Averaging Down Strategy1. Averaging Down:
Definition: "Averaging Down" is a strategy in which an investor buys more shares of a declining asset, thus lowering the average purchase price. The main idea is that, by averaging down, the investor can recover faster when the price eventually rebounds.
Risk Considerations: This strategy assumes that the asset will recover in value. If the price continues to decline, however, the investor may suffer larger losses. Academic research highlights the psychological bias of loss aversion that often leads investors to engage in averaging down, despite the increased risk (Barberis & Huang, 2001).
2. RSI (Relative Strength Index):
Definition: The RSI is a momentum oscillator that measures the speed and change of price movements. It ranges from 0 to 100 and is commonly used to identify overbought or oversold conditions. A reading below 30 (or in this case, 35) typically indicates an oversold condition, which might suggest a potential buying opportunity (Wilder, 1978).
Risk Considerations: RSI-based strategies can produce many false signals in range-bound or choppy markets, where prices do not exhibit strong trends. This can lead to multiple losing trades and an overall negative performance (Gencay, 1998).
3. Combination of RSI and Price Movement:
Approach: The combination of RSI for entry signals and price movement (previous day's high) for exit signals aims to capture short-term market reversals. This hybrid approach attempts to balance momentum with price confirmation.
Risk Considerations: While this combination can work well in trending markets, it may struggle in volatile or sideways markets. Additionally, a significant risk of averaging down is that the trader may continue adding to a losing position, which can exacerbate losses if the price keeps falling.
Risk Warnings:
Increased Losses Through Averaging Down:
Averaging down involves buying more of a falling asset, which can increase exposure to downside risk. Studies have shown that this approach can lead to larger losses when markets continue to decline, especially during prolonged bear markets (Statman, 2004).
A key risk is that this strategy may lead to significant capital drawdowns if the price of the asset does not recover as expected. In the worst-case scenario, this can result in a total loss of the invested capital.
False Signals with RSI:
RSI-based strategies are prone to generating false signals, particularly in markets that do not exhibit strong trends. For example, Gencay (1998) found that while RSI can be effective in certain conditions, it often fails in choppy or range-bound markets, leading to frequent stop-outs and drawdowns.
Psychological Bias:
Behavioral finance research suggests that the "Averaging Down" strategy may be influenced by loss aversion, a bias where investors prefer to avoid losses rather than achieve gains (Kahneman & Tversky, 1979). This can lead to poor decision-making, as investors continue to add to losing positions in the hope of a recovery.
Empirical Studies:
Gencay (1998): The study "The Predictability of Security Returns with Simple Technical Trading Rules" found that technical indicators like RSI can provide predictive value in certain markets, particularly in volatile environments. However, they are less reliable in markets that lack clear trends.
Barberis & Huang (2001): Their research on behavioral biases, including loss aversion, explains why investors are often tempted to average down despite the risks, as they attempt to avoid realizing losses.
Statman (2004): In "The Diversification Puzzle," Statman discusses how strategies like averaging down can increase risk exposure without necessarily improving long-term returns, especially if the underlying asset continues to perform poorly.
Conclusion:
The "Averaging Down Strategy with RSI" combines elements of technical analysis with a psychologically-driven averaging down approach. While the strategy may offer opportunities in trending or oversold markets, it carries significant risks, particularly in volatile or declining markets. Traders should be cautious when using this strategy, ensuring they manage risk effectively and avoid overexposure to a losing position.
Oscillators
Combo 2/20 EMA & CCI
This is another part of my research work, where I test a combination of two strategies, receiving a combined signal. In order to understand which indicator combinations work better, which work worse, as filters for trades. This is combo strategies for get a cumulative signal.
First strategy
This indicator plots 2/20 exponential moving average. For the Mov Avg X 2/20 Indicator, the EMA bar will be painted when the Alert criteria is met.
Second strategy
The Commodity Channel Index (CCI) is best used with markets that display cyclical or seasonal characteristics, and is formulated to detect the beginning and ending of the cycles by incorporating a moving average together with a divisor that reflects both possible and actual trading ranges. The final index measures the deviation from normal, which indicates major changes in market trend.
Strategy tester settings:
Initial capital: 1000
Order size: 0.5
Commission: 0.1%
Other as default.
Indicator settings:
EMA Length: 50
CCI Length: 10
Fast MA Length: 15
Slow MA Length: 20
Other as default.
WARNING:
- For purpose educate only
- This script to change bars colors.
RSI Trend Following StrategyOverview
The RSI Trend Following Strategy utilizes Relative Strength Index (RSI) to enter the trade for the potential trend continuation. It uses Stochastic indicator to check is the price is not in overbought territory and the MACD to measure the current price momentum. Moreover, it uses the 200-period EMA to filter the counter trend trades with the higher probability. The strategy opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Two layers trade filtering system: Strategy utilizes MACD and Stochastic indicators measure the current momentum and overbought condition and use 200-period EMA to filter trades against major trend.
Trailing take profit level: After reaching the trailing profit activation level script activates the trailing of long trade using EMA. More information in methodology.
Wide opportunities for strategy optimization: Flexible strategy settings allows users to optimize the strategy entries and exits for chosen trading pair and time frame.
Methodology
The strategy opens long trade when the following price met the conditions:
RSI is above 50 level.
MACD line shall be above the signal line
Both lines of Stochastic shall be not higher than 80 (overbought territory)
Candle’s low shall be above the 200 period EMA
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with trailing EMA(by default = 20 period). If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 1.75)
ATR Trailing Profit Activation Level (by default = 2.25)
MACD Fast Length (by default = 12, period of averaging fast MACD line)
MACD Fast Length (by default = 26, period of averaging slow MACD line)
MACD Signal Smoothing (by default = 9, period of smoothing MACD signal line)
Oscillator MA Type (by default = EMA, available options: SMA, EMA)
Signal Line MA Type (by default = EMA, available options: SMA, EMA)
RSI Length (by default = 14, period for RSI calculation)
Trailing EMA Length (by default = 20, period for EMA, which shall be broken close the trade after trailing profit activation)
Justification of Methodology
This trading strategy is designed to leverage a combination of technical indicators—Relative Strength Index (RSI), Moving Average Convergence Divergence (MACD), Stochastic Oscillator, and the 200-period Exponential Moving Average (EMA)—to determine optimal entry points for long trades. Additionally, the strategy uses the Average True Range (ATR) for dynamic risk management to adapt to varying market conditions. Let's look in details for which purpose each indicator is used for and why it is used in this combination.
Relative Strength Index (RSI) is a momentum indicator used in technical analysis to measure the speed and change of price movements in a financial market. It helps traders identify whether an asset is potentially overbought (overvalued) or oversold (undervalued), which can indicate a potential reversal or continuation of the current trend.
How RSI Works? RSI tracks the strength of recent price changes. It compares the average gains and losses over a specific period (usually 14 periods) to assess the momentum of an asset. Average gain is the average of all positive price changes over the chosen period. It reflects how much the price has typically increased during upward movements. Average loss is the average of all negative price changes over the same period. It reflects how much the price has typically decreased during downward movements.
RSI calculates these average gains and losses and compares them to create a value between 0 and 100. If the RSI value is above 70, the asset is generally considered overbought, meaning it might be due for a price correction or reversal downward. Conversely, if the RSI value is below 30, the asset is considered oversold, suggesting it could be poised for an upward reversal or recovery. RSI is a useful tool for traders to determine market conditions and make informed decisions about entering or exiting trades based on the perceived strength or weakness of an asset's price movements.
This strategy uses RSI as a short-term trend approximation. If RSI crosses over 50 it means that there is a high probability of short-term trend change from downtrend to uptrend. Therefore RSI above 50 is our first trend filter to look for a long position.
The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in an asset's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line = 12 period EMA − 26 period EMA
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
This strategy uses MACD as a second short-term trend filter. When MACD line crossed over the signal line there is a high probability that uptrend has been started. Therefore MACD line above signal line is our additional short-term trend filter. In conjunction with RSI it decreases probability of following false trend change signals.
The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
This strategy uses stochastic to define the overbought conditions. The logic here is the following: we want to avoid long trades in the overbought territory, because when indicator reaches it there is a high probability that the potential move is gonna be restricted.
