from the original one. (green: #02732A, red: #D92332, yellow: #F2E313)
Coral Trend Pullback Strategy (TradeIQ)Description:
Strategy is taken from the TradeIQ YouTube video called "I Finally Found 80% Win Rate Trading Strategy For Crypto".
Check out the full video for further details/clarification on strategy entry/exit conditions.
The default settings are exactly as TradeIQ described in his video.
However I found some better results by some tweaking settings, increasing R:R ratio and by turning off confirmation indicators.
This would suggest that perhaps the current confirmation indicators are not the best options. I'm happy to try add some other optional confirmation indicators if they look to be more effective.
Recommended timeframe: 1H
Strategy incorporates the following features:
Risk management:
Configurable X% loss per stop loss
Configurable R:R ratio
Trade entry:
Based on strategy conditions below
Trade exit:
Based on strategy conditions below
Backtesting:
Configurable backtesting range by date
Trade drawings:
Each entry condition indicator can be turned on and off
TP/SL boxes drawn for all trades. Can be turned on and off
Trade exit information labels. Can be turned on and off
NOTE: Trade drawings will only be applicable when using overlay strategies
Alerting:
Alerts on LONG and SHORT trade entries
Debugging:
Includes section with useful debugging techniques
Strategy conditions
Trade entry:
LONG
C1: Coral Trend is bullish
C2: At least 1 candle where low is above Coral Trend since last cross above Coral Trend
C3: Pullback happens and price closes below Coral Trend
C4: Coral Trend colour remains bullish for duration of pullback
C5: After valid pullback, price then closes above Coral Trend
C6: Optional confirmation indicators (choose either C6.1 or C6.2 or NONE):
C6.1: ADX and DI (Single indicator)
C6.1.1: Green line is above red line
C6.1.2: Blue line > 20
C6.1.3: Blue trending up over last 1 candle
C6.2: Absolute Strengeh Histogram + HawkEye Volume Indicator (Two indicators combined)
C6.2.1: Absolute Strengeh Histogram colour is blue
C6.2.2: HawkEye Volume Indicator colour is green
SHORT
C1: Coral Trend is bearish
C2: At least 1 candle where high is below Coral Trend since last cross below Coral Trend
C3: Pullback happens and price closes above Coral Trend
C4: Coral Trend colour remains bearish for duration of pullback
C5: After valid pullback, price then closes below Coral Trend
C6: Optional confirmation indicators (choose either C6.1 or C6.2 or NONE):
C6.1: ADX and DI (Single indicator)
C6.1.1: Red line is above green line
C6.1.2: Blue line > 20
C6.1.3: Blue trending up over last 1 candle
C6.2: Absolute Strengeh Histogram + HawkEye Volume Indicator (Two indicators combined)
C6.2.1: Absolute Strengeh Histogram colour is red
C6.2.2: HawkEye Volume Indicator colour is red
NOTE: All the optional confirmation indicators cannot be overlayed with Coral Trend so feel free to add each separately to the chart for visual purposes
Trade exit:
Stop Loss: Calculated by recent swing low over previous X candles (configurable with "Local High/Low Lookback")
Take Profit: Calculated from R:R multiplier * Stop Loss size
Credits
Strategy origin: TradeIQ's YouTube video called "I Finally Found 80% Win Rate Trading Strategy For Crypto"
It combines the following indicators for trade entry conditions:
Coral Trend Indicator by @LazyBear (Main indicator)
Absolute Strength Histogram | jh by @jiehonglim (Optional confirmation indicator)
Indicator: HawkEye Volume Indicator by @LazyBear (Optional confirmation indicator)
ADX and DI by @BeikabuOyaji (Optional confirmation indicator)
AlgoTrade DCA Bot Backtester█ OVERVIEW
This script can be used to backtest DCA Bots. It draws inspiration from 3Commas and has most settings that are available on 3Commas. It contains a few popular DCA Bot Presets that are well known in the community for you to test out! Preset used here: Kirigakure V4
█ FEATURES
DCA Preset (Custom, Standard TA,Urma Lite V3,Kirigakure V1,Kirigakure V3,Kirigakure V4)
Order Size Type (Fixed/% of equity to simulate compounding)
Base Order Size
Safety Order Size
Max Safety Trades Count
Price Deviation to open safety order %
Safety Order Volume Scale
Safety Order Step Scale
Take Profit %
Use ADR (Average Daily Range) as Take Profit
ADR length (if ADR as take profit is enabled)
Take Profit Type (% from total volume / % from base order)
Trailing Take Profit
Stop Loss
Deal Start Condition (Start ASAP) ▶ More Deal Starting Conditions will be added in the future
Bot Direction (Long / Short)
Start Time ▶ 1999-01-01 (Use this to always backtest the entire history)
End Time
This strategy also allows you to plot the Average Price and Take Profit of each trade, so it's easier to follow the trade and understand what's happening.
