Visual ATR StopThis indicator uses the Average True Range (ATR) to display a visual range for stop placement. Two multiplier values (example, 1 and 3) can be set to create a filled area below the price. This area represents the range between the two ATR levels, adjusted by subtracting the current price, providing a simple way to visualize stop-loss placement based on volatility.
The indicator is customizable; for example, negative values can place the area above the price for short positions. The filled color can also be removed, which allows precise levels to be marked above and below.
Riskmanagment
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)
QTY@RISK VWAP based calculationVWAP Volatility-Based Risk Management Calculator for Intraday Trading
Overview
This script is an innovative tool designed to help traders manage risk effectively by calculating position sizes and stop-loss levels using the Volume Weighted Average Price (VWAP) and its standard deviation (StdDev). Unlike traditional methods that rely on time-based calculations, this approach is time-independent within the intraday timeframe, making it particularly useful for traders seeking precision and efficiency.
Key Concepts
VWAP (Volume Weighted Average Price): VWAP is a trading benchmark that represents the average price a security has traded at throughout the day, based on both volume and price. It provides insight into the average price level over a specific period, helping traders understand the market trend.
StdDev (Standard Deviation): In the context of VWAP, the standard deviation measures the volatility around the VWAP. It provides a quantifiable range that traders can use to set stop-loss levels, ensuring they are neither too tight nor too loose.
How the Script Works
1. VWAP Calculation: The script calculates the VWAP continuously as the market trades, integrating both price and volume data.
2. Volatility Measurement: It then computes the standard deviation of the VWAP, giving a measure of market volatility.
3. Stop-Loss Calculation: Using user-defined StdDev factors, the script calculates two stop-loss levels. These levels adjust dynamically based on market conditions, ensuring they remain relevant throughout the trading session.
4. Position Sizing: By incorporating your risk tolerance, the script determines the appropriate position size. This ensures that your maximum loss per trade does not exceed your predefined risk value.
How to Use the Calculator
1. Select Two VWAP StdDev Factors: Choose two standard deviation factors for calculating stop-loss levels. For example, you might choose 0.5 and 0.75 to set conservative and aggressive stop-losses respectively.
2. Set Your Trading Account Size: Enter your total trading capital. For example, $50,000.
3. Maximum Lot Size: Define the maximum number of shares you are willing to trade in a single position. For instance, 200 shares.
4. Risk Value per Trade: Input the maximum amount of money you are willing to risk on a single trade. For instance, $50.
5. Plotting Options: If you wish to visualize the stop-loss levels, enable the plot option and choose the price base for the plot, such as the closing price or the average of the high and low prices (hl2).
Example of Use
1. Initial Setup: After the market opens, wait for at least 15 minutes to ensure the VWAP has stabilized with sufficient volume data.
2. Parameter Configuration: Input your desired parameters into the calculator. For instance:
- VWAP StdDev Factors: 0.5 and 0.75
- Trading Account Size: $50,000
- Maximum Lot Size: 200 shares
- Risk Value per Trade: $50
- Plot Option: On, using "hl2" or "close" as the price base
3. Execution: Based on the inputs, the script calculates the position size and stop-loss levels. If the calculated stop-loss falls within the selected VWAP StdDev range, it will provide you with precise stop-loss prices.
4. Trading: Use the calculated position size and stop-loss levels to execute your trades confidently, knowing that your risk is managed effectively.
Advantages for Traders
- Time Independence: By relying on VWAP and its StdDev, the calculations are not dependent on specific time intervals, making them more adaptable to real-time trading conditions.
- Focus on Strategy: Novice traders can focus more on their trading strategies rather than getting bogged down with complex calculations.
- Dynamic Adjustments: The script adjusts stop-loss levels dynamically based on evolving market conditions, providing more accurate and relevant risk management.
- Flexibility: Traders can tailor the calculator to their risk preferences and trading style by adjusting the StdDev factors and risk parameters.
By incorporating these concepts and using this risk management calculator, traders can enhance their trading efficiency, improve their risk management, and ultimately make more informed trading decisions.
RSI Multi Strategies With Overlay SignalsHello everyone,
In this indicator, you will find 6 different entry and exit signals based on the RSI :
Entry into overbought and oversold zones
Exit from overbought and oversold zones
Crossing the 50 level
RSI cross RSI MA below or above the 50 level
RSI cross RSI MA in the overbought or oversold zones
RSI Divergence
With the signals identified, you can create your own strategy . (If you have any suggestions, please mention them in the comments).