The 200-period EMA is a widely recognized indicator for identifying the long-term trend direction. The strategy only trades in the direction of this primary trend to increase the probability of successful trades. For instance, when the price is above the 200 EMA, only long trades are considered, aligning with the overarching trend direction.
Therefore, strategy uses combination of RSI and MACD to increase the probability that price now is in short-term uptrend, Stochastic helps to avoid the trades in the overbought (>80) territory. To increase the probability of opening long trades in the direction of a main trend and avoid local bounces we use 200 period EMA.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -3.94%
Maximum Single Profit: +15.78%
Net Profit: +1359.21 USDT (+13.59%)
Total Trades: 111 (36.04% win rate)
Profit Factor: 1.413
Maximum Accumulated Loss: 625.02 USDT (-5.85%)
Average Profit per Trade: 12.25 USDT (+0.40%)
Average Trade Duration: 40 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe and chart (optimal performance observed on 2h BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
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)
Golden Cross Strategy with Trend FilterHere's the English translation:
**Entry for Long Position:** Enter a long position only when the 5SMA crosses above the 25SMA and the current price is above the 75SMA.
**Entry for Short Position:** Enter a short position only when the 5SMA crosses below the 25SMA and the current price is below the 75SMA.
**Exit Position:** Hold the long position until a short signal is generated, and hold the short position until a long signal is generated.
By using the 75SMA to confirm the trend direction and taking positions only in alignment with that trend, you can enhance trading accuracy and potentially improve the profit factor.
Rsi Long-Term Strategy [15min]Hello, I would like to present to you The "RSI Long-Term Strategy" for 15min tf
The "RSI Long-Term Strategy " is designed for traders who prefer a combination of momentum and trend-following techniques. The strategy focuses on entering long positions during significant market corrections within an overall uptrend, confirmed by both RSI and volume. The use of long-term SMAs ensures that trades are made in line with the broader market trend. The stop-loss feature provides risk management by limiting losses on trades that do not perform as expected. This strategy is particularly well-suited for longer-term traders who monitor 15-minute charts but look for substantial trend reversals or continuations.
Indicators and Parameters:
Relative Strength Index (RSI):
- The RSI is calculated using a 10-period length. It measures the magnitude of recent price changes to evaluate overbought or oversold conditions. The script defines oversold conditions when the RSI is at or below 30 and overbought conditions when the RSI is at or above 70.
Volume Condition:
-The strategy incorporates a volume condition where the current volume must be greater than 2.5 times the 20-period moving average of volume. This is used to confirm the strength of the price movement.
Simple Moving Averages (SMA):
- The strategy uses two SMAs: SMA1 with a length of 250 periods and SMA2 with a length of 500 periods. These SMAs help identify long-term trends and generate signals based on their crossover.
Strategy Logic:
Entry Logic:
A long position is initiated when all the following conditions are met:
The RSI indicates an oversold condition (RSI ≤ 30).
SMA1 is above SMA2, indicating an uptrend.
The volume condition is satisfied, confirming the strength of the signal.
Exit Logic:
The strategy closes the long position when SMA1 crosses under SMA2, signaling a potential end of the uptrend (a "Death Cross").
Stop-Loss:
A stop-loss is set at 5% below the entry price to manage risk and limit potential losses.
Buy and sell signals are highlighted with circles below or above bars:
Green Circle : Buy signal when RSI is oversold, SMA1 > SMA2, and the volume condition is met.
Red Circle : Sell signal when RSI is overbought, SMA1 < SMA2, and the volume condition is met.
Black Cross: "Death Cross" when SMA1 crosses under SMA2, indicating a potential bearish signal.
to determine the level of stop loss and target point I used a piece of code by RafaelZioni, here is the script from which a piece of code was taken
I hope the strategy will be helpful, as always, best regards and safe trades
;)
BTC 5 min SHBHilalimSB A Wedding Gift 🌙
What is HilalimSB🌙?
First of all, as mentioned in the title, HilalimSB is a wedding gift.
HilalimSB - Revealing the Secrets of the Trend
HilalimSB is a powerful indicator designed to help investors analyze market trends and optimize trading strategies. Designed to uncover the secrets at the heart of the trend, HilalimSB stands out with its unique features and impressive algorithm.
Hilalim Algorithm and Fixed ATR Value:
HilalimSB is equipped with a special algorithm called "Hilalim" to detect market trends. This algorithm can delve into the depths of price movements to determine the direction of the trend and provide users with the ability to predict future price movements. Additionally, HilalimSB uses its own fixed Average True Range (ATR) value. ATR is an indicator that measures price movement volatility and is often used to determine the strength of a trend. The fixed ATR value of HilalimSB has been tested over long periods and its reliability has been proven. This allows users to interpret the signals provided by the indicator more reliably.
ATR Calculation Steps
1.True Range Calculation:
+ The True Range (TR) is the greatest of the following three values:
1. Current high minus current low
2. Current high minus previous close (absolute value)
3. Current low minus previous close (absolute value)
2.Average True Range (ATR) Calculation:
-The initial ATR value is calculated as the average of the TR values over a specified period
(typically 14 periods).
-For subsequent periods, the ATR is calculated using the following formula:
ATRt=(ATRt−1×(n−1)+TRt)/n
Where:
+ ATRt is the ATR for the current period,
+ ATRt−1 is the ATR for the previous period,
+ TRt is the True Range for the current period,
+ n is the number of periods.
Pine Script to Calculate ATR with User-Defined Length and Multiplier
Here is the Pine Script code for calculating the ATR with user-defined X length and Y multiplier:
//@version=5
indicator("Custom ATR", overlay=false)
// User-defined inputs
X = input.int(14, minval=1, title="ATR Period (X)")
Y = input.float(1.0, title="ATR Multiplier (Y)")
// True Range calculation
TR1 = high - low
TR2 = math.abs(high - close )
TR3 = math.abs(low - close )
TR = math.max(TR1, math.max(TR2, TR3))
// ATR calculation
ATR = ta.rma(TR, X)
// Apply multiplier
customATR = ATR * Y
// Plot the ATR value
plot(customATR, title="Custom ATR", color=color.blue, linewidth=2)
This code can be added as a new Pine Script indicator in TradingView, allowing users to calculate and display the ATR on the chart according to their specified parameters.
HilalimSB's Distinction from Other ATR Indicators
HilalimSB emerges with its unique Average True Range (ATR) value, presenting itself to users. Equipped with a proprietary ATR algorithm, this indicator is released in a non-editable form for users. After meticulous testing across various instruments with predetermined period and multiplier values, it is made available for use.
ATR is acknowledged as a critical calculation tool in the financial sector. The ATR calculation process of HilalimSB is conducted as a result of various research efforts and concrete data-based computations. Therefore, the HilalimSB indicator is published with its proprietary ATR values, unavailable for modification.
The ATR period and multiplier values provided by HilalimSB constitute the fundamental logic of a trading strategy. This unique feature aids investors in making informed decisions.
Visual Aesthetics and Clear Charts:
HilalimSB provides a user-friendly interface with clear and impressive graphics. Trend changes are highlighted with vibrant colors and are visually easy to understand. You can choose colors based on eye comfort, allowing you to personalize your trading screen for a more enjoyable experience. While offering a flexible approach tailored to users' needs, HilalimSB also promises an aesthetic and professional experience.
Strong Signals and Buy/Sell Indicators:
After completing test operations, HilalimSB produces data at various time intervals. However, we would like to emphasize to users that based on our studies, it provides the best signals in 1-hour chart data. HilalimSB produces strong signals to identify trend reversals. Buy or sell points are clearly indicated, allowing users to develop and implement trading strategies based on these signals.
For example, let's imagine you wanted to open a position on BTC on 2023.11.02. You are aware that you need to calculate which of the buying or selling transactions would be more profitable. You need support from various indicators to open a position. Based on the analysis and calculations it has made from the data it contains, HilalimSB would have detected that the graph is more suitable for a selling position, and by producing a sell signal at the most ideal selling point at 08:00 on 2023.11.02 (UTC+3 Istanbul), it would have informed you of the direction the graph would follow, allowing you to benefit positively from a 2.56% decline.
