Algorithmic Trading - Machine Learning & Quant Strategies Course with Python 

well there are some making money, else there won't be big hedge funds

@@nobodyknows3560 the big hedge funds won't share their opinions clearly though, only vaguely

he failed

Turns out I can't even get the arch package to work. After pip install arch, can't even import it in Jupyter NB. 🤷🏻‍♂️

GARCH is only daunting if you don't understand it.

this is correct. upvoted

Yep, the clustering part was only to make the "algo" more appealing. I thought he would have actually analyze each cluster trying to find useful features...

This example is for intra-day and day-trading as specified at the beginning of the video. After a quick research the best indicator for this kind of trading is the RSi. Due to this the main feature chosen by him is the RSI because it is the best indicator for this kind of trading strategies.

Based comment

@@TyTy-gm8yb RSI is not the best indicator. It shows basically what has already happened in the past. It is by no means usable in a forecast model. Should you want to have useful data you could try using Money Flow, or simple price action with Volume. Or if possible, you can create a model using real time agression.

Can you explain more about this?

Are you sure successful quant funds really trade Wyckoff, or what's worth - Fibonacci retracements? 😅

@@flexorx what ever they do, price action plays right, that enough for us...😏

Thank you mate, cheers!

Bulgarians don't play around, you know you stopped caring what people think when you don't code in dark mode.

Lol. It's about beating the market.

Are u a computer scientist?

Are u a computer scientist?

Good day , I would like to work with you on FOREX trading strategies

Please 🙏 🥺 do same for real estate

ugliness of trading in market??

He collapses it to the end of the month. The number your swing consist of days before

​@@loganbishop4619 upon further review, the normalization is applied per security using the entire time series so I think this is an issue raised by @johnsmith-qc8bud ... wouldn't the correct way to do this would be to apply normalization on a rolling basis?

Are you following quant?

your quant journy will end soon when you realize it is just not possible, at all, you live in a dream if you think it is. There are just a small number of large organizations who can do this efficiently, it requires much more than a laptop.

NVM, the logic is reversed in the later code

usually the log returns supposed to follow a normal distribution more closely. well it still doesn't. you would need tick data and use volume instead of time to get really close to a normal distribution.

Hey,since you are a former quant ,I found it really intriguing .I'm currently working on a script for currency analysis and since I'm relatively new to the quant world , I'd love to connect and chat about it .Maybe we could share some insights and experiences .Let me know if you're open to it. Cheers!

@@thenoblegodplease let me know when he messages you

Is there trading based freelancing in India?

I think he means portfolio management.

Don't bother with Indian clients on freelancing website for trading. They suck your time and not worth it. And trading on nse is no different than any other market if you get the fundamentals right.

I am on the same page with you. Have you got any response regarding that?

after a while, I figured this was the case also

What's your expertise? I'm pretty sure you don't have even 1% the level of expertise of this guy.

I am getting the same error and cannot find a workaround.

yes

Good day , I would like to work with you on FOREX trading strategies

Exactly, but it still does giv eyou some insight on how to analyze data. The real thing is to come up with something that would work

But the Total Video length as shown above is 2:59 hrs only

Where is the additional hours from?

bro is lying

Very Funny.

change 'index' to "Ticker".

@@hammam92 pls tell where

@@pranavsingh1947 bro give me the code that gives the error.

@@hammam92 .... returns_dataframe = np.log(new_df['Adj Close']).diff() portfolio_df = pd.DataFrame() for start_date in fixed_dates.keys(): try: end_date = (pd.to_datetime(start_date)+pd.offsets.MonthEnd(0)).strftime('%Y-%m-%d') cols = fixed_dates[start_date] optimization_start_date = (pd.to_datetime(start_date)-pd.DateOffset(months=12)).strftime('%Y-%m-%d') optimization_end_date = (pd.to_datetime(start_date)-pd.DateOffset(days=1)).strftime('%Y-%m-%d') optimization_df = new_df[optimization_start_date:optimization_end_date]['Adj Close'][cols] success = False try: weights = optimize_weights(prices=optimization_df, lower_bound=round(1/(len(optimization_df.columns)*2),3)) weights = pd.DataFrame(weights, index=pd.Series(0)) success = True except: print(f'Max Sharpe Optimization failed for {start_date}, Continuing with Equal-Weights') if success==False: weights = pd.DataFrame([1/len(optimization_df.columns) for i in range(len(optimization_df.columns))], index=optimization_df.columns.tolist(), columns=pd.Series(0)).T temp_df = returns_dataframe[start_date:end_date] temp_df = temp_df.stack().to_frame('return').reset_index(level=0)\ .merge(weights.stack().to_frame('weight').reset_index(level=0, drop=True), left_index=True, right_index=True)\ .reset_index().set_index(['Date', 'index']).unstack().stack() temp_df.index.names = ['date', 'ticker'] temp_df['weighted_return'] = temp_df['return']*temp_df['weight'] temp_df = temp_df.groupby(level=0)['weighted_return'].sum().to_frame('Strategy Return') portfolio_df = pd.concat([portfolio_df, temp_df], axis=0) except Exception as e: print(e) portfolio_df = portfolio_df.drop_duplicates() portfolio_df ... will print ... "None of ['index'] are in the columns"

Good day , I would like to work with you on FOREX trading strategies

same error

same here, how did you solve it?

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'), # df.unstack()[last_cols].resample('M').last().stack('ticker')], # axis=1)).dropna() #df.unstack(['ticker','date'])['dollar_volume'].reset_index('date').set_index('date').resample('M').mean() data = (pd.concat([df.unstack(['ticker'])['dollar_volume'].reset_index('date').set_index('date').resample('M').mean().stack('ticker').to_frame('dollar_volume'), df.unstack()[last_cols].reset_index('date').set_index('date').resample('M').last().stack('ticker')], axis=1)).dropna() data

Let me know if you do get it ;)