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VIDYA Trend StrategyOne of the most common messages I get is people reaching out asking for quantitative strategies that trade cryptocurrency. This has compelled me to write this script and article, to help provide a quantitative/technical perspective on why I believe most strategies people write for crypto fail catastrophically, and how one might build measures within their strategies that help reduce the risk of that happening. For those that don't trade crypto, know that these approaches are applicable to any market.
I will start off by qualifying up that I mainly trade stocks and ETFs, and I believe that if you trade crypto, you should only be playing with money you are okay with losing. Most published crypto strategies I have seen "work" when the market is going up, and fail catastrophically when it is not. There are far more people trying to sell you a strategy than there are people providing 5-10+ year backtest results on their strategies, with slippage and commissions included, showing how they generated alpha and beat buy/hold. I understand that this community has some really talented people that can create some really awesome things, but I am saying that the vast majority of what you find on the internet will not be strategies that create alpha over the long term.
So, why do so many of these strategies fail?
There is an assumption many people make that cryptocurrency will act just like stocks and ETFs, and it does not. ETF returns have more of a Gaussian probability distribution. Because of this, ETFs have a short term mean reverting behavior that can be capitalized on consistently. Many technical indicators are built to take advantage of this on the equities market. Many people apply them to crypto. Many of those people are drawn down 60-70% right now while there are mean reversion strategies up YTD on equities, even though the equities market is down. Crypto has many more "tail events" that occur 3-4+ standard deviations from the mean.
There is a correlation in many equities and ETF markets for how long an asset continues to do well when it is currently doing well. This is known as momentum, and that correlation and time-horizon is different for different assets. Many technical indicators are built based on this behavior, and then people apply them to cryptocurrency with little risk management assuming they behave the same and and on the same time horizon, without pulling in the statistics to verify if that is actually the case. They do not.
People do not take into account the brokerage commissions and slippage. Brokerage commissions are particularly high with cryptocurrency. The irony here isn't lost to me. When you factor in trading costs, it blows up most short-term trading strategies that might otherwise look profitable.
There is an assumption that it will "always come back" and that you "HODL" through the crash and "buy more." This is why Three Arrows Capital, a $10 billion dollar crypto hedge fund is now in bankruptcy, and no one can find the owners. This is also why many that trade crypto are drawn down 60-70% right now. There are bad risk practices in place, like thinking the martingale gambling strategy is the same as dollar cost averaging while also using those terms interchangeably. They are not the same. The 1st will blow up your trade account, and the 2nd will reduce timing risk. Many people are systematically blowing up their trade accounts/strategies by using martingale and calling it dollar cost averaging. The more risk you are exposing yourself too, the more important your risk management strategy is.
There is an odd assumption some have that you can buy anything and win with technical/quantitative analysis. Technical analysis does not tell you what you should buy, it just tells you when. If you are running a strategy that is going long on an asset that lost 80% of its value in the last year, then your strategy is probably down. That same strategy might be up on a different asset. One might consider a different methodology on choosing assets to trade.
Lastly, most strategies are over-fit, or curve-fit. The more complicated and more parameters/settings you have in your model, the more likely it is just fit to historical data and will not perform similar in live trading. This is one of the reasons why I like simple models with few parameters. They are less likely to be over-fit to historical data. If the strategy only works with 1 set of parameters, and there isn't a range of parameters around it that create alpha, then your strategy is over-fit and is probably not suitable for live trading.
So, what can I do about all of this!?
I created the VIDYA Trend Strategy to provide an example of how one might create a basic model with a basic risk management strategy that might generate long term alpha on a volatile asset, like cryptocurrency. This is one (of many) risk management strategies that can reduce the volatility of your returns when trading any asset. I chose the Variable Index Dynamic Average (VIDYA) for this example because it's calculation filters out some market noise by taking into account the volatility of the underlying asset. I chose a trend following strategy because regressions are capturing behaviors that are not just specific to the equities market.
The more volatile an asset, the more you have to back-off the short term price movement to effectively trend-follow it. Otherwise, you are constantly buying into short term trends that don't represent the trend of the asset, then they reverse and loose money. This is why I am applying a trend following strategy to a 4 hour chart and not a 4 minute chart. It is also important to note that following these long term trends on a volatile asset exposes you to additional risk. So, how might one mitigate some of that risk?