Beyond these signals, you can set SL (Stop Loss) and TP (Take Profit) levels to better manage your positions.
SL Methods:
Percentage: The stop loss is determined by the percentage you specify.
ATR : The stop level is determined based on the Average True Range (ATR).
TP Methods:
Percentage: The take profit is determined by the percentage you specify.
RR ( Risk Reward ): The take profit level is determined based on the distance from the stop level.
You can mix and match these options as you like.
What makes the indicator unique and effective is its ability to display the RSI in the bottom chart and the signals, SL (Stop Loss), and TP (Take Profit) levels in the overlay chart simultaneously. This feature allows you to manage your trading quickly and easily without the need for using two separate indicators.
Let's try out a few strategies together.
My entry signal: RSI Entered OS (Oversold) Zone
My exit signal: RSI Entered OB (Overbought) Zone
I'm not using a stoploss for this strategy ("Fortune favors the brave").
Let's keep ourselves safe by adding a stop loss.
I'm adding an ATR-based stop loss.
I think it's better now.
If you have any questions or suggestions about the indicator, you can contact me.
Cheers
Risk Management Chart█ OVERVIEW
Risk Management Chart allows you to calculate and visualize equity and risk depend on your risk-reward statistics which you can set at the settings.
This script generates random trades and variants of each trade based on your settings of win/loss percent and shows it on the chart as different polyline and also shows thick line which is average of all trades.
It allows you to visualize and possible to analyze probability of your risk management. Be using different settings you can adjust and change your risk management for better profit in future.
It uses compound interest for each trade.
Each variant of trade is shown as a polyline with color from gradient depended on it last profit.
Also I made blurred lines for better visualization with function :
poly(_arr, _col, _t, _tr) =>
for t = 1 to _t
polyline.new(_arr, false, false, xloc.bar_index, color.new(_col, 0 + t * _tr), line_width = t)
█ HOW TO USE
Just add it to the cart and expand the window.
█ SETTINGS
Start Equity $ - Amount of money to start with (your equity for trades)
Win Probability % - Percent of your win / loss trades
Risk/Reward Ratio - How many profit you will get for each risk(depends on risk per trade %)
Number of Trades - How many trades will be generated for each variant of random trading
Number of variants(lines) - How many variants will be generated for each trade
Risk per Trade % -risk % of current equity for each trade
If you have any ask it at comments.
Hope it will be useful.
VaR Market Sentiment by TenozenHello there! I am excited to share with you my new trading concept implemented in the "VaR Market Sentiment" indicator. But before that, let me explain what VaR is. VaR, or Value at Risk, is an indicator that helps you identify the worst-case scenario of a market movement based on a percentile/confidence level. This means that it calculates the worst moves, whether it's a buy or sell, based on the timeframe you're using.
Now, let's discuss how VaR Market Sentiment works. It uses a historical VaR to calculate the worst move either if the market goes up or down based on a percentile/confidence level. The default setting is the 95th percentile, which means that the market is unlikely to hit your SL level within the day if you're using a daily timeframe, etc.
To determine the strength of a candle, it subtracts the value of both sides based on the returns of the current timeframe with the VaR value (Bullish VaR - Bullish Returns, Bearish VaR - Bearish Returns). If the result is above the mean, the current candle is potentially weak. Conversely, if the result is below the mean, the current candle is potentially strong. The deviation shows critical sentiments, where if the market is above the deviation, it means that the current candle is really weak. If it's below the deviation, it means that the current candle is really strong.
It's important to note that this indicator needs other supporting indicators such as trend-following or mean reversion indicators based on your trading style. Also, as a follow-up to my previous concept, I called out that the market has what's called "power." And for now, I conclude that VaR Market Sentiment is the "power."
I'm going to share more helpful indicators in the future! I hope this indicator will be helpful for you guys! Ciao!
QTY@RISKWhat it does:
This indicator calculates the amount of shares to take at a predefined risk according to market volatility based on ATR.
This should help novice traders focus more on their trades and strategies instead of spending too much time calculating parameters.