█ HOW TO USE
1. Select a DCA Preset and change the initial capital to the exact amount that is required (seen in the error message on top of the table). When using a Preset the following settings will be locked, meaning if you change them in the script's settings it won't have any effect:
Base Order Size
Safety Order Size
Max Safety Trades Count
Price Deviation to open safety order %
Safety Order Volume Scale
Safety Order Step Scale
Use ADR (Average Daily Range) as Take Profit
1.1 When using Presets you can choose the Order Size Type of Fixed or % of equity which simulates compounding
1.2 Choose a Direction and a Start and End Time
2. To backtest customized settings choose the preset "Custom"
2.1 All other settings are now "unlocked" and can be used
█ LIMITATIONS
Whenever a DCA preset is changed the initial_capital needs to be changed to the exact amount the settings require. If the initial_capital is not the same there will be an error of top of the table. To fix this error navigate to the Script's Settings and Properties and change the initial_capital to the same amount that is stated in the error.
DCA Bots with a high number of safety orders, e.g. 100, can run into an error that says "Maximum number of orders (9000) reached". If this error happens change the backtesting time to a shorter timeframe.
Using % of equity simulates compounding but is unrealistic because you cannot re-invest every single dollar
█ THANKS
This script in insipred by rouxam's "Backtesting 3commas DCA Bot v2" script
Simple and Profitable Scalping Strategy (ForexSignals TV)Strategy is based on the "SIMPLE and PROFITABLE Forex Scalping Strategy" taken from YouTube channel ForexSignals TV.
See video for a detailed explaination of the whole strategy.
I'm not entirely happy with the performance of this strategy yet however I do believe it has potential as the concept makes a lot of sense.
I'm open to any ideas people have on how it could be improved.
Strategy incorporates the following features:
Risk management:
Configurable X% loss per stop (default to 1%)
Configurable R:R ratio
Trade entry:
Based on stratgey conditions outlined below
Trade exit:
Based on stratgey conditions outlined below
Backtesting:
Configurable backtesting range by date
Trade drawings:
Each entry condition indicator can be turned on and off
TP/SL boxes drawn for all trades. Can be turned on and off
Trade exit information labels. Can be turned on and off
NOTE: Trade drawings will only be applicable when using overlay strategies
Debugging:
Includes section with useful debugging techniques
Strategy conditions
Trade entry:
LONG
C1: On higher timeframe trend EMAs, Fast EMA must be above Slow EMA
C2: On higher timeframe trend EMAs, price must be above Fast EMA
C3: On current timeframe entry EMAs, Fast EMA must be above Medium EMA and Medium EMA must be above Slow EMA
C4: On current timeframe entry EMAs, all 3 EMA lines must have fanned out in upward direction for previous X candles (configurable)
C5: On current timeframe entry EMAs, previous candle must have closed above and not touched any EMA lines
C6: On current timeframe entry EMAs, current candle must have pulled back to touch the EMA line(s)
C7: Price must break through the high of the last X candles (plus price buffer) to trigger entry (stop order entry)
SHORT
C1: On higher timeframe trend EMAs, Fast EMA must be below Slow EMA
C2: On higher timeframe trend EMAs, price must be below Fast EMA
C3: On current timeframe entry EMAs, Fast EMA must be below Medium EMA and Medium EMA must be below Slow EMA
C4: On current timeframe entry EMAs, all 3 EMA lines must have fanned out in downward direction for previous X candles (configurable)
C5: On current timeframe entry EMAs, previous candle must have closed above and not touched any EMA lines
C6: On current timeframe entry EMAs, current candle must have pulled back to touch the EMA line(s)
C7: Price must break through the low of the last X candles (plus price buffer) to trigger entry (stop order entry)
Trade entry:
Calculated position size based on risk tolerance
Entry price is a stop order set just above (buffer configurable) the recent swing high/low (long/short)
Trade exit:
Stop Loss is set just below (buffer configurable) trigger candle's low/high (long/short)
Take Profit calculated from Stop Loss using R:R ratio
Credits
"SIMPLE and PROFITABLE Forex Scalping Strategy" taken from YouTube channel ForexSignals TV
SUPPORT RESISTANCE STRATEGY [5MIN TF]A SUPPORT RESISTANCE BREAKOUT STRATEGY for 5 minute Time-Frame , that has the time condition for Indian Markets
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) Price crosses above Resistance Level ,indicated by Red Line, is a Long condition.
2) Price crosses below Support Level ,indicated by Green Line , is a Short condition.
3) Candle high crosses above ema1, is a part of the Long condition .
4) Candle low crosses below ema1, is a part of the Short condition .
5) Volume Threshold is an added confirmation for long/short positions.
6) Maximum Risk per trade for the intraday trade can be changed .
7) Default qty size is set to 50 contracts , which can be changed under settings → properties → order size.
