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Algorithmic Trading – Machine Learning & Quant Strategies Course with Python

On this complete course on algorithmic buying and selling, you’ll find out about three cutting-edge buying and selling methods to boost your monetary toolkit. Within the first module, you will discover the Unsupervised Learning Trading Technique, using S&P 500 shares knowledge to grasp options, indicators, and portfolio optimization.

Subsequent, you will leverage the ability of social media with the Twitter Sentiment Investing Technique, rating NASDAQ shares based mostly on engagement and evaluating efficiency in opposition to the QQQ return. Lastly, the Intraday Technique will introduce you to the GARCH mannequin, combining it with technical indicators to seize each each day and intraday alerts for potential profitable positions.

✏️ Course developed by @lachone_

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⭐️ Contents ⭐️
0:00:00 – Algorithmic Trading & Machine Learning Fundamentals
0:15:25 – Constructing An Unsupervised Learning Trading Technique
2:05:08 – Constructing A Twitter Sentiment Investing Technique
2:28:08 – Constructing An Intraday Technique Utilizing GARCH Mannequin

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28 thoughts on “Algorithmic Trading – Machine Learning & Quant Strategies Course with Python

  • Can you clarify whether survivorship bias occurs when including data from a company that has gone bankrupt, or when excluding data from a company that has faced bankruptcy?

  • Thanks for sharing! For the 1st project, I wonder how the clustering is different from selecting top RSI stocks in each month given RSI is not normalized? Or is it just for demoing the possibility of using the clustering algo?

  • Hi, very good video and good job! But the daily signal volatility definition needs to be more conservative i.e. 1 std and not 1.5 std, due to the high CV of the signal ( to improve the results)

  • Great tutorial. Stuck on macd function though. Doesn't seem to work as is.def compute_macd(close):
    macd = pandas_ta.macd(close=close, length=20).iloc[:,0]
    return macd.sub(macd.mean()).div(macd.std())

  • For anyone having issue at 36 minutes concerning the .resample() method, here is a solution I have come up with, there might be better workarounds but at least this one enables us to continue:

    last_cols = [c for c in df.columns.unique(0) if c not in ['dollar_volume', 'volume', 'open',

    'high', 'low', 'close']]

    test = df.unstack('ticker')['dollar_volume']

    test.index = pd.to_datetime(test.index)

    test2 = df.unstack()[last_cols]

    test2.index = pd.to_datetime(test2.index)

    data = (pd.concat([test.resample('M').mean().stack('ticker').to_frame('dollar_volume'),




    be aware however that this method drops certain stocks, I believe it's because if any of the tickers don't have data for the entire period of time covered by the DataFrame, they will be dropped during the resampling process.

  • I have trading model that’s proven and I’ve broken down. Would there be anyone with quant experience willing to take my parameters and incorporate into a systematic trading algorithm?

  • I'm getting this error in the concatenation part:

    last_cols = [c for c in df.columns.unique(0) if c not in ['dollar_volume', 'volume', 'open',

    'high', 'low', 'close']]

    data = (pd.concat([df.unstack('ticker')['dollar_volume'].resample('M').mean().stack('ticker').to_frame('dollar_volume'),




    TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'Index'

  • I got an error in the following line of code:
    KeyError: 'Level ticker not found'
    Anybody can help?

  • I am amazed to see algo videos rarely on YouTube. I appreciate your work. Could you please make a similar kind of video on price action algo trading with Wyckoff multi-timeframe analysis, without using any indicators, and generate trades live through your machine learning or AI models?

    Many people are eagerly waiting to see these kinds of videos, but no one has ever made any price action trading videos. I request you to do so at the earliest.

    Thank you,

    Muni Babu

  • Well, good luck making this profitable…. 😄There are exactly zero profitable strategies out in the open like this, for the simple reason that everyone who actually comes up with something profitable will keep is to themselves so as not to lose their edge on the market.

  • Great class. Really appreciate you sharing.
    I am unable to get past the error that you ran into in the video when calculating MACD.

    def compute_macd(close):
    macd = pandas_ta.macd(close=close, length=20).iloc[:,0]
    return macd.sub(macd.mean()).div(macd.std())

    df['macd'] = df.groupby(level=1, group_keys=False)['adj close'].apply(compute_macd)

    Any suggestions would be appreciated.
    Thanks again

  • this thing is for students of python code. Not traders. But good video non the less

  • If you are concerned about stocks that fall out of tne S&P500 (survivorship bias), you could have just checked the top 150 against those that were always present in the S&P500 and then dropped those that werent from your dataset. (Or set some cut-off where it had to be present at least x% of the time)

  • I am getting error in this line df.unstack('ticker')['dollar_volume'].resample('M') can anyone tell me how to solve this error?

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