Algo: Beer Money 1.0.0

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Algo: Beer Money 1.0.0

Last comment by - September 2020

Algo: Beer Money 1.0.0

Low capacity algo for $10k trading, generates profit. Use with caution and widen ratio spread. Free.

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Algo: Beer Money 1.0.0

Warren Harding | October 2018

Hi,

Here is a low capacity algo for you. It only trades $10,000, and generates some profit. Watch out trading it live, you might want to widen the lowerRatio, upperRatio spread, so that the market doesn't react too much to what you are doing. Use at your own risk.

This one is free, on me.

Regards,

Warren

Author

Warren Harding

October 2018

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5 people upvoted this

Jared Broad INVESTOR

QuantConnect | October 2018

Interesting, what effect is it focusing on / harnessing?

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Douglas Stridsberg INVESTOR

October 2018

Couple of words of caution for those looking to trade this live (a.k.a. disclaimers I would have put before posting this online):

You're relying on very precise and timely fills as you need to trade huge amounts to get any meaningful % return. I don't know how AT trades but I would be extremely wary of anything that backtests at 13 Sharpe and 1500% CAGR and trades only one asset.
The way this algorithm works, it has no risk management - it relies on the price ticking upwards for your limit sell order to be filled. Let's say you had your buy order filled but then the price tanked - unless it recovered back up, there would be nothing to stop you from losing your entire capital (theoretically), as you're putting it all at risk with each trade. You're risking it all to make a small, incremental profit - this isn't necessarily bad but must be understood properly before trading.
If you don't get filled at exactly your limit order, you can very quickly incur great losses (owing to the large order sizes needed to generate P&L). This is a textbook example of a strategy with extremely negative skew (many small gains with a few, large losses) and high kurtosis (significant outliers in the distribution of returns). If you're going to trade this, you need to understand what this means for your risk profile.
The very high frequency of trading (~13 per day) means you will be running up a huge commission and trading fee bill. If you're looking to trade this live, be diligent in understanding exactly how and when your broker will charge you (per trade or at end of month? Actual trading or estimated volume? Fixed charge per stock or % charge on volume?) You do not want to end up owing more than you can afford because you ran into a loss like I described in #2.
The backtest shown uses a very short lookback (1 year) and shows only one underlier. I suspect this is not a coincidence - this is (very likely) the best possible outcome out of many. Needless to say, before you choose to trade this live, you need to have tested this on multiple underliers and time spans to get a sense of how this performs.

Having said all this, straight lines with positive slopes are always beautiful to look at :) .

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3 people upvoted this

Yujun Tang INVESTOR

October 2018

Nice troll algo :)

You're just filling limit orders to buy at the bid and sell at the offer, basically a Godlike backtest Market Maker.

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Warren Harding INVESTOR

October 2018

As Yujun says, it's just a simple market maker. The trick is to screen first for stocks that have both a consistently high spread AND decent dollar volume. It won't work on stocks with a low spread as you get killed by fees. It helps if the stock you are trading is relatively range bound as well.

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Yujun Tang INVESTOR

October 2018

Yeah it'll work under the circumstances like you said, although the algo is too simple to trade live.

It needs more considerations, e.g. If you're holding a position and the market is turning against you, the algo would still be placing limit orders that will not get filled.

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Warren Harding INVESTOR

October 2018

Ya, I'm using market making as just one concept in my latest higher capacity, multi-stock stuff.

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Warren Harding INVESTOR

October 2018

There's a lot of skepticism on this thread. Oh well, I'm still standing by my work. Never surrender!!!

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8 people upvoted this

Michael Manus INVESTOR

October 2018

hehe

still nice work and very good contribution to quantconnect.

one day there will be more people posting their stuff here :) (when the skepticism fades)

some have to see the larger picture :):):):) to see more people contributing to QC

thanks warren for this post!

btw: now i know how to trade AT

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Warren Harding INVESTOR

October 2018

Thanks, Michael! To be sure if everyone starts trading AT it'll lead to a situation where we are all frontrunning each other, and no-one will profit. I actually have a list of stocks that perform well with this type of strategy, but that list is proprietary for the time being. You can do all right if you write your own screener to find instruments with high spread and high dollar volume, I've found that I do even better by combining market maker concepts with ways of predicting which way the stock is going to move.

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Yujun Tang INVESTOR

October 2018

Hey Warren, agologies if i sounded skeptical about the strategy. I was questioning the accuracy of the backtest results; it's unlikely that 1500% CAGR would reflect real world trading. The thing about market making algos is that there is no reliable way to backtest them, not even by paper trading, as it depends on the reactions of other market participants.

The strategy most definitely could work, although the order placement needs to be far more complex than just simple limit orders at the bid and ask.

Anyway, thanks for sharing with the community!

Cheers

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Warren Harding INVESTOR

October 2018

No problem Yujun. There's actually a bunch of stuff that I'm not describing regarding how I conceived of this minimalist script, so it probably looks like I just got lucky. It wasn't all luck. Ya, going live is another thing altogether, any backtested algo should be closely monitored, particularly at the outset, to see if it's behaving as expected.