8) ATR is used for trailing after entry, as mentioned in the inputs below.
// ═════════════════════════//
// ————————> INPUTS <————————— //
// ═════════════════════════//
→ L_Bars ———————————> Length of Resistance / Support Levels.
→ R_Bars ———————————> Length of Resistance / Support Levels.
→ Volume Break ———————> Volume Breakout from range to confirm Long/Short position.
→ Price Cross Ema —————> Added condition as explained above (3) and (4).
→ ATR LONG —————————> ATR stoploss trail for Long positions.
→ ATR SHORT ————————> ATR stoploss trail for Short positions.
→ RISK ————————————> Maximum Risk per trade intraday.
The strategy was back-tested on TCS ,the input values and the results are mentioned under "BACKTEST RESULTS" below.
// ═════════════════════════ //
// ————————> PROPERTIES<——————— //
// ═════════════════════════ //
Default_qty_size ————> 50 contracts , which can be changed under
Settings
↓
Properties
↓
Order size
// ═══════════════════════════════//
// ————————> TIME CONDITION <————————— //
// ═══════════════════════════════//
The time can be changed in the script , Add it → click on ' { } ' → Pine editor→ making it a copy [right top corner} → Edit the line 27.
The Indian Markets open at 9:15am and closes at 3:30pm.
The 'time_cond' specifies the time at which Entries should happen .
"Close All" function closes all the trades at 3pm , at the open of the next candle.
To change the time to close all trades , Go to Pine Editor → Edit the line 92 .
All open trades get closed at 3pm , because some brokers don't allow you to place fresh intraday orders after 3pm .
// ═══════════════════════════════════════════════ //
// ————————> BACKTEST RESULTS ( 100 CLOSED TRADES )<————————— //
// ═══════════════════════════════════════════════ //
INPUTS can be changed for better Back-Test results.
The strategy applied to NSE:TCS ( 5 min Time-Frame and contract size 50) gives us 60% profitability , as shown below
It was tested for a period a 6 months with a Profit Factor of 1.8 ,net Profit of 30,000 Rs profit .
Sharpe Ratio : 0.49
Sortino Ratio : 1.4
The graph has a Linear Curve with Consistent Profits.
The INPUTS are as follows,
1) L_Bars —————————> 4
2) R_Bars —————————> 4
3) Volume Break ————> 5
4) Price Cross Ema ——> 100
5) ATR LONG ——————> 2.4
6) ATR SHORT —————> 2.6
7) RISK —————————> 2000
8) Default qty size ——> 50
NSE:TCS
Save it to favorites.
Apply it to your charts Now !!
Thank You ☺ NSE:TCS
Regression Channel, Candles and Candlestick Patterns by MontyRegression Candles by ugurvu
Regression Channel by Tradingview
All Candlestick Patterns By Tradingview
This script was combined for a friend of mine who needed this.
This Script has regression candles by ugurvu, Regression channel and Candlestick patterns by tradingview.
The intention was to fuse these together so more information can be processed on the cost of a single indicator.
SSL + Wave Trend StrategyStrategy incorporates the following features:
Risk management:
Configurable X% loss per stop loss
Configurable R:R ratio
Trade entry:
Based on strategy conditions below
Trade exit:
Based on strategy conditions below
Backtesting:
Configurable backtesting range by date
Trade drawings:
Each entry condition indicator can be turned on and off
TP/SL boxes drawn for all trades. Can be turned on and off
Trade exit information labels. Can be turned on and off
NOTE: Trade drawings will only be applicable when using overlay strategies
Alerting:
Alerts on LONG and SHORT trade entries
Debugging:
Includes section with useful debugging techniques
Strategy conditions
Trade entry:
LONG
C1: SSL Hybrid baseline is BLUE
C2: SSL Channel crosses up (green above red)
C3: Wave Trend crosses up (represented by pink candle body)
C4: Entry candle height is not greater than configured threshold
C5: Entry candle is inside Keltner Channel (wicks or body depending on configuration)
C6: Take Profit target does not touch EMA (represents resistance)
SHORT
C1: SSL Hybrid baseline is RED
C2: SSL Channel crosses down (red above green)
C3: Wave Trend crosses down (represented by orange candle body)
C4: Entry candle height is not greater than configured threshold
C5: Entry candle is inside Keltner Channel (wicks or body depending on configuration)
C6: Take Profit target does not touch EMA (represents support)
Trade exit:
Stop Loss: Size configurable with NNFX ATR multiplier
Take Profit: Calculated from Stop Loss using R:R ratio
Credits
Strategy is based on the YouTube video "This Unique Strategy Made 47% Profit in 2.5 Months " by TradeSmart.
