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53 lines
1.5 KiB
Markdown
53 lines
1.5 KiB
Markdown
# 2022-07-16 15:14:59 make a new conda env for volt
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```sh
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export PROJ=volt
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mamba create -y --name $PROJ python=3.7 -c pytorch -c conda-forge
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# now I want to install pytorch with conda to avoid cuda issues, make sure we get the right version by looking at nvidia-smi to get cuda version. pick the closest
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mamba install -y pytorch torchvision torchaudio cudatoolkit=11.3 -c pytorch
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mamba install botorch -c pytorch -c gpytorch -c conda-forge
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# install kernel
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mamba install -y ipykernel pip ipywidgets
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python -m ipykernel install --user --name $PROJ --display-name $PROJ
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# install this
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pip install -e .
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```
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# data
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instead of using robinhood (US only) we will use tingo
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```py
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import datetime
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import pandas_datareader as pdr
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# restricting to 1996-01-04 00:00:00 2019-06-28 00:00:00
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api_key = os.environ["TIINGO_API_KEY"]
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start = datetime.datetime(1996, 1, 4)
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end = datetime.datetime(2019, 6, 28)
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tickers = ["MSFT", "AAPL", "XOM"] #, "TUR", "RSX", "EWY", "EWS", "VTIP", "TLT", "BWX", "PDBC", "IAU", "VNQI"]
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symbols = pdr.get_data_tiingo(tickers, api_key=api_key, start=start, end=end)
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symbols
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```
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#
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Abandoned after learnign the project more closely. It's not a huge advance over the compared models.
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It looks like that one nice figure was picked as momentum with the right timeframe.
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The cross correlatioon one is interesting. But I would like to a see a timeseies showing what i learnt from each.
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# 2022-07-17 21:07:50
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Tried again
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It seems hard to get it to capture the vol in stock data with a differen't freq.
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It's quite finicky to get each stage correct.
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