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)
Index
[Pt] Premarket Breakout StrategyThis is a 1 trade per day strategy for trading SPY or QQQ index. By default, this is designed for 1 min time frame. This was an experimental script that seems to be profitable at the time of publication.
How it works:
Pre-market high and low is defined per trading day between 9:00 to 9:30 EST.
Then we looking for the first breakout on either PM high or PM low.
- Breakout high = long trade
- Breakout low = short trade
If long trade, we wait until Stochastic RSI D signal line to hit a lower threshold (18 by default). Then we enter long when K crosses above D line.
If short trade, we wait until Stochastic RSI D signal line to hit an upper threshold (82 by default). Then we enter short when K crosses below D line.
Stop loss for long
- set to PM low if entry is above PM high + %ATR buffer
- or set to PM range + %ATR buffer
Stop loss for short
- set to PM high if entry is below PM low + %ATR buffer
- or set to PM range + %ATR buffer
Profit target is set to 2x the risk by default.
*Note: Different Stochastic RSI lengths should be used if trading 5 min time frame. See tooltip.
Happy trading~~!
Ichimoku with MACD/ CMF/ TSIThis is a very powerful trend strategy designed for markets such as stocks market , stock index and crypto.
For time frames I found out that 1h seems to do the trick.
Components:
Ichimoku full pack
MACD histogram
CMF oscillator
TSI oscillator
Rules for entry
Long :
For Ichimoku:Tenkan part of cloud is bigger than kijun, Chikou is above 0 , close of a candle is above the Senkou
MACD histogram is above 0
CMF oscillator is positive and bigger than 0.1
TSI oscillator is above 0
Short:
For Ichimoku:Tenkan part of cloud is smaller than kijun, Chikou is below 0 , close of a candle is belowthe Senkou
MACD histogram is below 0
CMF oscillator is negative and below -0.1
TSI oscillator is below 0
Rules for exit
This strategy does not have any risk management inside. Instead it exits whenver it receives an opposite signal form the original one used for entry.
If you have any questions let me know !
Combo Backtest 123 Reversal & Positive Volume 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 theory behind the indexes is as follows: On days of increasing volume,
you can expect prices to increase, and on days of decreasing volume, you can
expect prices to decrease. This goes with the idea of the market being in-gear
and out-of-gear. Both PVI and NVI work in similar fashions: Both are a running
cumulative of values, which means you either keep adding or subtracting price
rate of change each day to the previous day`s sum. In the case of PVI, if today`s
volume is less than yesterday`s, don`t add anything; if today`s volume is greater,
then add today`s price rate of change. For NVI, add today`s price rate of change
only if today`s volume is less than yesterday`s.
WARNING:
- For purpose educate only
- This script to change bars colors.
MACD oscillator with EMA strategy 4H This is a simple, yet efficient strategy, which is made from a combination of an oscillator and a moving average.
Its setup for 4h candles with the current settings, however it can be adapted to other different timeframes.
It works nicely ,beating the buy and hold for both BTC and ETH over the last 3 years.
As well with some optimizations and modifications it can be adapted to futures market, indexes(NASDAQ,NIFTY etc), forex(GBPUSD), stocks and so on.
Components:
MACD
EMA
Time condition
Long/short option
For long/exit short we enter when we are above the ema, histogram is positive and current candle is higher than previous.
For short /exit long , when close below ema, histo negative and current candles smaller than previous
If you have any questions please let me know !
Combo Backtest 123 Reversal & MASS 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 Mass Index was designed to identify trend reversals by measuring
the narrowing and widening of the range between the high and low prices.
As this range widens, the Mass Index increases; as the range narrows
the Mass Index decreases.
The Mass Index was developed by Donald Dorsey.
WARNING:
- For purpose educate only
- This script to change bars colors.
Combo Backtest 123 Reversal & Market Facilitation 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 Market Facilitation Index is an indicator that relates price range to
volume and measures the efficency of price movement. Use the indicator to
determine if the market is trending. If the Market Facilitation Index increased,
then the market is facilitating trade and is more efficient, implying that the
market is trending. If the Market Facilitation Index decreased, then the market
is becoming less efficient, which may indicate a trading range is developing that
may be a trend reversal.
WARNING:
- For purpose educate only
- This script to change bars colors.
Bollinger Bands Strategy with Intraday Intensity IndexFor Educational Purposes. Results can differ on different markets and can fail at any time. Profit is not guaranteed.
This only works in a few markets and in certain situations. Changing the settings can give better or worse results for other markets.