Technology and Innovation:
HilalimSB aims to enhance the trading experience using the latest technology. With its innovative approach, it enables users to discover market opportunities and support their decisions. Thus, investors can make more informed and successful trades. Real-Time Data Analysis: HilalimSB analyzes market data in real-time and identifies updated trends instantly. This allows users to make more informed trading decisions by staying informed of the latest market developments. Continuous Update and Improvement: HilalimSB is constantly updated and improved. New features are added and existing ones are enhanced based on user feedback and market changes. Thus, HilalimSB always aims to provide the latest technology and the best user experience.
Social Order and Intrinsic Motivation:
Negative trends such as widespread illegal gambling and uncontrolled risk-taking can have adverse financial effects on society. The primary goal of HilalimSB is to counteract these negative trends by guiding and encouraging users with data-driven analysis and calculable investment systems. This allows investors to trade more consciously and safely.
What is BTC 5 min ☆SHB Strategy🌙?
BTC 5 min ☆SHB Strategy is a strategy supported by the HilalimSB algorithm created by the creator of HilalimSB. It automatically opens trades based on the data it receives, maintaining trades with its uniquely defined take profit and stop loss levels, and automatically closes trades when necessary. It stands out in the TradingView world with its unique take profit and stop loss markings. BTC 5 min ☆SHB Strategy is close to users' initiatives and is a strategy suitable for 5-minute trades and scalp operations developed on BTC.
What does the BTC 5 min ☆SHB Strategy target?
The primary goal of BTC 5 min ☆SHB Strategy is to close trades made by traders in short timeframes as profitably as possible and to determine the most effective trading points in low time periods, considering the commission rates of various brokerage firms. BTC 5 min ☆SHB Strategy is one of the rare profitable strategies released in short timeframes, with its useful interface, in addition to existing strategies in the markets. After extensive backtesting over a long period and achieving above-average success, BTC 5 min ☆SHB Strategy was decided to be released. Following the completion of test procedures under market conditions, it was presented to users with the unique visual effects of ☆SB.
BTC 5 min ☆SHB Strategy and Heikin Ashi
BTC 5 min ☆SHB Strategy produces data in Heikin-Ashi chart types, but since Heikin-Ashi chart types have their own calculation method, BTC 5 min ☆SHB Strategy has been published in a way that cannot produce data in this chart type due to BTC 5 min ☆SHB Strategy's ideology of appealing to all types of users, and any confusion that may arise is prevented in this way. Heikin-Ashi chart types, especially in short time intervals, carry significant risks considering the unique calculation methods involved. Thus, the possibility of being misled by the coder and causing financial losses has been completely eliminated. After the necessary conditions determined by the creator of BTC 5 min ☆SHB are met, BTC 5 min ☆SHB Heikin-Ashi will be shared exclusively with invited users only, upon request, to users who request an invitation.
Key Features:
+HilalimSHB Algorithm: This algorithm uses a dynamic ATR-based trend-following mechanism to identify the current market trend. The strategy detects trend reversals and takes positions accordingly.
+Heikin Ashi Compatibility: The strategy is optimized to work only with standard candlestick charts and automatically deactivates when Heikin Ashi charts are in use, preventing false signals.
+Advanced Chart Enhancements: The strategy offers clear graphical markers for buy/sell signals. Candlesticks are automatically colored based on trend direction, making market trends easier to follow.
Strategy Parameters:
+Take Profit (%): Defines the target price level for closing a position and automates profit-taking. The fixed value is set at 2%.
+Stop Loss (%): Specifies the stop-loss level to limit losses. The fixed value is set at 3%.
The shared image is a 5-minute chart of BTCUSDC.P with a fixed take profit value of 2% and a fixed stop loss value of 3%. The trades are opened with a commission rate of 0.063% set for the USDT trading pair on Binance.🌙
Multi-Factor StrategyThis trading strategy combines multiple technical indicators to create a systematic approach for entering and exiting trades. The goal is to capture trends by aligning several key indicators to confirm the direction and strength of a potential trade. Below is a detailed description of how the strategy works:
Indicators Used
MACD (Moving Average Convergence Divergence):
MACD Line: The difference between the 12-period and 26-period Exponential Moving Averages (EMAs).
Signal Line: A 9-period EMA of the MACD line.
Usage: The strategy looks for crossovers between the MACD line and the Signal line as entry signals. A bullish crossover (MACD line crossing above the Signal line) indicates a potential upward movement, while a bearish crossover (MACD line crossing below the Signal line) signals a potential downward movement.
RSI (Relative Strength Index):
Usage: RSI is used to gauge the momentum of the price movement. The strategy uses specific thresholds: below 70 for long positions to avoid overbought conditions and above 30 for short positions to avoid oversold conditions.
ATR (Average True Range):
Usage: ATR measures market volatility and is used to set dynamic stop-loss and take-profit levels. A stop loss is set at 2 times the ATR, and a take profit at 3 times the ATR, ensuring that risk is managed relative to market conditions.
Simple Moving Averages (SMA):
50-day SMA: A short-term trend indicator.
200-day SMA: A long-term trend indicator.
Usage: The strategy uses the relationship between the 50-day and 200-day SMAs to determine the overall market trend. Long positions are taken when the price is above the 50-day SMA and the 50-day SMA is above the 200-day SMA, indicating an uptrend. Conversely, short positions are taken when the price is below the 50-day SMA and the 50-day SMA is below the 200-day SMA, indicating a downtrend.
Entry Conditions
Long Position:
-MACD Crossover: The MACD line crosses above the Signal line.
-RSI Confirmation: RSI is below 70, ensuring the asset is not overbought.
-SMA Confirmation: The price is above the 50-day SMA, and the 50-day SMA is above the 200-day SMA, indicating a strong uptrend.
Short Position:
MACD Crossunder: The MACD line crosses below the Signal line.
RSI Confirmation: RSI is above 30, ensuring the asset is not oversold.
SMA Confirmation: The price is below the 50-day SMA, and the 50-day SMA is below the 200-day SMA, indicating a strong downtrend.
Opposite conditions for shorts
Exit Strategy
Stop Loss: Set at 2 times the ATR from the entry price. This dynamically adjusts to market volatility, allowing for wider stops in volatile markets and tighter stops in calmer markets.
Take Profit: Set at 3 times the ATR from the entry price. This ensures a favorable risk-reward ratio of 1:1.5, aiming for higher rewards on successful trades.
Visualization
SMAs: The 50-day and 200-day SMAs are plotted on the chart to visualize the trend direction.
MACD Crossovers: Bullish and bearish MACD crossovers are highlighted on the chart to identify potential entry points.
Summary
This strategy is designed to align multiple indicators to increase the probability of successful trades by confirming trends and momentum before entering a position. It systematically manages risk with ATR-based stop loss and take profit levels, ensuring that trades are exited based on market conditions rather than arbitrary points. The combination of trend indicators (SMAs) with momentum and volatility indicators (MACD, RSI, ATR) creates a robust approach to trading in various market environments.
RSI Strategy with Adjustable RSI and Stop-LossThis trading strategy uses the Relative Strength Index (RSI) and a Stop-Loss mechanism to make trading decisions. Here’s a breakdown of how it works:
RSI Calculation:
The RSI is calculated based on the user-defined length (rsi_length). This is a momentum oscillator that measures the speed and change of price movements.
Buy Condition:
The strategy generates a buy signal when the RSI value is below a user-defined threshold (rsi_threshold). This condition indicates that the asset might be oversold and potentially due for a rebound.
Stop-Loss Mechanism:
Upon triggering a buy signal, the strategy calculates the Stop-Loss level. The Stop-Loss level is set to a percentage below the entry price, as specified by the user (stop_loss_percent). This level is used to limit potential losses if the price moves against the trade.
Sell Condition:
A sell signal is generated when the current closing price is higher than the highest high of the previous day. This condition suggests that the price has reached a new high, and the strategy decides to exit the trade.
Plotting:
The RSI values are plotted on the chart for visual reference. A horizontal line is drawn at the RSI threshold level to help visualize the oversold condition.
Summary
Buying Strategy: When RSI is below the specified threshold, indicating potential oversold conditions.
Stop-Loss: Set based on a percentage of the entry price to limit potential losses.
