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)
Relative
Market First - Relative Strength/Weakness (the ZenBot strategy)This market-first trading strategy gives BUY, SHORT, and CLOSE signals based on volume, trend, and relative strength or weakness to the market (SPY by default, can be customized). This indicator is useful for signaling day-trade entries and exits for tickers that are strong (or weak) against the market.
Stocks that are showing relative strength (or weakness) to the market, are trending, and have decent movement generate a buy (or short) signal. When the trend runs out, a CLOSE signal is fired.
Potential profit (based on ATR) and actual profit is calculated, predicting the type of move expected
Unique 'stay in trade' logic helps prevent unnecessary CLOSE signals if a trend is likely to continue
A colored plot indicates the strength of the current trend and turns orange/red when the strength is weakened.
Crypto traders can uncheck 'Trade during market hours' for 24-hour trading, and should change the comparison ticker from SPY to BTCUSD or something similar for their market.
Enjoy!
KEY CONCEPTS
The three- and five-minute timeframes are used to establish and verify trend ( ADX /DI with custom logic)
Entries and exits are based on Parabolic SAR and confirmed on multiple timeframes, trend, and relative volume
Relative strength /weakness to the market compares ticker to SPY
Chop is avoided at all costs. I've experimented with choppiness indicator below 38, but found that the ADX DI+/- readings work even better.
Trend is established using ADX DI+/- readings over 20, confirmed by EMA 5/13 crossover and EMA5 slope
Signals will fire only if the average volume for the current 5-min bar is above normal
Only tickers with a five-bar / 13 period ATR of 1% the ticker's price generate signal.
Only longs above daily-anchored VWAP , shorts below daily-anchored VWAP
Signals fire on bar close to prevent repainting / look-ahead bias
Indicator labels and alerts generated
SIGNALS
BUY: up-trending tickers showing relative strength are bought on the three-minute PSAR
SELL: when the close price falls below the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
SHORT: down-trending tickers with relative weakness are shorted on the three-minute PSAR
COVER: when the close price moves above the 1, 3, and 5-minute PSAR, or the ADX DI- falls below 20
ALERTS
Alerts are generated on BUY, SELL, SHORT, and COVER signals, as well as optional LOST RELATIVE STRENGTH and LOST RELATIVE WEAKNESS
INPUTS
Use relative strength /weakness comparison with the market : trigger trades based on the ticker's strength or weakness to the selected comparison ticker (usually SPY for equities or BTCUSD for crypto)
[* ]Comparison Ticker for relative strength /weakness : Ticker to compare against for relative strength /weakness
Trade during market hours only : Take buy/sells during specified hours. Disable this for crypto trading.
[* ]Market hours (market time) : Customize market hours - defaults to 9:30 to 16:00 EST
[* ]"Only trade very strong trends" : take trades only if an established trend is very strong ( ADX over 40 ) (DEFAULT = ON)
"Limit trade direction to VWAP" : Long trades only above VWAP , shorts below (DEFAULT = ON)
"Limit trade direction to Market direction" : Long trades only if SPY (or selected comparison ticker) is up, shorts if the market is down. (DEFAULT= ON)
"Limit trades based on a ticker's green/red status for the day" : Long trades if the ticker is green for the day, shorts if red. (DEFAULT = ON)
RELATIVE VALUE TRADE MANAGEMENT WEBHOOKThis script it's created to send open-close signals via webhook. It allows you to open a relative value position based in the relative graph. You can set the TP and SL levels and the script will send the signal to your exchange.
Due a pine limitations it is necessary apply the script in the 2 different actives and set the alerts. You can just do the relative analysis and then go to the first asset and set the script. Create your alert and then just go to the other asset and create the alert. It doesn't necessary to change anything in the script because the levels are the same.
It is also possible to do the analysis using the script, deploying the relative graph, but could be annoying sometimes due scales.
Positions will be placed at close always.
THIS IS IMPORTANT: I use Zignaly as a exhange so if you are using Binance or other YOU MUST CHANGE the code. If you know the JSON format that It requires would be easy.
Here is tips in all the important imputs. But let me explain the most important.
The MANDATORY fields are:
Ticker IDs: Here you must write the EXACT ID code for the active. Caps included.
