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Krishna Vishal krishvishal

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krishvishal / high_low_spread_estimator.py
Created February 20, 2022 02:48 — forked from nicklatin/high_low_spread_estimator.py
Computes the high-low spread estimator, an estimate of bid-offer spreads, a measure of liquidity risk. See Corwin & Schultz (2011) for details: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1106193
View high_low_spread_estimator.py
# high-low spread estimator (hlse)
def hlse(ohlc_df, frequency='daily'):
"""
Computes the high-low spread estimator, an estimate of bid-offer spreads, a measure of liquidity risk.
See Corwin & Schultz (2011) for details: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1106193
Parameters
----------
ohlc_df: DataFrame
DataFrame with DatetimeIndex and Open, High, Low and Close (OHLC) prices from which to compute the high-low spread estimates.
@krishvishal
krishvishal / turing-ad-benchmark.jl
Created August 30, 2021 15:43 — forked from torfjelde/turing-ad-benchmark.jl
Convenient code for benchmarking different AD-backends on a particular Turing.jl model.
View turing-ad-benchmark.jl
# Use packages to ensure that we trigger Requires.jl.
using Zygote: Zygote
using ReverseDiff: ReverseDiff
using ForwardDiff: ForwardDiff
using Tracker: Tracker
using Memoization: Memoization # used for ReverseDiff.jl cache.
using Turing.Core: ForwardDiffAD, ReverseDiffAD, TrackerAD, ZygoteAD, CHUNKSIZE
const DEFAULT_ADBACKENDS = [
View HighDimGaussian.ipynb
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View DeepLOB.ipynb
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@krishvishal
krishvishal / rdiff_test.jl
Created August 11, 2020 11:01
This file has a function f(x) and I use ReverseDiff to calculate gradient of that function. If ReveseDiff is really the problem, this shouldn't run successfully.
View rdiff_test.jl
using ReverseDiff
f(x) = maximum(x;dims=1)
if abspath(PROGRAM_FILE) == @__FILE__
x = rand(3, 3)
println(x)
println(ReverseDiff.gradient(sumf, x))
end
@krishvishal
krishvishal / getsize.py
Created November 14, 2019 09:40
Size of a python object even with recursive encapsulation
View getsize.py
def getsize(obj_0):
"""Recursively iterate to sum size of object & members."""
_seen_ids = set()
def inner(obj):
obj_id = id(obj)
if obj_id in _seen_ids:
return 0
_seen_ids.add(obj_id)
size = sys.getsizeof(obj)
if isinstance(obj, zero_depth_bases):
@krishvishal
krishvishal / plot_kernels.py
Last active October 22, 2022 18:27
Visualize weights in pytorch
View plot_kernels.py
from model import Net
from trainer import Trainer
import torch
from torch import nn
from matplotlib import pyplot as plt
model = Net()
ckpt = torch.load('path_to_checkpoint')
model.load_state_dict(ckpt['state_dict'])
filter = model.conv1.weight.data.numpy()