It combines the following indicators to determine trade entry/exit conditions:
Wave Trend: Indicator: WaveTrend Oscillator by @LazyBear
SSL Channel: SSL channel by @ErwinBeckers
SSL Hybrid: SSL Hybrid by @Mihkel00
Keltner Channels: Keltner Channels Bands by @ceyhun
Candle Height: Candle Height in Percentage - Columns by @FreeReveller
NNFX ATR: NNFX ATR by @sueun123
Risk Management Strategy TemplateThis strategy is intended to be used as a base template for building new strategies.
It incorporates the following features:
Risk management:
Configurable X% loss per stop loss
Configurable R:R ratio
Trade entry:
Calculated position size based on risk tolerance
Trade exit:
Stop Loss currently configurable ATR multiplier but can be replaced based on strategy
Take Profit calculated from Stop Loss using R:R ratio
Backtesting:
Configurable backtesting range by date
Trade drawings:
TP/SL boxes drawn for all trades. Can be turned on and off
Trade exit information labels. Can be turned on and off
NOTE: Trade drawings will only be applicable when using overlay strategies
Debugging:
Includes section with useful debugging techniques
Strategy conditions
Trade entry:
LONG
C1: Price is above EMA line
C2: RSI is crossing out of oversold area
SHORT
C1: Price is below EMA line
C2: RSI is crossing out of overbought area
Trade exit:
Stop Loss: Stop Loss ATR multiplier is hit
Take Profit: R:R multiplier * Stop Loss is hit
The idea is to use RSI to catch pullbacks within the main trend.
Note that this strategy is intended to be a simple base strategy for building upon. It was not designed to be traded in its current form.
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%
TTP Kent StratKent Strat trades breakouts using Bollinger Bands together with SuperTrend.
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%
X48 - Strategy | MA Type Cross + TPSL | Future&Spot | V.2Thank You For Open Source Code, This Strategy Ref. By 1.Simple Strategy Like MA Crossover For Long/Short or Spot Trade, 2. CDC Action Zone V.2 for BarPaint
This Strategy Mixing With MA Crossover Strategy and BarPaint By CDC Action Zone and TP/SL by Varbara
### How To Use Strategy : Setting EMA/SMA Crossover EMA/SMA, Any Value If You Want
For Long Position : Cross Up
For Short Position : Cross Down
Can Use With Spot Trade : Cross Up = Buy, Cross Down = Sell
TP/SL When Your OrderSize Change From any % Of Your TP/SL Value
### In Strategy Setting
Intitial Capital = Ex. 200
Order Size = Should Be Money Management Not Use 100% of Capital Ex. 10% of Capital (200$) = Order Size 20$
StopLoss and Take Profit = If You Run Trend TF 4H+ or 1D+ You Can Change TP% = 1,000% for nonlimit and Stop Loss 5 - 20% from your order size
Ex. Stoploss 15% = OrderSize / 100 x %SL = 20$/100 x 15% = 3$ Loss from order size 20$ (if you not set stop loss.)
Base Currency = (Your Currency) # Ex. USD
Commission = (Your Trading Fee) # Ex. Future Fee Can Check At Binance Fee Rate > www.binance.com > Choose Your Fee Type, Ex. USD M Future (Regular User) = 0.02 (Maker), 0.04 (Taker)
Commission Symbol Type = % # (Ref. By Binance Fee Rate)
### Notice ####
Default Setting It's Realistic From Normal Life Ex. Capital 200$ / Ordersize 20$ (10%)/ Commission 0.1% (Buy+Sell) / Slippage = 2 / TP = 1000% (nonlimit) / SL = 15%/OrderSize
Low Risk But High Return, Good Luck
### Bot Auto Trade by X4815162342 ###
if you wanna try my bot auto trade X48-3in1-bot : Contact My Line ID : x4815x
Full Command Alert For This Strategy If You Wanna See It's
'{"ex":"'+markettype+'","side": "'+longcommand+'", "amount": "@{{strategy.order.contracts}}", "symbol": "{{ticker}}", "passphrase": "'+passphrase+'","leverage":"'+str.tostring(leveragex)+'"}'
'{"ex":"'+markettype+'","side": "'+shortcommand+'", "amount": "@{{strategy.order.contracts}}", "symbol": "{{ticker}}", "passphrase": "'+passphrase+'","leverage":"'+str.tostring(leveragex)+'"}'
But Easy Than Full Command Just Use Thisssssss !! Strategy Be Manage Auto Long and Short or TPSL Position
You Don't Do Anything Just Use This Message to Alerts Message
{{strategy.order.alert_message}}
### If you don't use bot but just looking for strategy test ####
Just Pass Bot Setting Function It's Nothing Effect For Strategy !!!!!!
Let's Enjoy With Your Strategy BackTest 😁
Remember Beware Max drawdown%. I'm Recommend Lower Than 10% It's Very Good.
STD-Filtered, Gaussian-Kernel-Weighted Moving Average BT [Loxx]STD-Filtered, Gaussian-Kernel-Weighted Moving Average BT is the backtest for the following indicator
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. Make sure you do your own research so you understand what is happening here.