Selling Strategy: When the price surpasses the highest high of the previous day, signaling a potential exit point.
This strategy aims to capture potential rebounds from oversold conditions and manage risk using a Stop-Loss mechanism. As with any trading strategy, it’s essential to test and optimize it under various market conditions to ensure its effectiveness.
MACD with 1D Stochastic Confirmation Reversal StrategyOverview
The MACD with 1D Stochastic Confirmation Reversal Strategy utilizes MACD indicator in conjunction with 1 day timeframe Stochastic indicators to obtain the high probability short-term trend reversal signals. The main idea is to wait until MACD line crosses up it’s signal line, at the same time Stochastic indicator on 1D time frame shall show the uptrend (will be discussed in methodology) and not to be in the oversold territory. Strategy works on time frames from 30 min to 4 hours and opens only long trades.
Unique Features
Dynamic stop-loss system: Instead of fixed stop-loss level strategy utilizes average true range (ATR) multiplied by user given number subtracted from the position entry price as a dynamic stop loss level.
Configurable Trading Periods: Users can tailor the strategy to specific market windows, adapting to different market conditions.
Higher time frame confirmation: Strategy utilizes 1D Stochastic to establish the major trend and confirm the local reversals with the higher probability.
Trailing take profit level: After reaching the trailing profit activation level scrip activate the trailing of long trade using EMA. More information in methodology.
Methodology
The strategy opens long trade when the following price met the conditions:
MACD line of MACD indicator shall cross over the signal line of MACD indicator.
1D time frame Stochastic’s K line shall be above the D line.
1D time frame Stochastic’s K line value shall be below 80 (not overbought)
When long trade is executed, strategy set the stop-loss level at the price ATR multiplied by user-given value below the entry price. This level is recalculated on every next candle close, adjusting to the current market volatility.
At the same time strategy set up the trailing stop validation level. When the price crosses the level equals entry price plus ATR multiplied by user-given value script starts to trail the price with EMA. If price closes below EMA long trade is closed. When the trailing starts, script prints the label “Trailing Activated”.
Strategy settings
In the inputs window user can setup the following strategy settings:
ATR Stop Loss (by default = 3.25, value multiplied by ATR to be subtracted from position entry price to setup stop loss)
ATR Trailing Profit Activation Level (by default = 4.25, value multiplied by ATR to be added to position entry price to setup trailing profit activation level)
Trailing EMA Length (by default = 20, period for EMA, when price reached trailing profit activation level EMA will stop out of position if price closes below it)
User can choose the optimal parameters during backtesting on certain price chart, in our example we use default settings.
Justification of Methodology
This strategy leverages 2 time frames analysis to have the high probability reversal setups on lower time frame in the direction of the 1D time frame trend. That’s why it’s recommended to use this strategy on 30 min – 4 hours time frames.
To have an approximation of 1D time frame trend strategy utilizes classical Stochastic indicator. The Stochastic Indicator is a momentum oscillator that compares a security's closing price to its price range over a specific period. It's used to identify overbought and oversold conditions. The indicator ranges from 0 to 100, with readings above 80 indicating overbought conditions and readings below 20 indicating oversold conditions.
It consists of two lines:
%K: The main line, calculated using the formula (CurrentClose−LowestLow)/(HighestHigh−LowestLow)×100 . Highest and lowest price taken for 14 periods.
%D: A smoothed moving average of %K, often used as a signal line.
Strategy logic assumes that on 1D time frame it’s uptrend in %K line is above the %D line. Moreover, we can consider long trade only in %K line is below 80. It means that in overbought state the long trade will not be opened due to higher probability of pullback or even major trend reversal. If these conditions are met we are going to our working (lower) time frame.
On the chosen time frame, we remind you that for correct work of this strategy you shall use 30min – 4h time frames, MACD line shall cross over it’s signal line. The MACD (Moving Average Convergence Divergence) is a popular momentum and trend-following indicator used in technical analysis. It helps traders identify changes in the strength, direction, momentum, and duration of a trend in a stock's price.
The MACD consists of three components:
MACD Line: This is the difference between a short-term Exponential Moving Average (EMA) and a long-term EMA, typically calculated as: MACD Line=12-period EMA−26-period
Signal Line: This is a 9-period EMA of the MACD Line, which helps to identify buy or sell signals. When the MACD Line crosses above the Signal Line, it can be a bullish signal (suggesting a buy); when it crosses below, it can be a bearish signal (suggesting a sell).
Histogram: The histogram shows the difference between the MACD Line and the Signal Line, visually representing the momentum of the trend. Positive histogram values indicate increasing bullish momentum, while negative values indicate increasing bearish momentum.
In our script we are interested in only MACD and signal lines. When MACD line crosses signal line there is a high chance that short-term trend reversed to the upside. We use this strategy on 45 min time frame.
ATR is used to adjust the strategy risk management to the current market volatility. If volatility is low, we don’t need the large stop loss to understand the there is a high probability that we made a mistake opening the trade. User can setup the settings ATR Stop Loss and ATR Trailing Profit Activation Level to realize his own risk to reward preferences, but the unique feature of a strategy is that after reaching trailing profit activation level strategy is trying to follow the trend until it is likely to be finished instead of using fixed risk management settings. It allows sometimes to be involved in the large movements.
Backtest Results
Operating window: Date range of backtests is 2023.01.01 - 2024.08.01. It is chosen to let the strategy to close all opened positions.
Commission and Slippage: Includes a standard Binance commission of 0.1% and accounts for possible slippage over 5 ticks.
Initial capital: 10000 USDT
Percent of capital used in every trade: 30%
Maximum Single Position Loss: -4.79%
Maximum Single Profit: +20.14%
Net Profit: +2361.33 USDT (+44.72%)
Total Trades: 123 (44.72% win rate)
Profit Factor: 1.623
Maximum Accumulated Loss: 695.80 USDT (-5.48%)
Average Profit per Trade: 19.20 USDT (+0.59%)
Average Trade Duration: 30 hours
These results are obtained with realistic parameters representing trading conditions observed at major exchanges such as Binance and with realistic trading portfolio usage parameters.
How to Use
Add the script to favorites for easy access.
Apply to the desired timeframe between 30 min and 4 hours and chart (optimal performance observed on 45 min BTC/USDT).
Configure settings using the dropdown choice list in the built-in menu.
Set up alerts to automate strategy positions through web hook with the text: {{strategy.order.alert_message}}
Disclaimer:
Educational and informational tool reflecting Skyrex commitment to informed trading. Past performance does not guarantee future results. Test strategies in a simulated environment before live implementation
Self Optimizing RSI and Self Adaptive TP/SL [Starbots]Self Optimizing RSI and Self Adaptive TP/SL Strategy. (non-repainting)
This script continuously backtests 20 different combinations of RSI Buy conditions across 5 different Take Profit/Stop Loss combinations. In total, it tests 100 variants on every bar close and records the Net Profit gained for each combination. The strategy then selects and uses the best-performing combination of settings currently available for you to trade.
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The Relative Strength Index (RSI), developed by J. Welles Wilder, is a momentum oscillator that measures the speed and change of price movements. The RSI oscillates between zero and 100. Traditionally the RSI is considered overbought when above 70 and oversold when below 30. Signals can be generated by looking for divergences and failure swings. RSI can also be used to identify the general trend.
To improve our results we are calculating Multiple Length RSI - Average RSI based on the multiple periods. You can use just 1 Length or Multiple.
Set Inputs to Min=14, Max=14 if you want to use just 1 period.
= RSI(14)
3 RSI Lengths example (12,13 and 14):
Min=12, Max=14
(12+13+14) / 3 = avg. RSI
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Backtester - Optimizer Explained:
The backtester runs numerous backtests in the background to optimize trading strategies. Here’s how it works:
Default Inputs (Combinations of TP/SL)
TP 1%, SL4%
TP 2%, SL4%
TP 3%, SL4%
TP 2%, SL5%
TP 4.5%, SL10%
Default Inputs (RSI Crossover Buys) :
18 ,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,45,55, 69
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Backtest RSI Crossover 18:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
,...
,...