Example : BINANCE:SUSHIUSDTPERP
It is also important select the correct market side. If you want to be long of ANY active you must write that ID in the LONG ID. For short positions is the same.
Time frame: Here you can select the time frame of the graph (not the current active graph, I mean the relative one.) The orders will be send using that time frame. I recommend to do the analysis in other window and then use the script to trigger the order in the time frame that you want.
Money management: In these fields you can select the qty that you will lose if the SL level is reach. Based in a determinated amount of currency or in a % of your capital.
Dates: It is important to select the start date. If the order is already open, we must look for the moment where the activation price was reached. If the order is unopened it is better to select the current date, so the order will be triggered when the entry level is reached.
Ids: If you have current open position you can set here that ID to send the correct order to the exchange.
To set the alert just call the function {{{strategy.order.alert_message}}}
Combo Backtest 123 Reversal & Relative Volatility Index This is combo strategies for get a cumulative signal.
First strategy
This System was created from the Book "How I Tripled My Money In The
Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies.
The strategy buys at market, if close price is higher than the previous close
during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50.
The strategy sells at market, if close price is lower than the previous close price
during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50.
Second strategy
The RVI is a modified form of the relative strength index (RSI).
The original RSI calculation separates one-day net changes into
positive closes and negative closes, then smoothes the data and
normalizes the ratio on a scale of zero to 100 as the basis for the
formula. The RVI uses the same basic formula but substitutes the
10-day standard deviation of the closing prices for either the up
close or the down close. The goal is to create an indicator that
measures the general direction of volatility. The volatility is
being measured by the 10-days standard deviation of the closing prices.
WARNING:
- For purpose educate only
- This script to change bars colors.
Hilega_Milega_StrategyThis study shows the price action strength using RSI & Moving Averages.
In this study different line indicates below measures---
BlackLine - Strength
Red Line - Volume
GreenLine - Price
1. Volume line is below price and strength then the price may go up.
2. Volume line is above price and strength then the price may go down.
3. Volume line below strength line price may stop going down.
4. Price line above strength then price may go down.
There is a total of 2 zones that indicate the price strength in the red zone price is showing strength ness and in the blue zone, the price is showing weakness.
This study helps to identify if the price is having strength or not if going up or down based on volume.\
Strategy give signal based on above-mentioned line as follows.
Buy - Strength line above volume & price line + Price line crossover Volume line.
Sell - Strength line below volume & price line + Price line crossunder Volume line.
Super Momentum StrategyThis is a strategy utilizing multiple of Evergets RMI (thanks to him for permission to publish) and a Chance Momentum.
It buys when 4 of the RMIs are below their thresholds and sells when they are above. There is a 5th one I added last night that works in Reverse - buy when its above and sell when below, which should work better for pyramiding strats by using it at the right rate to set the overall trend.
Very basic sample below, I could have set it up better as my example but just want to publish.
RMI + Triple HMRSI + Double EVWRSI + TERSI + CMO StrategyThis is a strange experimental strategy WIP that I decided to upload an early version to share some of what I am working on. Just one script of a few.
It combines Chande Momentum with RMI and some weird ones I am experimenting with - Triple Hull MA RSI, Double Exponential + Volume Weighted RSI, Triple Exponential RSI. And to top it off, a final oscillator that combines the THMRSI with the RMI.
The main intention here, currently, is to test the usefulness of each on different timeframes and values. Currently it is considered to buy when all are below their threshold and sell when all are above, with the chande momentum crossing its line as the final confirmation.
For now there is no individual for each of the unique elements included. I am going to likely use this is a working house project to test other experimental indicators in the future.
It may be some of these are better suited for long term but I do think they have valid uses in checking short and long term momentum at the very least.
I copied the RMI from Everget.
XPloRR S&P500 Stock Market Crash Detection Strategy v2XPloRR S&P500 Stock Market Crash Detection Strategy v2
Long-Term Trailing-Stop strategy detecting S&P500 Stock Market Crashes/Corrections and showing Volatility as warning signal for upcoming crashes
Detecting or avoiding stock market crashes seems to be the 'Holy Grail' of strategies.