One of the ways of reducing timing risk is scaling into a trade. This is different from "doubling down" or "trippling down." It is really a basic application of dollar cost averaging to reduce timing risk, although DCA would typically happen over a longer time period. If it is really a trend you are following, it will probably still be a trend tomorrow. Trend following strategies have lower win rates because the beginning of a trend often reverses. The more volatile the asset, the more likely that is to happen. However, we can reduce risk of buying into a reversal by slowly scaling into the trend with a small % of equity per trade.
Our example "VIDYA Trend Strategy" executes this by looking at a medium-term, volatility adjusted trend on a 4 hour chart. The script scales into it with 4% of the account equity every 4-hours that the trend is still up. This means you become fully invested after 25 trades/bars. It also means that early in the trade, when you might be more likely to experience a reversal, most of your account equity is not invested and those losses are much smaller. The script sells 100% of the position when it detects a trend reversal. The slower you scale into a trade, the less volatile your equity curve will be. This model also includes slippage and commissions that you can adjust under the "settings" menu.
This fundamental concept of reducing timing risk by scaling into a trade can be applied to any market.
Disclaimer: This is not financial advice. Open-source scripts I publish in the community are largely meant to spark ideas that can be used as building blocks for part of a more robust trade management strategy. If you would like to implement a version of any script, I would recommend making significant additions/modifications to the strategy & risk management functions. If you don’t know how to program in Pine, then hire a Pine-coder. We can help!
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R19 STRATEGYHello again.
Let me introduce you R19 Strategy I wrote for mostly BTC long/short signals
This is an upgrated version of STRATEGY R18 F BTC strategy.
I checked this strategy on different timeframes and different assest and found it very usefull for BTC 1 Hour and 5 minutes chart.
Strategy is basically takes BTC/USDT as a main indicator, so you can apply this strategy to all cryptocurrencies as they mostly acts accordingly with BTC itself (Of course you can change main indicator to different assets if you think that there is a positive corelation with. i.e. for BTC signals you can sellect DXY index for main indicator to act for BTC long/short signals)
Default variables of the inticator is calibrated to BTC/USDT 5 minute chart. I gained above %77 success.
Strategy simply uses, ADX, MACD, SMA, Fibo, RSI combination and opens positions accordingly. Timeframe variable is very important that, strategy decides according the timeframe you've sellected but acts within the timeframe in the chart. For example, if you're on the 5 minutes chart, but you've selected 1 hour for the time frame variable, strategy looks for 1 hour MACD crossover for opening a position, but this happens in 5 minutes candle, It acts quickly and opens the position.
Strategy also uses a trailing stop loss feature. You can determine max stoploss, at which point trailing starts and at which distance trailing follows. The green and red lines will show your stoploss levels according to the position strategy enters (green for long, red for short stop loss levels). When price exceeds to the certaing levels of success, stop loss goes with the profitable price (this means, when strategy opens a position, you can put your stop loss to the green/red line in actual trading)
You can fine tune strategy to all assets.
Please write down your comments if you get more successfull about different time zones and different assets. And please tell me your fine tuning levels of this strategy as well.
See you all. Strategy
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Bitcoin Golden Pi CyclesTops are signaled by the fast top MA crossing above the slow top MA, and bottoms are signaled by the slow bottom MA crossing above the fast bottom MA. Alerts can be set on top and bottom prints. Does not repaint.
Similar to the work of Philip Swift regarding the Bitcoin Pi Cycle Top, I’ve recently come across a similar mathematically curious ratio that corresponds to Bitcoin cycle bottoms. This ratio was extracted from skirmantas’ Bitcoin Super Cycle indicator . Cycle bottoms are signaled when the 700D SMA crosses above the 137D SMA (because this indicator is closed source, these moving averages were reverse-engineered). Such crossings have historically coincided with the January 2015 and December 2018 bottoms. Also, although yet to be confirmed as a bottom, a cross occurred June 19, 2022 (two days prior to this article)
The original pi cycle uses the doubled 350D SMA and the 111D SMA . As pointed out this gives the original pi cycle top ratio:
350/111 = 3.1532 ≈ π
Also, as noted by Swift, 111 is the best integer for dividing 350 to approximate π. What is mathematically interesting about skirmanta’s ratio?
700/138 = 5.1095
After playing around with this for a while I realized that 5.11 is very close to the product of the two most numerologically significant geometrical constants, π and the golden ratio, ϕ:
πϕ = 5.0832
However, 138 turns out to be the best integer denominator to approximate πϕ:
700/138 = 5.0725 ≈ πϕ
This is what I’ve dubbed the Bitcoin Golden Pi Bottom Ratio.