Backtest RSI Crossover 69:
TP1%, SL4% => Save net profit
TP2%, SL4% => Save net profit
TP3%, SL4% => Save net profit
TP2%, SL5% => Save net profit
TP4.5%, SL10% => Save net profit
Self Optimizing Buy Condition and Self Optimizing Take Profit - Stop Los
This process involves testing various combinations of RSI crossover values with different Take Profit (TP) and Stop Loss (SL) percentages. The net profit for each combination is saved, allowing the optimizer to select the best-performing settings for trading.
It recalculates on every bar close. If one combination starts performing better than others—achieving a higher net profit gain (essentially like running 100 backtests with different settings in the background)—the strategy switches to that combination of TP/SL and Buy condition. It continues trading with the new settings until another parameter starts performing better and the strategy switches to that setting.
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If you wish to use it as INDICATOR - turn on 'Recalculate - On every tick' in Properties tab to have this script updating constantly and use it as a normal Indicator tool for manual trading.
Other functions:
Set the %fee for optimizing engine. If you set this % higher, you also punish small average trades and make the strategy prefer larger avg. trades, giving you better chances to make your strategy profitable.
Trade with trend and optimize the strategy only when the market is uptrending with EMA/HMA
Use Moving Average of avg.RSI and smooth the values for indicator even more. (Yes strategy is self optimizing RSI or avg.RSI or RSI-MA, you can select all sorts of this indicator for optimizing)
All trading alerts are working and functional, if you want to automate the strategy
This script is simple to use for any trader as it saves a lot of time for searching good parameters on your own. It's self-optimizing and adjusting to the markets on the go.
TradeBuilderOverview
TradeBuilder is an ever-growing toolbox that lets you combine and compound any number of bundled indicators and algorithms to create a compound strategy. At launch, we're including two Moving Averages (SMA, EMA), RSI, and Stochastic Oscillator, with many more to come. You can use any combination of indicators, be it just one, two, or all.
Key Concepts
Indicator Integration: Tradebuilder allows the use of Moving Averages, RSI, and Stochastic Oscillators, with customizable parameters for each. More indicators to come.
Mode Selection : Choose between Confirm Trend Mode (using indicators to confirm trends) and Momentum Mode (using indicators to spot reversals).
Trade Flexibility : Offers options for both long and short trades, enabling diverse trading strategies.
Customizable Inputs : Easily toggle indicators on or off and adjust specific settings like periods and thresholds.
Signal Generation : Combines multiple conditions to generate entry and exit signals.
Input Parameters:
Moving Average (MA):
use_ma : Enable this to include the Moving Average in your strategy.
ma_cross_type : Choose between "Close/MA" (price crossing the MA) or "MA/MA" (one MA crossing another).
ma_length : Set the period for the primary MA.
ma_type : Choose between "SMA" (Simple Moving Average) or "EMA" (Exponential Moving Average).
ma_length2 : Set the period for the secondary MA if using the "MA/MA" cross type.
ma_type2 : Set the type for the secondary MA.
Relative Strength Index (RSI):
use_rsi : Enable this to include RSI in your strategy.
rsi_length : Set the period for RSI calculation.
rsi_overbought : Define the overbought level.
rsi_oversold : Define the oversold level.
Stochastic Oscillator:
use_stoch : Enable this to include the Stochastic Oscillator in your strategy.
stoch_k : Set the %K period.
stoch_d : Set the %D period.
stoch_smooth : Define the smoothing factor.
stoch_overbought : Set the overbought level.
stoch_oversold : Set the oversold level.
Confirmation or Momentum Mode:
confirm_trend : Set this to true to use RSI and Stochastic Oscillator to confirm trends (long when above overbought, short when below oversold). Set to false to trade on momentum (short when above overbought, long when below oversold).
Tip: When set to false and used with just momentum oscillators like Stochastic or RSI, it's geared toward scalping as it essentially becomes momentum trading.
Trade Directions:
trade_long : Enable to allow long trades.
trade_short : Enable to allow short trades.
Example Strategy on E-mini S&P 500 Index Futures ( CME_MINI:ES1! ), 1-minute Chart
Let’s say you want to create a strategy to go long when:
A 5-period SMA crosses above a 100-period EMA.
RSI is above 20.
The Stochastic Oscillator is above 95.
Trend Confirmation Mode is on.
For short:
A 5-period SMA crosses below a 100-period EMA.
RSI is below 45.
The Stochastic Oscillator is below 5.
Trend Confirmation Mode is on.
Here’s how you would set it up in Tradebuilder:
use_ma = true
ma_cross_type = "MA/MA"
ma_length = 5
ma_type = "SMA"
ma_length2 = 100
ma_type2 = "EMA"
use_rsi = true
rsi_length = 14
rsi_overbought = 20
rsi_oversold = 45
use_stoch = true
stoch_k = 8
stoch_d = 1
stoch_smooth = 1
stoch_overbought = 95
stoch_oversold = 5
confirm_trend = true
trade_long = true
trade_short = false
Alerts
Here is how to set TradeBuilder alerts: open a TradingView chart, attach TradeBuilder, right-click on chart -> Add Alert. Condition: Symbol (e.g. NQ) >> TradeBuilder >> Open-Ended Alert >> Once Per Bar Close.
Development Roadmap
We plan to add many more compoundable indicators to TradeBuilder over the coming months from all walks of technical analysis, including Volume, Volatility, Trend Detection/Validation, Momentum, Divergences, Chart Patterns, Support/Resistance Analysis. etc.
Chandelier Exit Strategy with 200 EMA FilterStrategy Name and Purpose
Chandelier Exit Strategy with 200EMA Filter
This strategy uses the Chandelier Exit indicator in combination with a 200-period Exponential Moving Average (EMA) to generate trend-based trading signals. The main purpose of this strategy is to help traders identify high-probability entry points by leveraging the Chandelier Exit for stop loss levels and the EMA for trend confirmation. This strategy aims to provide clear rules for entries and exits, improving overall trading discipline and performance.
Originality and Usefulness
This script integrates two powerful indicators to create a cohesive and effective trading strategy:
Chandelier Exit : This indicator is based on the Average True Range (ATR) and identifies potential stop loss levels. The Chandelier Exit helps manage risk by setting stop loss levels at a distance from the highest high or lowest low over a specified period, multiplied by the ATR. This ensures that the stop loss adapts to market volatility.
200-period Exponential Moving Average (EMA) : The EMA acts as a trend filter. By ensuring trades are only taken in the direction of the overall trend, the strategy improves the probability of success. For long entries, the close price must be above the 200 EMA, indicating a bullish trend. For short entries, the close price must be below the 200 EMA, indicating a bearish trend.
Combining these indicators adds layers of confirmation and risk management, enhancing the strategy's effectiveness. The Chandelier Exit provides dynamic stop loss levels based on market volatility, while the EMA ensures trades align with the prevailing trend.
Entry Conditions
Long Entry
A buy signal is generated by the Chandelier Exit.
The close price is above the 200 EMA, indicating a strong bullish trend.
Short Entry
A sell signal is generated by the Chandelier Exit.
The close price is below the 200 EMA, indicating a strong bearish trend.
Exit Conditions
For long positions: The position is closed when a sell signal is generated by the Chandelier Exit.
For short positions: The position is closed when a buy signal is generated by the Chandelier Exit.
Risk Management
Account Size: 1,000,00 yen
Commission and Slippage: 17 pips commission and 1 pip slippage per trade
Risk per Trade: 10% of account equity
Stop Loss: For long trades, the stop loss is placed slightly below the candle that generated the buy signal. For short trades, the stop loss is placed slightly above the candle that generated the sell signal. The stop loss levels are dynamically adjusted based on the ATR.
Settings Options
ATR Period: Set the period for calculating the ATR to determine the Chandelier Exit levels.
ATR Multiplier: Set the multiplier for ATR to define the distance of stop loss levels from the highest high or lowest low.
Use Close Price for Extremums: Choose whether to use the close price for calculating the extremums.
EMA Period: Set the period for the EMA to adjust the trend filter sensitivity.
Show Buy/Sell Labels: Choose whether to display buy and sell labels on the chart for visual confirmation.
Highlight State: Choose whether to highlight the bullish or bearish state on the chart.
Sufficient Sample Size
The strategy has been backtested with a sufficient sample size to evaluate its performance accurately. This ensures that the strategy's results are statistically significant and reliable.