Since none of the strategies that I tested can beat the long term Buy&Hold strategy, the purpose was to detect a stock market crash on the S&P500 and step out in time to minimize losses and beat the Buy&Hold strategy. So beat the Buy&Hold strategy with around 10 trades. 100% capitalize sold trade into new trade.
With the default parameters the strategy generates 10262% profit (starting at 01/01/1962 until release date), with 10 closed trades, 100% profitable, while the Buy&Hold strategy only generates 3633% profit, so this strategy beats the Buy&Hold strategy by 2.82 times !
Also the strategy detects all major S&P500 stock market crashes and corrections since 1962 depending on the Trailing Stop Smoothness parameter, and steps out in time to cut losses and steps in again after the bottom has been reached. The 5 major crashes/corrections of 1987, 1990, 2001, 2008 and 2010 were successfully detected with the default parameters.
The script was first released on November 03 2019 and detected the Corona Crash on March 04 2020 with a Volatility crash-alert and a Sell crash-alert.
I have also created an Alerter Study Script based on the engine of this script, which generates Buy, Sell and Volatility signals.
If you are interested in this Alerter version script, please drop me a mail.
The script shows a lot of graphical information:
the Close value is shown in light-green. When the Close value is temporarily lower than the Buy value, the Close value is shown in light-red. This way it is possible to evaluate the virtual losses during the current trade.
the Trailing Stop value is shown in dark-green. When the Sell value is lower than the Buy value, the last color of the trade will be red (best viewed when zoomed)
the EMA and SMA values for both Buy and Sell signals are shown as colored graphs
the Buy signals are labeled in blue and the Sell signals are labeled in purple
the Volatility is shown below in green and red. The Alert Threshold (red) is default set to 2 (see Volatility Threshold parameter below)
How to use this Strategy?
Select the SPX (S&P500) graph and add this script to the graph.
Look in the strategy tester overview to optimize the values Percent Profitable and Net Profit (using the strategy settings icon, you can increase/decrease the parameters), then keep using these parameters for future Buy/Sell signals on the S&P500.
More trades don't necessarily generate more overall profit. It is important to detect only the major crashes and avoid closing trades on the smaller corrections. Bearing the smaller corrections generates a higher profit.
Watch out for the Volatility Alerts generated at the bottom (red). The Threshold can by changed by the Volatility Threshold parameter (default=2% ATR). In almost all crashes/corrections there is an alert ahead of the crash.
Although the signal doesn't predict the exact timing of the crash/correction, it is a clear warning signal that bearish times are ahead!
The correction in December 2018 was not a major crash but there was already a red Volatility warning alert. If the Volatility Alert repeats the next weeks/months, chances are higher that a bigger crash or correction is near. As can be seen in the graphic, the deeper the crash is, the higher and wider the red Volatility signal goes. So keep an eye on the red flag!
Here are the parameters:
Fast MA Buy: buy trigger when Fast MA Buy crosses over the Slow MA Buy value (use values between 10-20)
Slow MA Buy: buy trigger when Fast MA Buy crosses over the Slow MA Buy value (use values between 21-50)
Minimum Buy Strength: minimum upward trend value of the Fast MA Buy value (directional coefficient)(use values between 10-100)
Fast MA Sell: sell trigger when Fast MA Sell crosses under the Slow MA Sell value (use values between 10-20)
Slow MA Sell: sell trigger when Fast MA Sell crosses under the Slow MA Sell value (use values between 21-50)
Minimum Sell Strength: minimum downward trend value of the Fast MA Sell value (directional coefficient)(use values between 10-100)
Trailing Stop ATR: trailing stop % distance from the smoothed Close value (use values between 2-20)
Trailing Stop Smoothness: MA value for smoothing out the Trailing Stop close value
Buy On Start Date: force Buy on start date even without Buy signal (default: true)
Sell On End Date: force Sell on end date even without Sell signal (default: true)
Volatility EMA Period: MA value of the Volatility value (default 15)
Volatility Threshold: Threshold value to change volatility graph to red (default 2)
Volatility Graph Scaler: Scaling of the volatility graph (default 5)
Important : optimizing and using these parameters is no guarantee for future winning trades!
[STRATEGY] Jurik RSXA private strategy from the Profitable Jurik RSX preview for backtesting purposes.