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Nate Betz INVESTOR

October 2018

Just to echo a few other comments on here, this type of algo is unlikely to produce live trading results as bountiful as the backtest shown. I'm not saying this to be negative; it's great when people share their ideas and experiences, I just want to caution folks who may see a backtest like this and run out to put money at risk like this guy who tried to trade one of the too-good-to-be-true GDAX algos that was posted.

The shorter the timeframe you're trading on, the less accurate backtests are likely to be. Trade execution and risk management becomes as important, if not more important, than your alpha strategy for higher frequency trading scenarios.

I've transitioned my focus from backtesting to live trading in the past year and my whole methodology for algorithm development has evolved. Don't get me wrong--there is still a lot of value in backtesting, particularly on higher timeframe/lower frequency trading/investing, but there are a lot of additional issues in live trading like liquidity, risk management, and operational/technical issues that folks should be aware of.

That said, this is a winner in the backtesting game and thanks for sharing it!

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1 person upvoted this

Jared Broad INVESTOR

QuantConnect | October 2018

Do you mind sharing about your method Nate?

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Nate Betz INVESTOR

October 2018

Hi Jared. Yes, I've been wanting to put together a few slides and hope to have time to do that sometime soon. In a nutshell, there's more of an emphasis on tuning based upon live (or paper) trading data. My signals are based upon predictive models, which I refresh regularly with data captured by my algo(s). A lot of code assets around configuration management, reporting, dynamic trade execution/risk management parameters (e.g. stops & targets). Still very much a WIP and of course all built around LEAN.

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2 people upvoted this

Warren Harding INVESTOR

October 2018

IMHO, it seems best to start with good backtesting results, and concepts that have shown promise, then try and see if you can work the bugs out of when trying to trade it live. Starting with mediocre backtesting and concepts will almost certainly lead to mediocre results at best.

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1 person upvoted this

Petter Hansson INVESTOR

October 2018

Adversaries will fill you at the worst possible time in the short term so the assumption that you will get fills is very optimistic. Heck, HFTs can place an order themselves one tick away from your limit order and use your limit as an exit if their trade goes the wrong way. Running this algo on IB's paper account will most likely show this.

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1 person upvoted this

Petter Hansson INVESTOR

October 2018

I should add, the simplest way to ensure an algo even has a chance of working live on the infrastructure we're given by IB is to watch its average win/loss. IMO anything with <0.5% win/trade has a high chance of being fantasy. This also has implications for how long you need to hold positions.

Btw I never want to say never, if someone somehow got a small trade win/loss algo to work on QC/IB I would be interested to hear how they did it.

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1 person upvoted this

Stefan INVESTOR

StefanJ | October 2018

I run market making on crypto exchanges. Backtesting results are usually off. I am quite sceptical of doing it on traditional markets due to it being dominated by HFTs. You will need to account for adversial selection and control inventory risk carefully. Also for market making you would want to acquire actual trade data for backtesting. You can search on Arxiv for some market making models.

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2 people upvoted this

Hugo Acuna INVESTOR

December 2018

How would you apply this to the forex market?

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Adeel Anwar INVESTOR

August 2019

I’m confused, if it trades 13 times per day, how can someone with 10K use it? PDT-rule and all?

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Boris Sachakov INVESTOR

December 2016

@Jared,

Would you please show me the code how to make it happen. Thanks in advance.

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Boris Sachakov INVESTOR

December 2016

How should I adjust the following formula for 5-min Period?

        public new TimeSpan Period
        {
            get { return QuantConnect.Time.OneDay; }
        }

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Alexandre Catarino INVESTOR

QuantConnect | December 2016

QuantConnect.Time Helper has the following "aliases":

QuantConnect.Time.OneDay = TimeSpan.FromDays(1);
QuantConnect.Time.OneHour = TimeSpan.FromHours(1);
QuantConnect.Time.OneMinute = TimeSpan.FromMinutes(1);
QuantConnect.Time.OneSecond = TimeSpan.FromSeconds(1);
QuantConnect.Time.OneMillisecond = TimeSpan.FromMilliseconds(1);

For other periods, we need to rely on TimeSpan.From* functions.

In this particular case, please adjust it with:

public new TimeSpan Period
{
    get { return TimeSpan.FromMinutes(5); }
}

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Boris Sachakov INVESTOR

December 2016

@Alexandre,

Thanks for your advice. However, the time is still reported by 1-min off: e.g. 11:16 instead of 11:15.

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Jared Broad INVESTOR

QuantConnect | December 2016

    	public override DateTime EndTime
        {
            get { return Time + TimeSpan.FromMinutes(5); }
        }

The default resolution length is 1 minute which makes it appear 1 minute past the start time (which is infact looking into the future since your bars are 5 minutes long). If you set your bar length to 5 minutes (like above) it'll appear on the 5 minute boundary.