Notes
This strategy is based on historical data and does not guarantee future results.
Thoroughly backtest and validate results before using in live trading.
Market volatility and other external factors can affect performance and may not yield expected results.
Acknowledgment
This strategy uses the Chandelier Exit indicator. Special thanks to the original contributors for their work on the Chandelier Exit concept.
Clean Chart Explanation
The script is published with a clean chart to ensure that its output is readily identifiable and easy to understand. No other scripts are included on the chart, and any drawings or images used are specifically to illustrate how the script works.
RSI Buy-30d Cooldown-AHR999亲爱的数字资产投资者，您是否在寻找一种智能、可靠的方式来积累您的投资组合？我们为您带来了一个革命性的交易策略！
🚀 引入"智慧积累者"策略 🚀
这是一个为长期数字资产投资者量身定制的智能买入策略。它能帮您在最佳时机买入，让您的投资组合稳步增长！
✨ 主要特点：
智能时机选择：结合RSI和创新的AHR999指标，精准捕捉买入机会。
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底部猎手：专注于市场低迷期，为您寻找最佳入场点。
灵活可定制：根据您的风险偏好，轻松调整各项参数。
可视化决策：直观的图表标记，让您清晰了解每次交易背后的逻辑。
💡 它是如何工作的？
当市场情绪低落（低RSI）且资产被低估（低AHR999）时，策略会自动为您买入。
每次买入固定金额，帮您实现美元成本平均。
智能冷却期确保您不会在短期内过度买入。
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随时查看您的总投资额、持有的资产数量和平均买入成本。
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长期价值：专注于积累，为未来做准备。
市场洞察：通过AHR999指标，深入了解市场周期。
无论您是经验丰富的投资者，还是刚开始接触数字资产，"智慧积累者"策略都能为您提供一种智能、低风险的方式来增加您的持仓。
准备好开始您的智能积累之旅了吗？立即尝试"智慧积累者"策略，让您的投资更上一层楼！
🚀 智能、安全、高效 - 您的数字资产积累好帮手！🚀
Strategy SEMA SDI WebhookPurpose of the Code:
The strategy utilizes Exponential Moving Averages (EMA) and Smoothed Directional Indicators (SDI) to generate buy and sell signals. It includes features like leverage, take profit, stop loss, and trailing stops. The strategy is intended for backtesting and automating trades based on the specified indicators and conditions.
Key Components and Functionalities:
1.Strategy Settings:
Overlay: The strategy will overlay on the price chart.
Slippage: Set to 1.
Commission Value: Set to 0.035.
Default Quantity Type: Percent of equity.
Default Quantity Value: 50% of equity.
Initial Capital: Set to 1000 units.
Calculation on Order Fills: Enabled.
Process Orders on Close: Enabled.
2.Date and Time Filters:
Inputs for enabling/disabling start and end dates.
Filters to execute strategy only within specified date range.
3.Leverage and Quantity:
Leverage: Adjustable leverage input (default 3).
USD Percentage: Adjustable percentage of equity to use for trades (default 50%).
Initial Capital: Calculated based on leverage and percentage of equity.
4.Take Profit, Stop Loss, and Trailing Stop:
Inputs for enabling/disabling take profit, stop loss, and trailing stop.
Adjustable parameters for take profit percentage (default 25%), stop loss percentage (default 4.8%), and trailing stop percentage (default 1.9%).
Calculations for take profit, stop loss, trailing price, and maximum profit tracking.
5.EMA Calculations:
Fast and slow EMAs.
Smoothed versions of the fast and slow EMAs.
6.SDI Calculations:
Directional movement calculation for positive and negative directional indicators.
Difference between the positive and negative directional indicators, smoothed.
7.Buy/Sell Conditions:
Long (Buy) Condition: Positive DI is greater than negative DI, and fast EMA is greater than slow EMA.
Short (Sell) Condition: Negative DI is greater than positive DI, and fast EMA is less than slow EMA.
8.Strategy Execution:
If buy conditions are met, close any short positions and enter a long position.
If sell conditions are met, close any long positions and enter a short position.
Exit conditions for long and short positions based on take profit, stop loss, and trailing stop levels.
Close all positions if outside the specified date range.
Usage:
This strategy is used to automate trading based on the specified conditions involving EMAs and SDI. It allows backtesting to evaluate performance based on historical data. The strategy includes risk management through take profit, stop loss, and trailing stops to protect gains and limit losses. Traders can customize the parameters to fit their specific trading preferences and risk tolerance. Differently, it can perform leverage analysis and use it as a template.
By using this strategy, traders can systematically execute trades based on technical indicators, helping to remove emotional bias and improve consistency in trading decisions.
Important Note:
This script is provided for educational and template purposes and does not constitute financial advice. Traders and investors should conduct their research and analysis before making any trading decisions.
TSI w SuperTrend decision - Strategy [presentTrading]This strategy aims to improve upon the performance of Traidngview's newly published "Trend Strength Index" indicator by incorporating the SuperTrend for better trade execution and risk management. Enjoy :)
█ Introduction and How it is Different
The "TSI with SuperTrend Decision - Strategy" combines the Trend Strength Index (TSI) with SuperTrend indicators to determine entry and exit points. Unlike traditional strategies that rely solely on one indicator, this method leverages the strengths of both TSI and SuperTrend to provide a more nuanced and adaptive trading strategy.
This dual approach allows for capturing trends more effectively, especially in volatile markets.
BTCUSD 8h LS Performance
█ Strategy, How it Works: Detailed Explanation
🔶 Trend Strength Index (TSI)
The TSI is a momentum oscillator that shows both the direction and strength of a trend. It is calculated by comparing the price movement with the bar index over a specified period. The formula for TSI is as follows:
```
TSI = (PC / |PC|)
where:
PC = Change in price over the period
```
In this strategy, TSI is calculated using the closing prices and a default period of 64 bars. The TSI values help identify overbought and oversold conditions, providing signals for potential market reversals.
🔶 SuperTrend Indicator
The SuperTrend is a trend-following indicator based on the average true range (ATR). It helps in identifying the direction of the market trend. The SuperTrend calculation involves:
```
SuperTrend = HLC3 ± (Factor * ATR)
where:
HLC3 = (High + Low + Close) / 3
Factor = User-defined multiplier
ATR = Average True Range over a period
```
The SuperTrend settings in this strategy include a length of 10 bars and a factor of 3.0.
Last Bull Cycle of BTC
🔶 Entry and Exit Conditions
The strategy uses the TSI and SuperTrend together to determine entry and exit points:
- Long Entry: When the SuperTrend indicates a downward trend (st.d < 0) and the TSI is above the oversold level (-0.241).
- Long Exit: When the SuperTrend indicates an upward trend (st.d > 0) and the TSI is below the overbought level (0.241).
- Short Entry: When the SuperTrend indicates an upward trend (st.d > 0) and the TSI is below the overbought level (0.241).
- Short Exit: When the SuperTrend indicates a downward trend (st.d < 0) and the TSI is above the oversold level (-0.241).
█ Trade Direction
The strategy allows users to select the trade direction through the `tradeDirection` input. The options are:
- Both: Enables both long and short trades.
- Long: Enables only long trades.
- Short: Enables only short trades.
█ Default Settings
- TSI Length: 64
- SuperTrend Length: 10
- SuperTrend Factor: 3.0
- Trade Direction: Both
- Take Profit (%): 30.0
- Stop Loss (%): 20.0
Impact of Default Settings
- TSI Length: A longer TSI period smooths out noise but may lag in identifying trends. A shorter period is more responsive but can generate false signals.
- SuperTrend Length: A shorter length provides quicker signals but can be prone to whipsaws. A longer length is more reliable but may delay entries and exits.
- SuperTrend Factor: A higher factor increases the distance of the SuperTrend from the price, reducing sensitivity to minor price fluctuations.
- Trade Direction: Allows flexibility in trading strategies by enabling both long and short trades based on market conditions.
- Take Profit and Stop Loss: These settings manage risk by automatically closing trades at predefined profit or loss levels. Higher percentages provide larger potential gains but also higher risk.
