View high_low_spread_estimator.py
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# 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. |
View turing-ad-benchmark.jl
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# 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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View rdiff_test.jl
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using ReverseDiff | |
f(x) = maximum(x;dims=1) | |
if abspath(PROGRAM_FILE) == @__FILE__ | |
x = rand(3, 3) | |
println(x) | |
println(ReverseDiff.gradient(sum∘f, x)) | |
end |
View getsize.py
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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): |
View plot_kernels.py
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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() |