finance

Describe your context
Please provide us your environment, so we can easily reproduce the issue.

• replace the result of pip list | grep dash below

dash                      2.0.0
dash-bootstrap-components 1.0.0

• if frontend related, tell us your Browser, Version and OS

  • OS: [e.g. iOS] Windows
  • Browser [e.g. chrome, safari]: Chrome 96.0x, Edge 96.0x, Firefox

🐛 Bug Description

Under MLP implementation there are several variables that need to be initialized. Such as loss, lr, lr_decay, lr_decay_steps, optimizer. However, it seems that the variables lr_decay andlr_decay_steps are indeed being initialized but are not being used at any point in the code.

Expected Behavior

We can update the trigger price of limit if touched orders with an UpdateTriggerPrice method, just like we can use UpdateTag, UpdateQuantity, UpdateLimitPrice, `UpdateS

The preprocessing is very slow in FinRL. The Nmba allow GPU parallelization with C speed for process array data. Some high speed trading framework like vectorbt use it.

Is possible to take experience from vectorbt and use Numba to speed up FinRL?

Reading currencies, alphavantage returns a greeting note ("welcome") and this note raises an error in alphavantage.py line 363.

            elif "Note" in json_response and self.treat_info_as_error:
                raise ValueError(json_response["Note"])

For this reason, alphavantage does not work in home assistant.

Heston model has accurate density approximations for European option prices, which are of interest.

The module implementing this method should live under tf_quant_finance/volatility/heston_approximation.py. It should support both European option puts and calls approximations. Tests should be in heston_approximation_test.py in the same folder.

According to @rspadim, functions in entropy.py could use numpy array instead of strings, as it's better to numba.

Guide on how to implement this is available in the comments in PR #311 .

Which version are you running? The latest version is on Github. Pip is for major releases.

import pandas_ta as ta
print(ta.version)
0.3.64b0

Indicator addition to match another broker

Hi,
So kind of new around here, and I've been looking for indicator to match certain behavior of VWAP standard deviation bands like the bands that appears in Interactive brokers management