HMA Crossover 1H with RSI, Stochastic RSI, and Trailing StopThe strategy script provided is a trading algorithm designed to help traders make informed buy and sell decisions based on certain technical indicators. Here’s a breakdown of what each part of the script does and how the strategy works:
Key Components:
Hull Moving Averages (HMA):
HMA 5: This is a Hull Moving Average calculated over 5 periods. HMAs are used to smooth out price data and identify trends more quickly than traditional moving averages.
HMA 20: This is another HMA but calculated over 20 periods, providing a broader view of the trend.
Relative Strength Index (RSI):
RSI 14: This is a momentum oscillator that measures the speed and change of price movements over a 14-period timeframe. It helps identify overbought or oversold conditions in the market.
Stochastic RSI:
%K: This is the main line of the Stochastic RSI, which combines the RSI and the Stochastic Oscillator to provide a more sensitive measure of overbought and oversold conditions. It is smoothed with a 3-period simple moving average.
Trading Signals:
Buy Signal:
Generated when the 5-period HMA crosses above the 20-period HMA, indicating a potential upward trend.
Additionally, the RSI must be below 45, suggesting that the market is not overbought.
The Stochastic RSI %K must also be below 39, confirming the oversold condition.
Sell Signal:
Generated when the 5-period HMA crosses below the 20-period HMA, indicating a potential downward trend.
The RSI must be above 60, suggesting that the market is not oversold.
The Stochastic RSI %K must also be above 63, confirming the overbought condition.
Trailing Stop Loss:
This feature helps protect profits by automatically selling the position if the price moves against the trade by 5%.
For sell positions, an additional trailing stop of 100 points is included.
Advanced Gold Scalping Strategy with RSI Divergence# Advanced Gold Scalping Strategy with RSI Divergence
## Overview
This Pine Script implements an advanced scalping strategy for gold (XAUUSD) trading, primarily designed for the 1-minute timeframe. The strategy utilizes the Relative Strength Index (RSI) indicator along with its moving average to identify potential trade setups based on divergences between price action and RSI movements.
## Key Components
### 1. RSI Calculation
- Uses a customizable RSI length (default: 60)
- Allows selection of the source for RSI calculation (default: close price)
### 2. Moving Average of RSI
- Supports multiple MA types: SMA, EMA, SMMA (RMA), WMA, VWMA, and Bollinger Bands
- Customizable MA length (default: 3)
- Option to display Bollinger Bands with adjustable standard deviation multiplier
### 3. Divergence Detection
- Implements both bullish and bearish divergence identification
- Uses pivot high and pivot low points to detect divergences
- Allows for customization of lookback periods and range for divergence detection
### 4. Entry Conditions
- Long Entry: Bullish divergence when RSI is below 40
- Short Entry: Bearish divergence when RSI is above 60
### 5. Trade Management
- Stop Loss: Customizable, default set to 11 pips
- Take Profit: Customizable, default set to 33 pips
### 6. Visualization
- Plots RSI line and its moving average
- Displays horizontal lines at 30, 50, and 70 RSI levels
- Shows Bollinger Bands when selected
- Highlights divergences with "Bull" and "Bear" labels on the chart
## Input Parameters
- RSI Length: Adjusts the period for RSI calculation
- RSI Source: Selects the price source for RSI (close, open, high, low, hl2, hlc3, ohlc4)
- MA Type: Chooses the type of moving average applied to RSI
- MA Length: Sets the period for the moving average
- BB StdDev: Adjusts the standard deviation multiplier for Bollinger Bands
- Show Divergence: Toggles the display of divergence labels
- Stop Loss: Sets the stop loss distance in pips
- Take Profit: Sets the take profit distance in pips
## Strategy Logic
1. **RSI Calculation**:
- Computes RSI using the specified length and source
- Calculates the chosen type of moving average on the RSI
2. **Divergence Detection**:
- Identifies pivot points in both price and RSI
- Checks for higher lows in RSI with lower lows in price (bullish divergence)
- Checks for lower highs in RSI with higher highs in price (bearish divergence)
3. **Trade Entry**:
- Enters a long position when a bullish divergence is detected and RSI is below 40
- Enters a short position when a bearish divergence is detected and RSI is above 60
4. **Position Management**:
- Places a stop loss order at the entry price ± stop loss pips (depending on the direction)
- Sets a take profit order at the entry price ± take profit pips (depending on the direction)
5. **Visualization**:
- Plots the RSI and its moving average
- Draws horizontal lines for overbought/oversold levels
- Displays Bollinger Bands if selected
- Shows divergence labels on the chart for identified setups
## Usage Instructions
1. Apply the script to a 1-minute XAUUSD (Gold) chart in TradingView
2. Adjust the input parameters as needed:
- Increase RSI Length for less frequent but potentially more reliable signals
- Modify MA Type and Length to change the sensitivity of the RSI moving average
- Adjust Stop Loss and Take Profit levels based on current market volatility
3. Monitor the chart for Bull (long) and Bear (short) labels indicating potential trade setups
4. Use in conjunction with other analysis and risk management techniques
## Considerations
- This strategy is designed for short-term scalping and may not be suitable for all market conditions
- Always backtest and forward test the strategy before using it with real capital
- The effectiveness of divergence-based strategies can vary depending on market trends and volatility
- Consider using additional confirmation signals or filters to improve the strategy's performance
Remember to adapt the strategy parameters to your risk tolerance and trading style, and always practice proper risk management.
Chande Momentum Oscillator (CMO) Buy Sell Strategy [TradeDots]The "Chande Momentum Oscillator (CMO) Buy Sell Strategy" leverages the CMO indicator to identify short-term buy and sell opportunities.
HOW DOES IT WORK
The standard CMO indicator measures the difference between recent gains and losses, divided by the total price movement over the same period. However, this version of the CMO has some limitations.
The primary disadvantage of the original CMO is its responsiveness to short-term volatility, making the signals less smooth and more erratic, especially in fluctuating markets. This instability can lead to misleading buy or sell signals.
To address this, we integrated the concept from the Moving Average Convergence Divergence (MACD) indicator. By applying a 9-period exponential moving average (EMA) to the CMO line, we obtained a smoothed signal line. This line acts as a filter, identifying confirmed overbought or oversold states, thereby reducing the number of false signals.
Similar to the MACD histogram, we generate columns representing the difference between the CMO and its signal line, reflecting market momentum. We use this momentum indicator as a criterion for entry and exit points. Trades are executed when there's a convergence of CMO and signal lines during an oversold state, and they are closed when the CMO line diverges from the signal line, indicating increased selling pressure.
APPLICATION
Since the 9-period EMA smooths the CMO line, it's less susceptible to extreme price fluctuations. However, this smoothing also makes it more challenging to breach the original +50 and -50 benchmarks.
To increase trading opportunities, we've tightened the boundary ranges. Users can customize the target benchmark lines in the settings to adjust for the volatility of the underlying asset.
The 'cool down period' is essentially the number of bars that await before the next signal generation. This feature is employed to dodge the occurrence of multiple signals in a short period.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Signal Cool Down Period: 5
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Custom Signal Oscillator StrategyThe CSO is made to help traders easily test their theories by subtracting the difference between two customizable plots(indicators) without having to search for strategies. The general purpose is to provide a tool to users without coding knowledge.
How to use :
Apply the indicator(s) to test
Go to the CSO strategy input settings and select the desired plots from the added indicators. (The back test will enter long or short depending on the fast signal crosses on the slow signal)
Pull up the strategy tester
Adjust the input settings on the selected indicator(s) to back test
For example, the published strategy is using the basis lines from two Donchian channels with varying length. This can be utilized with multiple overlays on the chart and oscillators that are operating on the same scale with each other. Since chart glows aren't extremely common, a glow option is included to stand out on the chart as the chain operator. A long only option for is also included for versatility.
Ultimate Trading StrategyDescription:
In this TradingView Pine Script publication, we introduce a powerful tool designed to enhance your trading strategies by combining the Exponential Moving Average (EMA) and the Relative Strength Index (RSI). This strategy is specifically tailored for the EGLD/USDT.P pair on Binance, using a 5-minute interval to capture timely trading opportunities in a volatile market.