STD-Filtered, Gaussian-Kernel-Weighted Moving Average is a moving average that weights price by using a Gaussian kernel function to calculate data points. This indicator also allows for filtering both source input price and output signal using a standard deviation filter.
Purpose
This purpose of this indicator is to take the concept of Kernel estimation and apply it in a way where instead of predicting past values, the weighted function predicts the current bar value at each bar to create a moving average that is suitable for trading. Normally this method is used to create an array of past estimators to model past data but this method is not useful for trading as the past values will repaint. This moving average does NOT repaint, however you much allow signals to close on the current bar before taking the signal. You can compare this to Nadaraya-Watson Estimator wherein they use Nadaraya-Watson estimator method with normalized kernel weighted function to model price.
What are Kernel Functions?
A kernel function is used as a weighing function to develop non-parametric regression model is discussed. In the beginning of the article, a brief discussion about properties of kernel functions and steps to build kernels around data points are presented.
Kernel Function
In non-parametric statistics, a kernel is a weighting function which satisfies the following properties.
A kernel function must be symmetrical. Mathematically this property can be expressed as K (-u) = K (+u). The symmetric property of kernel function enables its maximum value (max(K(u)) to lie in the middle of the curve.
The area under the curve of the function must be equal to one. Mathematically, this property is expressed as: integral −∞ + ∞ ∫ K(u)d(u) = 1
Value of kernel function can not be negative i.e. K(u) ≥ 0 for all −∞ < u < ∞.
Kernel Estimation
In this article, Gaussian kernel function is used to calculate kernels for the data points. The equation for Gaussian kernel is:
K(u) = (1 / sqrt(2pi)) * e^(-0.5 *(j / bw )^2)
Where xi is the observed data point. j is the value where kernel function is computed and bw is called the bandwidth. Bandwidth in kernel regression is called the smoothing parameter because it controls variance and bias in the output.
EMA Cross (data chaining template)Hello Traders! This is the source code for a simple EMA Cross indicator that can be chained to other indicators or another instance of itself as a filter.
The code is fully commented to guide you in building your own chain of indicators based on your ideas and trading plans.
All the best,
Val - Protervus Trading
Unicorn X-AlgoUnicorn X-Algo is a multifunctional trading indicator. It is designed to help traders make real-time decisions using quantitative models.
Its core is a trend trading strategy based on our enhanced Trailing Stop-Loss algorithm. This strategy provides the user with position entry and exit signals. It is customizable and has a built-in instant backtesting feature.
For those who have difficulty with finding the good settings the indicator has the Automatic Mode. In this mode, there is no need for the user to adjust any settings. The indicator calculates optimized trading signals automatically.
In addition, the indicator provides a number of useful tools that aim to provide additional confirmation to the trading signals. They include: support and resistance levels forecast, price range prediction and institutional activity detection.
The script can send real-time alerts to the user’s Email and to the cell phone via notifications in the TradingView app.
The indicator can be used for various types of trend and swing trading, including positional trading, day trading and scalping.
Unicorn X-Algo allows users to:
forecast direction of trends with BUY and SELL signals;
determine the right time to close a position;
detect institutional activity in the market;
forecast key support and resistance levels;
predict the future price range for any market;
customize any settings and do a backtest with one click;
see historical trades on the chart;
use the fully Automatic Mode where the algorithm optimizes all its settings itself.
When using this script, keep in mind that past results do not necessarily reflect future results and that many factors influence trading results.
FEATURES
Trading signals
The feature calculates trend or swing entry and exit signals. The underlying strategy does not use fixed Take-Profit levels. It trails the price with a Trailing Stop-Loss to get as many pips as possible from price movements.
The feature is based on our custom Volatility Stop algorithm. It uses linear regressions instead of averaging. As our practice shows, this helps to reduce signal lag while keeping the number of false signals low.
Trading signals are customizable with Sensitivity and Trade Length parameters which determine the trading signals frequency and width of the Trailing-Stop levels, respectively.
Automatic Mode
The Trading Signals function has an automatic mode. When it is turned on, you do not need to adjust the trading signals settings. The algorithm tries to calculate the best settings automatically using an optimization algorithm.
In this mode, Buy and Sell signals are displayed as green and red triangles respectively. There are two types of exit signals displayed as circles and crosses. A circle signal means that a price reversal is expected and you can partially close the position. A cross signal means that a trading signal in the opposite direction is expected soon and you can partially or completely close your position.
Support and Resistance Levels
Support/Resistance levels forecasting model. The forecasted levels are non-repainting. Once calculated for a specified period in the future (day, week, month, etc.), they don't change during this period.
The feature allows the trader to plan trades and use the forecasted levels as entry levels and targets for opening and closing positions. Both intraday and higher timeframes are supported.