Upvote

Alexandre Catarino INVESTOR

QuantConnect | December 2016

Boris, you got a different result, because you removed the part we override EndTime. The complete solution would be:

public override DateTime EndTime
{
    get { return Time + Period; }
    set { Time = value - Period; }
}

public new TimeSpan Period
{
    get { return TimeSpam.FromMinutes(5); }
}

Since Jared's solution is 5 lines shorter, we can considered it better.

Upvote

Boris Sachakov INVESTOR

December 2016

Jared and Alexandre,

Thank you both for your suggestions. Both codes brought the time of events to the end of the bar. However, the time that 'Logs' and 'Trades' after Backtest show is 5 minutes later than the actual event. For example, by looking at the chart, 1st Bearish Closing Marubozu ends at 10:55, whereas the Logs record it at 11:00. Also the 'Trades' show that its price was 1988.50 USD at 11:00; but if you look at the actual records (see the link to Dropbox in GetSource), the price 1988.50 corresponds to Close at 10:55.

Basically, the candlestick pattern and the trade in reality occur at 10:55, but the records (Logs & Trades) show 11:00. Similar situation happens with the rest of patterns.

How can we fix this situation? Thanks in advance

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Jared Broad INVESTOR

QuantConnect | December 2016

That is the intended behaviour; the bars close on the end time (i.e. start + period).

20160104 10.55 ...$1988.50 + 5 minute period bar means the bar is available (closing) at 10.59.9999 and appearing in your algorithm at 11am.

If the bar was available at 10.55 you'd see into the future (as the closing price is at 10.59.99999)

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Boris Sachakov INVESTOR

December 2016

Jared,

Thanks for your response. Still, there is some confusion. But I am beginning to understand the problem, possibly. When I downloaded my data, I set the time of candle correspond to Close. So in my data 10:55 is the bar's close. In your case, probably, the time corresponds to the beginning of the candle.

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Phillip Espina INVESTOR

October 2019

Hi Everyone, Is there a way to do this on python?

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1 person upvoted this

Phillip Espina INVESTOR

October 2019

Hello everyone, I'm using candlesticks to compare if it goes beyond my Upper Bollinger Bands. To get this I need the value of the CandleStick indicator that I am using right? But it only return 1 or -1 as in detecting Bull or Bear patterns. How should I be able to do this using CandleSticks?

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Rene Ordosgoitia INVESTOR

November 2020

Hello guys, I´m trying to take access to the candlestick patterns libraries that are in the GitHub LEAN documentation, but there is not too much information related to how to calculate it in a universe selection approach.

In the next code, I want to use for the moment a simple condition to filter the stock that has an RSI value lower than 40 and the DojiStar built-in CandlestickPatterns library, but I don´t know how to use it in a universe selection approach:

import typing
import QuantConnect.Indicators.CandlestickPatterns
import datetime

class EMAMomentumUniverse(QCAlgorithm):
    
    def Initialize(self):
        self.SetStartDate(2020, 10, 11)
        self.SetEndDate(2020, 11, 11)
        self.SetCash(1000)
        self.UniverseSettings.Resolution = Resolution.Daily
        self.AddUniverse(self.CoarseSelectionFunction) 
        self.averages = { }
        
    
    def CoarseSelectionFunction(self, universe):  
        selected = []
        universe = sorted(universe, key=lambda c: c.DollarVolume, reverse=True)  
        universe = [c for c in universe if (c.Price > 10)][:100]

        for coarse in universe:  
            symbol = coarse.Symbol
            
            if symbol not in self.averages:
                # 1. Call history to get an array of 200 days of history data
                history = self.History(symbol, 200, Resolution.Daily)
                
                #2. Adjust SelectionData to pass in the history result
                self.averages[symbol] = SelectionData(history) 
                
                #here is where im trying to create the bool variable.
                self.hammer = self.CandlestickPatterns.DojiStar(symbol)

            self.averages[symbol].update(self.Time, coarse.AdjustedPrice)
            
            
            
            if  self.averages[symbol].is_ready() and (self.averages[symbol].rsi.Current.Value < 40):
                
                selected.append(symbol)
                
        return selected[:10]
        
    def OnSecuritiesChanged(self, changes):
        
        for security in changes.RemovedSecurities:
            
            self.Liquidate(security.Symbol)
            
        for security in changes.AddedSecurities:
            self.SetHoldings(security.Symbol, 0.10)
            
    
         
            
class SelectionData():
    
    #3. Update the constructor to accept a history array
      def __init__(self, history ):
        
        self.rsi = RelativeStrengthIndex(14)
        
        #4. Loop over the history data and update the indicators
        
        for data in history.itertuples():
            
            self.rsi.Update(data.Index[1], data.close)
            
            
    
      def is_ready(self):
        
        return  self.rsi.IsReady
    
      def update(self, time, price):
        
        self.rsi.Update(time, price)

Thanks a lot for any clue!

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