In the spirit of numerology I must mention that 137 does have some things going for it: it’s a prime number and is very famously almost exactly the reciprocal of the fine structure constant (α is within 0.03% of 1/137).
Now why 350 and 700 and not say 360 and 720? After all, 360 is obviously much more numerologically significant than 350, which is proven by the fact that 360 has its own wikipedia page, and 350 does not! Using 360/115 and 720/142, which are also approximations of π and πϕ respectively, this also calls cycle tops and bottoms.
There are infinitely many such ratios that could work to approximate π and πϕ (although there are a finite number whose daily moving averages are defined). Further analysis is needed to find the range(s) of numerators (the numerator determines the denominator when maintaining the ratio) that correctly produce bottom and top signals. Indicator
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Everything Bitcoin [Kioseff Trading]Hello!
This script retrieves most of the available Bitcoin data published by Quandl; the script utilizes the new request.security_lower_tf() function.
Included statistics,
True price
Volume
Difficulty
My Wallet # Of Users
Average Block Size
api.blockchain size
Median Transaction Confirmation Time
Miners' Revenue
Hash Rate
Cost Per Transaction
Cost % of Transaction Volume
Estimated Transaction Volume USD
Total Output Volume
Number Of Transactions Per Block
# of Unique BTC Addresses
# of BTC Transactions Excluding Popular Addresses
Total Number of Transactions
Daily # of Transactions
Total Transaction Fees USD
Market Cap
Total BTC
Retrieved data can be plotted as line graphs; however, the data is initially split between two tables.
The image above shows how the requested Bitcoin data is displayed.
However, in the user inputs tab, you can modify how the data is displayed.
For instance, you can append the data displayed in the floating statistics box to the stagnant statistics box.
The image above exemplifies the instance.
You can hide any and all data via the user inputs tab.
In addition to data publishing, the script retrieves lower timeframe price/volume/indicator data, to which the values of the requested data are appended to center-right table.
The image above shows the script retrieving one-minute bar data.
Up arrows reflect an increase in the more recent value, relative to the immediately preceding value.
Down arrows reflect a decrease in the more recent value relative to the immediately preceding value.
The ascending minute column reflects the number of minutes/hours (ago) the displayed value occurred.
For instance, 15 minutes means the displayed value occurred 15 minutes prior to the current time (value).
Volume, price, and indicator data can be retrieved on lower timeframe charts ranging from 1 minute to 1440 minutes.
The image above shows retrieved 5-minute volume data.
Several built-in indicators are included, to which lower timeframe values can be retrieved.
The image above shows LTF VWAP data. Also distinguished are increases/decreases for sequential values.
The image above shows a dynamic regression channel. The channel terminates and resets each fiscal quarter. Previous channels remain on the chart.
Lastly, you can plot any of the requested data.
The new request.security_lower_tf() function is immensely advantageous - be sure to try it in your scripts! Indicator
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Sideways Strategy DMI + Bollinger Bands (by Coinrule)Markets don’t always trade in a clear direction. At a closer look, most of the time, they move sideways. Relying on trend-following strategies all the time can thus lead to repeated false signals in such conditions.
However, before you can safely trade sideways, you have to identify the most suitable market conditions.
The main features of such strategies are:
Short-term trades, with quick entries and quick exits
Slightly contrarian and mean-reversionary
Require some indicator that tells you it’s a sideways market
This Sideways DMI + Bollinger Bands strategy incorporates such features to bring you a profitable alternative when the regular trend-following systems stop working.
ENTRY
1. The trading system requires confirmation for a sideways market from the Directional Movement Index (DMI) before you can start opening any trades. For this purpose, the strategy uses the absolute difference between positive and negative DMI, which must be lower than 20.
2. To pick the right moment to buy, the strategy looks at the Bollinger Bands (BB). It enters the trade when the price crosses over the lower BB.
EXIT
The strategy then exits when the move has been exhausted. Generally, in sideways markets, the price should revert lower. The position is closed when the price crosses back down below the upper BB.
The best time frame for this strategy based on our backtest is the 1-hr. Shorter timeframes can also work well on certain coins that are more volatile and trade sideways more often. However, as expected, these exhibit larger volatility in their returns. In general, this approach suits medium timeframes. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange. Strategy
Three EMAs Trend-following Strategy (by Coinrule)Trend-following strategies are great because they give you the peace of mind that you're trading in line with the market.