The forecasting model analyses the distribution of the price time series to find clusters in the data. These clusters are then used to make the key price levels forecast.
Big Money Activity detection
The Big Money Activity tool identifies areas on the price chart associated with instructional traders' activity in the market.
Institutional activity in a trending market can be a leading signal for upcoming reversal. Institutions could be fixing their profit, causing the price to move against the current trend.
Institutional activity in a sideways market can be due to positions accumulation and signal a new trend formation.
The algorithm uses tick volume, volume, and volatility data to forecast activity of institutional investors. The method develops the idea described in the Daigler & Wiley (2015) and Shalen (1993) works. It says that when institutional traders actively open or close their positions in the market, a divergence between volume and volatility time-series arises. It can be due to their use of position-splitting algorithms that reduce the impact of their positions on the market.
Trading Range Forecast
Trading Range Forecast feature predicts the price range of an asset for a selected period of time in the future, called Forecast Horizon. It can be the next day or 12-hour trading session. This function works if your chart timeframe is intraday (i.e. the timeframe below "D"). It shows the upper and lower bounds between which the price is going to stay in the upcoming Forecast Horizon period.
Instant Backtesting
After changing any settings, you can immediately see the performance of the strategy on the Instant Backtesting panel. Two metrics are displayed there - the percentage of profitable trades and the total return. This information, as well as the historical trades shown on the chart, will help you quickly and easily evaluate any settings you make.
SETTINGS
TRADING SIGNALS
Trade Length - defines the length of the trades the algorithm tries to make. Recommended values are from 1.0 to 6.0.
Sensitivity - controls the sensitivity of the trading signals algorithm. The sensitivity determines the density of trading signals and how close the trailing-stop levels follow the price. The higher the value of this parameter is, the less sensitive the algorithm is. High values of the Sensitivity parameters (100-500) can help to withstand large price swings to stay in longer price moves. Lower values (10-100) work well for short- and medium-term trades.
TRADING TOOLS
Big Money Activity - turns on and off the identification of the areas associated with institutional traders activity.
SUPPORТ AND RESISTANCЕ LEVELS
Show Support And Resistance Levels - turns on and off support and resistance levels calculation.
TRADING RANGE FORECAST
Show Trading Range Forecast - turns on/off trading range forecasting
Forecast Horizon - sets the period for which the trading range forecast is made
Forecasting Method - allows to choose a forecasting algorithm for the trading range forecast.
BACKTESTING
Use Starting Date - turns on/off the starting date for the strategy and backtests. When off, all available historical data is used.
Starting Date - sets the starting date for the strategy and backtests.
Show Instant Backtesting Dashboard - turns on/off a dashboard that shows the current strategy performance: the percentage of profitable trades and total return.
Leverage - sets the leverage that the strategy uses.
Unicorn QuantDeeply customizable trading algorithm with instant backtesting. It emulates real trading and displays all the actions it takes on the chart. For example, it shows when to enter or partially close a position, move Stop-Loss to breakeven, etc. The user can replicate these actions in their trading terminal in real time. The algorithm uses up to three Take-Profit levels, and a Stop-Loss level that can move in a trade to protect the floating profit.
The script can send real-time alerts to the user’s Email and to the cell phone via notifications in the TradingView app.
The indicator is designed to be used on all timeframes, including lower ones for intraday trading and scalping.
HOW TO USE
Set the Stop-Loss and up to three Take-Profit levels. Choose the rules for moving the Stop-Loss level in a trade. Adjust the sensitivity of the trading signals. And check the backtest result in the Instant Backtesting dashboard. If the performance of the strategy satisfies you, proceed with the forward testing or live trading.
When using this script, please, keep in mind that past results do not necessarily reflect future results and there are many factors that influence trading results.
FEATURES
Trading Signals
The feature calculates Buy and Sell signals for trend or swing trading. The user can change the Sensitivity parameter to control the frequency of the signals. This allows them to be adjusted for different markets and timeframes.
Position Manager
To make the Position Manager setup as easy as possible, the algorithm calculates Stop-Loss and Take-Profit levels in Average True Range (ATR) units. They are self-adjusting for any market and timeframe, since they account for its average volatility .
You don't have to worry about what market you are trading - Forex, Stocks, Crypto, etc. With the self-adjusting Stop-Loss and Take-Profit, you can find settings that work for one market and use the same numerical values as a starting point for a completely different market.
Instant Backtesting
After changing any settings, you can immediately see the performance of the strategy on the Instant Backtesting panel. Two metrics are displayed there - the percentage of profitable trades and the total return. This information, as well as the historical trades shown on the chart, will help you quickly and easily evaluate the settings.
SETTINGS
TRADING SIGNALS
Sensitivity - controls the sensitivity of the trading signals algorithm. It determines the frequency of the trading signals. The higher the value of this parameter, the less trading signals you get and the longer trends the algorithm tries to catch. The lower the sensitivity value, the more signals you receive. This can be useful if you want to profit from small price movements.
