Finance

Mean Variance Optimization (MVO) is a well-established but rigorous technique used in managing financial portfolios. However, the covariance calculated by MVO often contains estimation errors. To address this, the Ledoit-Wolf Shrinkage operator is frequently recommended. This approach is used as a baseline in this paper(https://icaps23.icaps-conference.org/papers/finplan/FinPlan23_paper_4.pdf) First, I will explain Mean Variance Optimization, and then I will discuss the Ledoit-Wolf Shrinkage operator, which is an improved version of MVO. Minimize risks(variances) by given returns import numpy as np import yfinance as yf import scipy....

Deep Reinforcement Learning for Optimal Portfolio Allocation https://icaps23.icaps-conference.org/papers/finplan/FinPlan23_paper_4.pdf 60 days look back in MVO MVO

Plotting Financial Data with Market Color Overrides import polars as pl import mplfinance as mpf df = pl.read_csv("df.csv", try_parse_dates=True) df = df.to_pandas() df = df.set_index("datetime") # Plot the data without market color overrides mpf.plot(df,volume=False,type='candle',style='yahoo',figscale=1.4) # Initialize the MCOverrides column with the base market colors (black and white) mc = mpf.make_marketcolors(base_mpf_style='classic') # Get the target column target = df["target"] # Initialize the column with all values set to `mc` df['MCOverrides'] = [mc] * len(df) # Override the market colors based on the target value df....

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hugginface stable diffusion https://huggingface.co/spaces/stabilityai/stable-diffusion my prompt is Sepia renaissance “title of the article” This cover image is also generated by AI. Prompt is polar bear sepia my prompt is Sepia renaissance “title of the article” This cover image is also generated by AI. Prompt is polar bear sepia