This is a mean reversion strategy based on Bollinger Bands and the Intraday Intensity Index (a volume indicator). John Bollinger mentions that the Intraday Intensity Index can be used with Bollinger Bands and is one of the top indicators he recommends in his book. It seems he prefers it over the other volume indicators that he compares to for some reason. III looks a lot like Chaikin Money Flow but without the denominator in that calculation. On the default settings of the BBs, the III helps give off better entry signals. John Bollinger however is vague on how to use the BBs and it's hard to say if one should enter when it is below/above the bands or when the price crosses them. I find that with many indicators and strategies it's best to wait for a confirmation of some sort, in this case by waiting for some crossover of a band. Like most mean reversion strategies, the exit is very loose if using BBs alone. Usually the plan to exit is when the price finally reverts back to the mean or in this case the middle band. This can potentially lead to huge drawdowns and/or losses. Mean reversion strategies can have high win/loss ratios but can still end up unprofitable because of the huge losses that can occur. These drawdowns/losses that mean reversion strategies suffer from can potentially eat away at a large chunk of all that was previously made or perhaps up to all of it in the worst cases, can occur weeks or perhaps up to months after being profitable trading such a strategy, and will take a while and several trades to make it all back or keep a profitable track record. It is important to have a stop loss, trailing stop, or some sort of stop plan with these types of strategies. For this one, in addition to exiting the trade when price reverts to the middle band, I included a time-based stop plan that exits with a gain or with a loss to avoid potentially large losses, and to exit after only a few periods after taking the trade if in profit instead of waiting for the price to revert back to the mean.
The Lazy Trader - Index (ETF) Trend Following Robot50/150 moving average, index (ETF) trend following robot. Coded for people who cannot psychologically handle dollar-cost-averaging through bear markets and extreme drawdowns (although DCA can produce better results eventually), this robot helps you to avoid bear markets. Be a fair-weathered friend of Mr Market, and only take up his offer when the sun is shining! Designed for the lazy trader who really doesn't care...
Recommended Chart Settings:
Asset Class: ETF
Time Frame: Daily
Necessary ETF Macro Conditions:
a) Country must have healthy demographics, good ratio of young > old
b) Country population must be increasing
c) Country must be experiencing price-inflation
Default Robot Settings:
Slow Moving Average: 50 (integer) //adjust to suit your underlying index
Fast Moving Average: 150 (integer) //adjust to suit your underlying index
Bullish Slope Angle: 5 (degrees) //up angle of moving averages
Bearish Slope Angle: -5 (degrees) //down angle of moving averages
Average True Range: 14 (integer) //input for slope-angle formula
Risk: 100 (%) //100% risk means using all equity per trade
ETF Test Results (Default Settings):
SPY (1993 to 2020, 27 years), 332% profit, 20 trades, 6.4 profit factor, 7% drawdown
EWG (1996 to 2020, 24 years), 310% profit, 18 trades, 3.7 profit factor, 10% drawdown
EWH (1996 to 2020, 24 years), 4% loss, 26 trades, 0.9 profit factor, 36% drawdown
QQQ (1999 to 2020, 21 years), 232% profit, 17 trades, 3.6 profit factor, 2% drawdown
EEM (2003 to 2020, 17 years), 73% profit, 17 trades, 1.1 profit factor, 3% drawdown
GXC (2007 to 2020, 13 years), 18% profit, 14 trades, 1.3 profit factor, 26% drawdown
BKF (2009 to 2020, 11 years), 11% profit, 13 trades, 1.2 profit factor, 33% drawdown
A longer time in the markets is better, with the exception of EWH. 6 out of 7 tested ETFs were profitable, feel free to test on your favourite ETF (default settings) and comment below.
Risk Warning:
Not tested on commodities nor other financial products like currencies (code will not work), feel free to leave comments below.
Moving Average Slope Angle Formula:
Reproduced and modified from source:
Mean Reversion w/ Bollinger BandsThis is a more advanced version of my original mean reversion script.
It employs the famous Bollinger Bands.
This robot will buy when price falls below the lower Bollinger Band, and sell when price moves above the upper Bollinger Band.
I've only tested it on the S&P 500, though you could try it out on other assets to see the backtest performance.
During the recent COVID-19 bear market drop, it produced several buy signals on the S&P which I followed, and made some nice gains so far.
I still think this would make a better investing strategy (buy undervalued / sell over-valued), rather than a trading strategy.
I use this robot for my long term portfolio.
Combo Strategy 123 Reversal & CCI 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 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 these
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.
To put it simply, the Commodity Channel Index (CCI) value shows how the instrument is trading
relative to its mean (average) price. When the CCI value is high, it means that the prices are
high compared to the average price; when the CCI value is down, it means that the prices are low
compared to the average price. The CCI value usually does not fall outside the -300 to 300 range
and, in fact, is usually in the -100 to 100 range.
WARNING:
- For purpose educate only
- This script to change bars colors.
Commodity Selection Index Backtest The Commodity Selection Index ("CSI") is a momentum indicator. It was
developed by Welles Wilder and is presented in his book New Concepts in
Technical Trading Systems. The name of the index reflects its primary purpose.
That is, to help select commodities suitable for short-term trading.
A high CSI rating indicates that the commodity has strong trending and volatility
characteristics. The trending characteristics are brought out by the Directional
Movement factor in the calculation--the volatility characteristic by the Average
True Range factor.
Wilder's approach is to trade commodities with high CSI values (relative to other
commodities). Because these commodities are highly volatile, they have the potential
to make the "most money in the shortest period of time." High CSI values imply
trending characteristics which make it easier to trade the security.
The Commodity Selection Index is designed for short-term traders who can handle
the risks associated with highly volatile markets.
WARNING:
- For purpose educate only
- This script to change bars colors.