POSITION MANAGER
SL - sets the Stop-Loss level measured in ATR units.
TP1, TP2, TP3 - set the Take-Profit levels measured in the ATR units.
Close % at TP1, Close % at TP2, Close % at TP3 - set portions of the open position (as a percentage of the initial order size) to close at each of the TP levels.
At TP1 move SL to, At TP2 move SL to - set the rules for moving the Stop-Loss level in an open trade to protect the floating profit.
Show Open Position Dashboard - turns on/off a dashboard that shows the current Stop-Loss and Take-Profit levels for the open position.
BACKTESTING
Use Starting Date - turns on/off the starting date for the strategy and backtests. When off, all available historical data is used.
Starting Date - sets the starting date for the strategy and backtests.
Show Instant Backtesting Dashboard - turns on/off a dashboard that shows the current strategy performance: the percentage of profitable trades and total return.
Leverage - sets the leverage that the strategy uses.
Breakout Probability (Expo)█ Overview
Breakout Probability is a valuable indicator that calculates the probability of a new high or low and displays it as a level with its percentage. The probability of a new high and low is backtested, and the results are shown in a table— a simple way to understand the next candle's likelihood of a new high or low. In addition, the indicator displays an additional four levels above and under the candle with the probability of hitting these levels.
The indicator helps traders to understand the likelihood of the next candle's direction, which can be used to set your trading bias.
█ Calculations
The algorithm calculates all the green and red candles separately depending on whether the previous candle was red or green and assigns scores if one or more lines were reached. The algorithm then calculates how many candles reached those levels in history and displays it as a percentage value on each line.
█ Example
In this example, the previous candlestick was green; we can see that a new high has been hit 72.82% of the time and the low only 28.29%. In this case, a new high was made.
█ Settings
Percentage Step
The space between the levels can be adjusted with a percentage step. 1% means that each level is located 1% above/under the previous one.
Disable 0.00% values
If a level got a 0% likelihood of being hit, the level is not displayed as default. Enable the option if you want to see all levels regardless of their values.
Number of Lines
Set the number of levels you want to display.
Show Statistic Panel
Enable this option if you want to display the backtest statistics for that a new high or low is made. (Only if the first levels have been reached or not)
█ Any Alert function call
An alert is sent on candle open, and you can select what should be included in the alert. You can enable the following options:
Ticker ID
Bias
Probability percentage
The first level high and low price
█ How to use
This indicator is a perfect tool for anyone that wants to understand the probability of a breakout and the likelihood that set levels are hit.
The indicator can be used for setting a stop loss based on where the price is most likely not to reach.
The indicator can help traders to set their bias based on probability. For example, look at the daily or a higher timeframe to get your trading bias, then go to a lower timeframe and look for setups in that direction.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Strategy BackTest Display Statistics - TraderHalaiThis script was born out of my quest to be able to display strategy back test statistics on charts to allow for easier backtesting on devices that do not natively support backtest engine (such as mobile phones, when I am backtesting from away from my computer). There are already a few good ones on TradingView, but most / many are too complicated for my needs.
Found an excellent display backtest engine by 'The Art of Trading'. This script is a snippet of his hard work, with some very minor tweaks and changes. Much respect to the original author.
Full credit to the original author of this script. It can be found here: www.tradingview.com
I decided to modify the script by simplifying it down and make it easier to integrate into existing strategies, using simple copy and paste, by relying on existing tradingview strategy backtester inputs. I have also added 3 additional performance metrics:
- Max Run Up
- Average Win per trade
- Average Loss per trade
As this is a work in progress, I will look to add in more performance metrics in future, as I further develop this script.
Feel free to use this display panel in your scripts and strategies.
Thanks and enjoy :)
Smoothed Heikin Ashi Trend on Chart - TraderHalai BACKTESTSmoothed Heikin Ashi Trend on chart - Backtest
This is a backtest of the Smoothed Heikin Ashi Trend indicator, which computes the reverse candle close price required to flip a Heikin Ashi trend from red to green and vice versa. The original indicator can be found in the scripts section of my profile.
This particular back test uses this indicator with a Trend following paradigm with a percentage-based stop loss.
Note, that backtesting performance is not always indicative of future performance, but it does provide some basis for further development and walk-forward / live testing.
Testing was performed on Bitcoin , as this is a primary target market for me to use this kind of strategy.