How it works:
You have some configuration parameters
1. Number of candles used to calculate the ATR
2. ATR calculation method (SMA, RMA, EMA, WMA)
3. How much you want to risk in $.
4. Safety factor on ATR, to get a buffer
5. Size of your capital to be able to calculate the maximum amount of shares
6. Shares limit, if you have a certain limit of shares to take
The formula is simple:
Is the RISK/ATR * price > Equity ? then take the Equity/price
otherwise: SHARES = RISK/(ATR * safety_factor)
How to use it:
This indicator is most useful for intraday timeframe. If you trade on 1m, then use it in this timeframe
Mason’s Line IndicatorThe Macon Strategy is an idea conceived by Didier Darcet , co-founder of Gavekal Intelligence Software. Inspired by the Water Level, an instrument used by masons to check the horizontality or verticality of a wall. This method aims to measure the psychology of financial markets and determine if the market is balanced or tilting towards an unfavorable side, focusing on the behavioral risk of markets rather than economic or political factors.
The strategy examines the satisfaction and frustration of investors based on the distance between the low and high points of the market over a period of one year. Investor satisfaction is influenced by the current price of the index and the path taken to reach that price. The distance to the low point provides satisfaction, while the distance to the high point generates frustration. The balance between the two dictates investors’ desire to hold or sell their positions.
To refine the strategy, it is important to consider the opinion of a group of investors rather than just one individual. The members of a hypothetical investor club invest successively throughout the past year. The overall satisfaction of the market on a given day is a democratic expression of all participants.
If the overall satisfaction is below 50%, investors are frustrated and sell their positions. If it is above, they are satisfied and hold their positions. The position of the group of investors relative to the high and low points represents the position of the air bubble in the water level. Market performance is measured day by day based on participant satisfaction or dissatisfaction.
In conclusion, memory, emotions, and decision-making ability are closely linked, and their interaction influences investment decisions. The Macon Strategy highlights the importance of the behavioral dimension in understanding financial market dynamics. By studying investor behavior through this strategy, it is possible to better anticipate market trends and make more informed investment decisions.
Presentation of the Mason’s Line Indicator:
The main strategy of this indicator is to measure the average satisfaction of investors based on the position of an imaginary air bubble in a tube delimited by the market’s highs and lows over a given period. After calculating the satisfaction level, it is then normalized between 0 and 1, and a moving average can be used to visualize trends.
Key features:
Calculation of highs and lows over a user-defined period.
Determination of the position of the air bubble in the tube based on the closing price.
Calculation of the average satisfaction of investors over a selected period.
Normalization of the average satisfaction between 0 and 1.
Visualization of normalized or non-normalized average satisfaction levels, as well as their corresponding moving averages.
User parameters:
Period for min and max (days) : Sets the period over which highs and lows will be calculated (1 to 365 days).
Period for average satisfaction (days) : Determines the period over which the average satisfaction of investors will be calculated (1 to 365 days).
Period for SMA : Sets the period of the simple moving average used to smooth the data (1 to 1000 days).
Bubble_value : Adjustment of the air bubble value, ranging from 0 to 1, in increments of 0.025.
Normalized average satisfaction : Option to choose whether to display the normalized or non-normalized average satisfaction.
Please note that the Mason’s Line Indicator is not a guarantee of future market performance and should be used in conjunction with proper risk management. Always ensure that you have a thorough understanding of the indicator’s methodology and its limitations before making any investment decisions. Additionally, past performance is not indicative of future results.
Basic Position Calculator (BPC)In trading, proper position sizing is essential to managing risk and maximizing returns. The script provided is a Basic Position Calculator that allows traders to quickly and easily calculate their position size, stop loss, take profit, and risk reward ratio for a given trade.
The script starts by defining several inputs for the user to customize the calculations. The first input is the "Account Size", which specifies the total amount of funds available for the trade. The next input is "Risk Amount %", which is the percentage of the account size that the trader is willing to risk per trade. The "Stop Loss" input specifies the maximum amount of loss that the trader is willing to accept, while the "Reward" input is the desired profit target for the trade. Finally, there is a "Position" input that allows the user to specify where on the chart the table of calculations will be displayed.