Key Features:
Combining EMA and RSI for Robust Signals
This script combines the EMA, which helps identify the overall trend direction, with the RSI, which measures the speed and change of price movements to identify overbought and oversold conditions.
The combination ensures that you get high-probability signals by leveraging both trend-following and momentum-based indicators.
Multiple Timeframe Analysis
Analyze the EMA and RSI across different timeframes to gain a comprehensive view of market conditions and make more informed trading decisions.
Reversing and Extending Signals
Reverse signals generated by indicators to adapt to various market conditions.
Extend signals by specifying conditions such as "RSI cross AND EMA cross WITHIN 2 bars" to capture more nuanced trading opportunities.
Backtesting and Risk Management
Evaluate the performance of your strategies by feeding the results into a backtesting engine.
The strategy risks a maximum of 10% of the account on a single trade to maintain sustainable risk levels.
Available Indicators:
EMA (Exponential Moving Average)
Helps identify the overall trend direction.
Signals:
Long Entry: When the price closes above the EMA.
Short Entry: When the price closes below the EMA.
RSI (Relative Strength Index)
Measures the speed and change of price movements.
Signals:
Long Entry: When RSI is below the oversold level (30).
Short Entry: When RSI is above the overbought level (70).
How It Works:
Long Entry: A buy signal is generated when the price closes above the EMA and the RSI is below the oversold level (30). This indicates that the price is in an upward trend and temporarily oversold, presenting a potential buying opportunity.
Short Entry: A sell signal is generated when the price closes below the EMA and the RSI is above the overbought level (70). This indicates that the price is in a downward trend and temporarily overbought, presenting a potential selling opportunity.
Close Long Position: The script closes long positions when the conditions for a short entry are met.
Close Short Position: The script closes short positions when the conditions for a long entry are met.
Parameters:
EMA Length: 20 (default)
RSI Length: 14 (default)
RSI Overbought Level: 70 (default)
RSI Oversold Level: 30 (default)
Initial Capital: 10,000 USDT (default) – Realistic starting capital for an average trader.
Commission: 0.1% (default) – Reflects typical trading commissions.
Slippage: 0.5 ticks (default) – Accounts for market conditions and potential price slippage during order execution.
Backtesting:
Trading Range: – Ensure that the dataset used covers a significant period to generate a sufficient number of trades.
Dataset Limitation: Due to TradingView Premium's limitation of backtesting only 20,000 candles, it may not be possible to generate more than 100 trades. This limitation affects the statistical relevance of the backtesting results, but the strategy has been tested to provide meaningful insights within these constraints.
Use Case:
This strategy combines the EMA and RSI to identify potential trading opportunities by detecting trend direction and overbought/oversold conditions. It is particularly effective in volatile markets where quick trend reversals are common.
How to Use:
Set the parameters according to your preference or use the default values.
Run the script on the EGLD/USDT.P pair with a 5-minute interval.
Monitor the signals and adjust your trades accordingly.
Korneev Reverse RSIRethinking the Legendary Relative Strength Index by John Welles Wilder
The essence of the new approach lies in the reverse use of the so-called "overbought" and "oversold" zones. In his 1978 book, "New Concepts in Technical Trading Systems," where the RSI mechanism was thoroughly described, Wilder writes that one way to use the oscillator is to open a long position when the RSI drops into oversold territory (below 30) and to open a short position when the RSI rises to overbought levels (above 70). However, backtesting this strategy with such inputs yields rather mediocre results.
Based on the calculation formula, the RSI calculates the rate of price change over a certain period. Therefore, overbought and oversold zones will have relative significance (relative to the set calculation period). It is no coincidence that the word "relative" was added to the name of the oscillator. It is worth accepting as an axiom the assertion that the price of an asset is fair at every moment in time.
Essentially, the RSI calculates the strength of a trend. If the oscillator value is above 70, it is highly likely that an upward movement is occurring in the market. Therefore, in the current strategy, a long position is opened precisely at the moment of greatest buyer strength (when RSI > 80), i.e., in the direction of the trend, since counter-trend trading with the RSI has proven to be ineffective. The position is closed after the buyers lose their advantage and the RSI drops to 40.
The strategy is recommended to be used only with long positions, as short positions show negative results. The strategy uses a moving average for the RSI with a period of 14 to smooth the oscillator data.
--------------------------------------------------------------------------------------------
Переосмысление легендарного осциллятора Relative strength index Джона Уэллса Уайлдера
Суть нового подхода заключается в реверсивном использовании так называемых зон "перекупленности" и "перепроданности". В своей книге от 1978 года "New concepts in tecnical trading systems", в которой был подробно описан механизм работы RSI, Уайлдер пишет, что один из способов использования осциллятора - открытие длинной позиции при снижении RSI в перепроданность (ниже 30) и открытие короткой позиции при повышении RSI до перекупленности (выше 70). Однако бэктест стратегии с такими вводными дает весьма посредственные результаты.
Исходя из формулы расчета, RSI рассчитывает скорость изменения цены за определенный период. Поэтому зоны перекупленности и перепроданности будут иметь относительное значение (относительно установленного периода расчета). Не зря ведь в названии осциллятора было добавлено слово "относительной". Стоит принять за аксиому утверждение, что цена актива справедлива в каждый момент времени.
По сути, RSI рассчитывает силу тренда. Если значение осциллятора выше 70, то на рынке с высокой долей вероятности происходит восходящее движение. Поэтому в текущей стратегии открытие лонга происходит именно в момент наибольшей силы покупателей (когда RSI > 80), то есть в сторону тренда, поскольку контртрендовая торговля по RSI показала свою несостоятельность. Закрытие позиции происходит после того, как покупатели теряют преимущество и RSI снижается до 40.
Стратегию рекомендуется использовать только с длинными позициями, поскольку короткие позиции показывают отрицательный результат. В стратегии используется скользящая средняя для RSI с периодом 14 для сглаживания данных осциллятора.
Triple EMA + QQE Trend Following Strategy [TradeDots]The "Triple EMA + QQE Trend Following Strategy" harnesses the power of two sophisticated technical indicators, the Triple Exponential Moving Average (TEMA) and the Qualitative Quantitative Estimation (QQE), to generate precise buy and sell signals. This strategy excels in capturing shifts in trends by identifying short-term price momentum and dynamic overbought or oversold conditions.
HOW IT WORKS
This strategy integrates two pivotal indicators:
Triple Exponential Moving Average (TEMA): TEMA enhances traditional moving averages by reducing lag and smoothing the data more effectively. It achieves this by applying the EMA formula three times onto the price, as follows:
tema(src, length) =>
ema1 = ta.ema(src, length)
ema2 = ta.ema(ema1, length)
ema3 = ta.ema(ema2, length)
tema = 3*ema1 - 3*ema2 + ema3
This computation helps to sharpen the sensitivity to price movements.
Qualitative Quantitative Estimation (QQE): The QQE indicator improves upon the standard RSI by incorporating a smoothing mechanism. It starts with the standard RSI, overlays a 5-period EMA on this RSI, and then enhances the result using a double application of a 27-period EMA. A slow trailing line is then derived by multiplying the result with a factor number. This approach establishes a more refined and less jittery trend-following signal, complementing the TEMA to enhance overall market timing during fluctuating conditions.
APPLICATION
Referenced from insights on "Trading Tact," the strategy implementation follows:
First of all, we utilize two TEMA lines: one set at a 20-period and the other at a 40-period. Then following the rules below:
40-period TEMA is rising
20-period TEMA is above 40-period TEMA
Price closes above 20-period TEMA
Today is not Monday
RSI MA crosses the Slow trailing line
This strategy does not employ an active take profit mechanism; instead, it utilizes a trailing stop loss to allow the price to reach the stop loss naturally, thereby maximizing potential profit margins.
DEFAULT SETUP
Commission: 0.01%
Initial Capital: $10,000
Equity per Trade: 80%
Users are advised to adjust and personalize this trading strategy to better match their individual trading preferences and style.
RISK DISCLAIMER
Trading entails substantial risk, and most day traders incur losses. All content, tools, scripts, articles, and education provided by TradeDots serve purely informational and educational purposes. Past performances are not definitive predictors of future results.
Reference:
Trading Tact. What Is the QQE Indicator? Retrieved from: tradingtact.com
