lastancientone / stock_analysis_for_quant Goto Github PK

First of all, thanks for this large compendium of technical indicators, it's very useful.

I'm trying to run RSI.ipynb and when opened and ran in Colab, and I've got the following error:

TypeError: dtype '<class 'datetime.date'>' not understood

in the following cell:

from matplotlib import dates as mdates
import datetime as dt

dfc = df.copy()
dfc['VolumePositive'] = dfc['Open'] < dfc['Adj Close']
#dfc = dfc.dropna()
dfc = dfc.reset_index()
dfc['Date'] = mdates.date2num(dfc['Date'].astype(dt.date)) // <!-- Issue with this line.
dfc.head()

My questions:

• What version of datetime you've used to run this notebook?
• What do you suggest to fix it? Downgrading datetime to a different version, or the code should fixed forward?

Colab: https://colab.research.google.com/github/LastAncientOne/Stock_Analysis_For_Quant/blob/master/Python_Stock/Technical_Indicators/RSI.ipynb

Thanks.

Hello @LastAncientOne , you have done really great work in maintaining this repository.
I was wondering if you can share any reference or any reading item so we can understand how to analyse heat map generated by python TA notebook.
As a beginner, the amount of data seems intimidating :)

Hi all, it would be useful to also have the Trade Intensity Indicator (TII) in the Technical_Analysis.xlsx spredsheet, do you think it is possible to implement it? many tks

My name is Luis, I'm a big-data machine-learning developer, I'm a fan of your work, and I usually check your updates.

I was afraid that my savings would be eaten by inflation. I have created a powerful tool that based on past technical patterns (volatility, moving averages, statistics, trends, candlesticks, support and resistance, stock index indicators).
All the ones you know (RSI, MACD, STOCH, Bolinger Bands, SMA, DEMARK, Japanese candlesticks, ichimoku, fibonacci, williansR, balance of power, murrey math, etc) and more than 200 others.

The tool creates prediction models of correct trading points (buy signal and sell signal, every stock is good traded in time and direction).
For this I have used big data tools like pandas python, stock market libraries like: tablib, TAcharts ,pandas_ta... For data collection and calculation.
And powerful machine-learning libraries such as: Sklearn.RandomForest , Sklearn.GradientBoosting, XGBoost, Google TensorFlow and Google TensorFlow LSTM.

With the models trained with the selection of the best technical indicators, the tool is able to predict trading points (where to buy, where to sell) and send real-time alerts to Telegram or Mail. The points are calculated based on the learning of the correct trading points of the last 2 years (including the change to bear market after the rate hike).

I think it could be useful to you, to improve, I would like to share it with you, and if you are interested in improving and collaborating I am also willing, and if not file it in the box.

You have done a great job including almost everything there is. I would love to help you with this repo and include Options and options trading strategies in this wonderful repo. @LastAncientOne

Wondering if you have done work on Portfolio Risk and Return Attribution. The most widely used is the multi period Brinson model.

Thanks.

wind 10
python 3.10

1).pycharm 2022.2:
-note: This error originates from a subprocess, and is likely not a problem with pip.
-error: legacy-install-failure

2).win10 CMD
-note: This error originates from a subprocess, and is likely not a problem with pip.
-error: legacy-install-failure
× Encountered error while trying to install package.
╰─> pyflux

note: This is an issue with the package mentioned above, not pip.
hint: See above for output from the failure.

1. For the "lazy portfolio" strategies, what are the fastest way to do backtesting and analysis? Lists like This, This, and This exists https://github.com/LastAncientOne/Stock_Analysis_For_Quant/blob/master/README.md#list-of-portfolio-strategies
2. Are all of the risk factors in QuantStats essential, or are some of them correlated enough such that some are redundant? https://github.com/LastAncientOne/Stock_Analysis_For_Quant/blob/master/README.md#list-of-risk-adjusted-returns-ratios-measurement
3. Are visualization a good idea for explaining stocks like PCA, Temporal Heatmaps, Correlative Networks, and Other Tools?
4. How does the state of the market correlate with risk and return? https://gmarti.gitlab.io/qfin/2020/02/03/sp500-sharpe-vs-corrmats.html