The script then calculates the position size, stop loss, take profit and risk reward ratio using the user-specified inputs. The position size is calculated by dividing the risk amount by the stop loss. The stop loss is calculated by multiplying the stop loss percentage by the close price, and the take profit is calculated by multiplying the stop loss percentage by the close price and the reward. Risk-reward ratio is the ratio of amount of profit potential to the amount of risk in a trade.
The script then creates a table and displays the calculated values on the chart at the specified location. The table includes the following information: account size, position size, account risk %, stop loss, stop loss %, take profit, take profit % and risk reward ratio. This allows the trader to quickly and easily see all the key calculations for their trade in one place.
Overall, the Basic Position Calculator script is a valuable tool for any trader looking to quickly and easily calculate their position size, stop loss, take profit, and risk reward ratio for a given trade. The ability to customize the inputs and display the calculations on the chart makes it a useful and user-friendly tool for managing risk and maximizing returns.
TrapulatorA position size, stop loss and take profit calculator to make forex trading easier.
Utilizes the symbol, payout rates, etc.
How to Use:
Go to the indicator's settings and change the "Settings" and "Entry" sections. After saving your settings, the values will be drawn in a table on the chart.
This has been republished, so that it has the correct name. You can't change the name of scripts once they've been published as far as I'm aware. So, this version of the script will receive updates, and the Trap Calculator will just be a 1.0 version, that's available.
There are default values within the calculator, so use at your own risk and do your own due diligence. This is public so that you can use it to make a calculator that better suits your needs if you want.
6 Multi-Timeframe Supertrend with Heikin Ashi as Source
This is a multiple multi-timeframe version of famous supertrernd only with Heikin Ashi as source. Atr which stands in the heart of supertrend is calculated based on heikin-ashi bars which omits a great deal of noises.
with 6 multiplication of the supertrend, its simply much easier to spot trend direction or use it as trailing stop with several levels available.
this is a great tool to assess and manage your risk and calculate your position volume if you use the heikin ashi supertrend as your stoploss.
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
Manual Stop Loss / Risk Management PanelHere is a panel where you enter the desired stop-loss price, the amount you would like to risk and it spits out what you should trade to only lose that amount if the stop-loss is hit.
Ultimate risk management toolHow to use:
Use the cursor to select the time, entry, stop loss, and target position. Then a window will pop up and type the trading fee or any other things you want to adjust to calculate the actual reward/risk ratio according to the price you selected.
Known error:
Settings of this script can't be saved as default might due to the interactive price selection function. If anyone knows how to fix it, please let me know.
feature:
1. Dynamic profit label can move up and down vertically on the right-hand side of the box.
2. The breakeven line can tell you you can move your stop loss to the entry price when the price reaches it.
3. Calculate the actual reward/risk ratio based on the trading fee. The calculator only calculates the actual Risk/Reward Ratio, which might be helpful for scalpers.
4. When the price touches sl or tp, that side of the box will be highlighted. Sometimes it doesn't work but I will try my best to fix it. Feel free to share your idea to help me to fix it.
5. Price alert. This tool compares with the alert function but reopens it if you want to change the alert price.
Full strategy AllinOne with risk management MACD RSI PSAR ATR MAHey, I am glad to present you one of the strategies where I put a lot of time in it.
This strategy can be adapted to all type of timecharts like scalping, daytrading or swing.
The context is the next one :
First we have the ATR to calculate our TP/SL points. At the same time we have another rule once we enter(we enter based on % risk from total equity, in this example 1%, at the same time, lowest ammount for this example is 0.1 lots, but can be modified to 0.01), so we can exit both by tp/sl points, or by losing 1% of our equity or winning 1% of our total equity. It's dinamic.
The strategy is made from
Trend direction :
PSAR
First confirmation point :
Crossover between 10EMA and Bollinger bands middle point
Second confirmation
MACD histogram
Third confirmation
RSI overbought/oversold levels
For entries : we check trend with psar, then once ema cross bb middle point, we confirm together with rsi level for overbought/oversold and macd histogram ( > 0 or <0).
We exit, when we have opposite sign, like from buy to sell or sell to buy, or when we reach tp/sl points, or when we reach % basaed equity points.
It can be changed to be fixed lots, or fixed tp/sl , you just have to uncomment the size from entries, and tp/sl lines.
At the same time, it has the possibility if one desires, to trade only concrete forex session like european, asian and so on for intraday trading.
Hope you enjoy it.
Let me know how it goes.