However, by definition, you're always following. That means you're always a bit later than your want to be. The main challenges such strategies face are:
Confirming that there is a trend
Following the trend, hopefully, early enough to catch the majority of the move
Hopping off the trade when it seems to have run its course
This EMA Trend-following strategy attempts to address such challenges while allowing for a dynamic stop loss.
ENTRY
The trading system requires three crossovers on the same candle to confirm that a new trend is beginning:
Price crossing over EMA 7
Price crossing over EMA 14
Price crossing over EMA 21
The first benefit of using all three crossovers is to reduce false signals. The second benefit is that you know that a strong trend is likely to develop relatively soon, with the help of the fast setup of the three EMAs.
EXIT
The strategy comes with a fixed take profit and a volatility stop, which acts as a trailing stop to adapt to the trend's strength. That helps you get out of the way as soon as market conditions change. Depending on your long-term confidence in the asset, you can edit the fixed take profit to be more conservative or aggressive.
The position is closed when:
The price increases by 4%
The price crosses below the volatility stop.
The best time frame for this strategy based on our backtest is the 4-hr. Shorter timeframes can also work well, although they exhibit larger volatility in their returns. In general, this approach suits medium timeframes. A trading fee of 0.1% is taken into account. The fee is aligned to the base fee applied on Binance, which is the largest cryptocurrency exchange. Strategy
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Cryptogrithm's Secret Momentum and Volatility IndicatorThis indicator is hard-coded for Bitcoin, but you may try it on other asset classes/coins. I have not updated this indicator in over 3 years, but it seems to still work very well for Bitcoin.
This indicator is NOT for beginners and is directed towards intermediate/advanced traders with a sensibility to agree/disagree with what this indicator is signalling (common sense).
This indicator was developed back in 2018 and I has not been maintained since, which is the reason why I am releasing it. (It still works great though! At the time of this writing of May 2022).
How to use:
Terms:
PA (Price Action): Literally the candlestick formations on your chart (and the trend formation). If you don't know how to read and understand price action, I will make a fast-track video/guide on this later (but in the meanwhile, you need to begin by learning Order-Flow Analysis, please google it first before asking).
CG Level (Cryptogrithm Level/Yellow Line): PA level above = bullish, PA level below = bearish
CG Bands (Cryptogrithm Bands): This is similar to how bollingers work, you can use this the same was as bollinger bands. The only difference is that the CG bands are more strict with the upper and lower levels as it uses different calculations to hug the price tighter allowing it to be more reactive to drastic price changes (earlier signals for oversold/overbought).
CG Upper Band (Red Upper Line): Above this upper bound line means overbought.
CG Middle Band (Light Blue Line): If PA trades above this line, the current PA trend is bullish continuing in the uptrend. If PA trades below this line, the current PA trend is bearish continuing in the downtrend. This band should only be used for short-term trends.
CG Lower Band (Green Lower Line): Below this lower bound line means oversold.
What the CG Level (yellow line) tells you:
PA is trading above CG Level = Bullish
PA is trading below CG Level = Bearish
Distance between CG Level and price = Momentum
What this means is that the further away the price is from the CG Level, the greater the momentum of the current PA trend. An increasing gap between the CG Level and PA indicates the price's strength (momentum) towards the current upward/downward trend. Basically when the PA and CG Level diverge, it means that the momentum is increasing in the current trend and when they converge, the current trend is losing momentum and the direction of the PA trend may flip towards the other direction (momentum flip).
PA+CG Level Momentum:
To use the CG Level as a momentum indicator, you need to pay attention to how the price and the CG level are moving away/closer from each other:
PA + CG Level Diverges = Momentum Increasing
PA + CG Level Converges = Momentum Decreasing
Examples (kind of common sense, but just for clarity):
Case 1: Bullish Divergence (Bullish): The PA is ABOVE and trending AWAY above from the CG Level = very bullish, this means that momentum is increasing towards the upside and larger moves will come (increasing gap between the price and CG Level)
Case 2: Bearish Convergence (Bearish): - The PA is ABOVE the CG Level and trending TOWARDS the CG Level = bearish, there is a possibility that the upward trend is ending. Look to start closing off long positions until case 1 (divergence) occurs again.
Case 3: Neutral - The PA is trading on the CG Level (no clear divergence or convergence between the PA and CG Level) = Indicates a back and forth (tug of war) between bears and bulls. Beware of choppy price patterns as the trend is undecisive until either supply/liquidity is dried out and a winner between bull/bear is chosen. This is a no trade zone, but do as you wish.