Sample Backtesting results as of 10th June 2022:
Backtesting parameters:
Position size: 10% of equity
Long stop: 1% below entry
Short stop: 1% above entry
Repainting: Off
Smoothing: SMA
Period: 10
8 Hour:
Number of Trades: 1046
Gross Return: 249.27 %
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 28.01 %
Profit Factor (Expectancy): 2.019
Average Loss: 0.33 %
Average Win: 1.69 %
Average Time for Loss: 1 day
Average Time for Win: 5.33 days
1 Day:
Number of Trades: 429
Gross Return: 458.4 %
CAGR Return: 15.76 %
Max Drawdown: 6.37 %
Profit Factor (Expectancy): 2.804
Average Loss: 0.8 %
Average Win: 7.2 %
Average Time for Loss: 3 days
Average Time for Win: 16 days
5 Day:
Number of Trades: 69
Gross Return: 1614.9 %
CAGR Return: 26.7 %
Max Drawdown: 5.7 %
Profit Factor (Expectancy): 10.451
Average Loss: 3.64 %
Average Win: 81.17 %
Average Time for Loss: 15 days
Average Time for Win: 85 days
Analysis:
The strategy is typical amongst trend following strategies with a less regular win rate, but where profits are more significant than losses. Most of the losses are in sideways, low volatility markets. This strategy performs better on higher timeframes, where it shows a positive expectancy of the strategy.
The average win was positively impacted by Bitcoin’s earlier smaller market cap, as the percentage wins earlier were higher.
Overall the strategy shows potential for further development and may be suitable for walk-forward testing and out of sample analysis to be considered for a demo trading account.
Note in an actual trading setup, you may wish to use this with volatility filters, combined with support resistance zones for a better setup.
As always, this post/indicator/strategy is not financial advice, and please do your due diligence before trading this live.
Original indicator links:
On chart version -
Oscillator version -
Update - 27/06/2022
Unfortunately, It appears that the original script had been taken down due to auto-moderation because of concerns with no slippage / commission. I have since adjusted the backtest, and re-uploaded to include the following to address these concerns, and show that I am genuinely trying to give back to the community and not mislead anyone:
1) Include commission of 0.1% - to match Binance's maker fees prior to moving to a fee-less model.
2) Include slippage of 10 ticks (This is a realistic slippage figure from searching online for most crypto exchanges)
3) Adjust account balance to 10,000 - since most of us are not millionaires.
The rest of the backtesting parameters are comparable to previous results:
Backtesting parameters:
Initial capital: 10000 dollars
Position size: 10% of equity
Long stop: 2% below entry
Short stop: 2% above entry
Repainting: Off
Smoothing: SMA
Period: 10
Slippage: 10 ticks
Commission: 0.1%
This script still remains to shows viability / profitablity on higher term timeframes (with slightly higher drawdown), and I have included the backtest report below to document my findings:
8 Hour:
Number of Trades: 1082
Gross Return: 233.02%
CAGR Return: 14.04 %
Max Drawdown: 7.9 %
Win percentage: 25.6%
Profit Factor (Expectancy): 1.627
Average Loss: 0.46 %
Average Win: 2.18 %
Average Time for Loss: 1.33 day
Average Time for Win: 7.33 days
Once again, please do your own research and due dillegence before trading this live. This post is for education and information purposes only, and should not be taken as financial advice.
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
TTP RSI drawdownThis indicator offers a way to trade the RSI drawdown combined together with its value.
Parameters
- Lookback. How many candles in the past are used to calculate the drawdown of the RSI
- RSI strength and timeframe
- Lower and Upper RSI limits. These are used to colour the drawdown chart, the greener the closed to your specified lower limit and the more red the closer to the upper limit. These limits are also used to confirm that the signal occurs with an RSI value within the given limits.
- Signal threshold. Buy signals will only be triggered when the drawdown of the RSI is higher than the provided value.
Buy signals
The signal will trigger in white color and will plot a 1 to allow backtesting.
TradingView alerts can be set for buy signals.
Buy signals will be trigger only when:
- RSI value is within the provided lower and upper limit values
- RSI drawdown is higher than the provided threshold.
Features
- Back-testable (plots 1 for buy)
- TV alerts
Chart modes
- show RSI
- show highest RSI
- show RSI drawdown
In the short time I've been playing with this new idea I noticed it can be used both for swing trading and for long term analysis like for example finding market bottoms.
Here's an example setup to find BTC past market cycle bottoms:
- load a chart with INDEX:BTCUSD
- set the chart timeframe to daily
- set the chart to logarithmic scale
- zoom out to see the full chart
- add the RSI drawdown indicator
- use the following parameters in the indicator: timeframe: week, gaps: off, show RSI drawdown: on, lookback:30, length:14, lower: 20, upper: 30: threshold: 40
Adaptive, Zero lag Schaff Trend Cycle Backtest (Simple) [Loxx]Simple backtest for "Adaptive, Zero lag Schaff Trend Cycle" found here:
What this backtest includes:
-Customization of inputs for Schaff Trend Cycle calculation
-Take profit 1 (TP1), and Stop-loss (SL), calculated using standard RMA-smoothed true range
-Activation of TP1 after entry candle closes
-Zero-cross entry signal plots
-Longs and shorts
-Continuation longs and shorts
Happy trading!