Case 4: Bearish Divergence (Bearish): The PA is BELOW and trending AWAY BELOW from the CG Level = very bearish, this means that momentum is increasing towards the downside and larger downward moves will come (increasing gap between the price and CG Level).
Case 5: Bullish Convergence (Bullish): - The PA is BELOW the CG Level and trending TOWARDS the CG Level = bullish, there is a possibility that the downward trend is ending and a trend flip is occuring. Look to start closing off short positions until case 4 (divergence) occurs again.
CG Bands + CG Level: You can use the CG bands instead of the PA candles to get a cleaner interpretation of reading the momentum. I won't go into detail as this is pretty self-explanatory. It is the same explanation as PA+CG Level Momentum, but you are replacing the PA candles with the CG Bands for interpretation. So instead of the PA converging/diverging from the CG Level, the Upper and Lower Bound levels are converging/diverging from the CG level instead.
Convergence: CG Level (yellow line) trades inside the CG bands
Divergence: CG Level (yellow line) trades outside the CG bands
Bullish/Bearish depends on whether the CG Band is trading below or above the CG level. If CG Band is above the CG Level, this is bullish. If CG Band is below the CG level, this is bearish.
Crosses (PA or CG Band crosses with CG level): This typically indicates volatility is incoming.
There are MANY MANY MANY other ways to use this indicator that is not explained here and even other undiscovered methods. Use some common sense as to how this indicator works (it is a momentum indicator and volatility predictor). You can get pretty creative and apply your own methods / knowledge to it and look for patterns that occur. Feel free to comment and share what you came up with! Indicator
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Stochastic Moving AverageHi all,
This Strategy script combines the power of EMAs along with the Stochastic Oscillator in a trend following / continuation manner, along with some cool functionalities.
I designed this script especially for trading altcoins, but it works just as good on Bitcoin itself and on some Forex pairs.
______ SIGNALS ______
The script has 4 mandatory conditions to unlock a trading signal. Find these conditions for a long trade below (works the exact other way round for shorts)
- Fast EMA must be higher than Slow EMA
- Stochastic K% line must be in oversold territory
- Stochastic K% line must cross over Stochastic D% line
- Price as to close between slow EMA and fast EMA
Once all the conditions are true, a trade will start at the opening of the next
______ SETTINGS ______
- Trade Setup:
Here you can choose to trade only longs or shorts and change your Risk:Reward.
You can also decide to adjust your volume per position according to your risk tolerance. With “% of Equity” your stop loss will always be equal to a fixed percentage of your initial capital (will “compound” overtime) and with “$ Amount” your stop loss will always be 'x' amount of the base currency (ex: USD, will not compound)
Stop Loss:
The ATR is used to create a stop loss that matches current volatility. The multiplier corresponds to how many times the ATR stop losses and take profits will be away from closing price.
- Stochastic:
Here you can find the usual K% & D% length and overbought (OB) and oversold (OS) levels.
The “Stochastic OB/OS lookback” increase the tolerance towards OB/OS territories. It allows to look 'x' bars back for a value of the Stochastic K line to be overbought or oversold when detecting an entry signal.
The “All must be OB/OS” refers to the previous “Stochastic OB/OS lookback” parameter. If this option is ticked, instead of needing only 1 OB/OS value within the lookback period to get a valid signal, now, all bars looked back must be OB/OS.
The color gradient drawn between the fast and slow EMAs is a representation of the Stochastic K% line position. With default setting colors, when fast EMA > slow EMA, gradient will become solid blue when Stochastic is oversold and when slow EMA > fast EMA, gradient will become solid blue when Stochastic is overbought
- EMAs:
Just pick your favorite ones
- Reference Market:
An additional filter to be certain to stay aligned with the current a market index trend (in our case: Bitcoin). If selected reference market (and timeframe) is trading above selected EMA, this strategy will only take long trades (vice-versa for shorts) Because, let’s face it… even if this filter isn’t bulletproof, you know for sure that when Bitcoin tanks, there won’t be many Alts going north simultaneously. Once again, this is a trend following strategy.
A few tips for increased performance: fast EMA and D% Line can be real fast… 😉
As always, my scripts evolve greatly with your ideas and suggestions, keep them coming! I will gladly incorporate more functionalities as I go.
All my script are tradable when published but remain work in progress, looking for further improvements.
Hope you like it!
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