semvec/pypi-packages huggingface dataset:
signal-backtester 1.0.4
tiny library for fast backtest on generated signals
Requires Python: >=3.7

![Alt text](logo.png)
![GitHub top language](https://img.shields.io/github/languages/top/xibalbas/signal_backtester)
![GitHub repo size](https://img.shields.io/github/repo-size/xibalbas/signal_backtester)
![PyPI](https://img.shields.io/pypi/v/signal-backtester)

# Signal Backtester
 a small repo Based on  [Backtesting](https://pypi.org/project/Backtesting/) Lib .  
 easiest way to backtest your generated signal

# Quick Start



## installation
```bash
pip install signal-backtester
```

## Usage
```python
from signal_backtester import SignalBacktester

# address of your dataset file 
# columns should include "Open, High, Low, Close, Volume, signal"

backtest = SignalBacktester(
                dataset="/home/xibalbas/sample.csv",
                strategy='two_side_sl_tp_reversed',
                cash=1000,
                commission=0.0005,  # equal 0.05 %
                percent_of_portfolio=99,
                stop_loss=1,
                take_profit=10,
                trailing_stop=3,    # if you are using trailing stop
                time_frame='30m', 
                output_path='.'     # path of result files
            )

backtest.run()
```

# strategy

available strategy to use are :

- `two_side_sl_tp_reversed`
- `two_side_sl_trailing_reversed`
- `one_side_buy_sl_tp`
- `one_side_sell_sl_tp`
- `one_side_buy_sl_trailing`
- `one_side_sell_sl_trailing`


# dataset structure
your data set structure should be like this table 

your buy signals should generate as 2
and your sell signals should generate as 1


you must have this columns in your dataset 
- Date
- Open
- High
- Low
- Close
- Volume
- signal

![Alt text](sample_dataset.png)
dvc-streamlit 0.0.2
Streamlit components for DVC.
Requires Python: >=3.7

# dvc-streamlit-components

Streamlit components for DVC 

Try online demo https://share.streamlit.io/daavoo/dvc-streamlit-components/main/example/example_app.py

Or run locally:

```
git clone git@github.com:iterative/dvc-streamlit-components.git
cd dvc-streamlit-components
pip install -r requirements.txt
streamlit run example/example_app.py
```

## - `dvc_streamlit.dvc_repo`

Gets a `dvc.api.Repo` instance from URL

## - `dvc_streamlit.rev_selector`

Multi select revisions from a `dvc.api.Repo`.

![init](img/init.gif)

## - `dvc_streamlit.dvc_metrics`

Show/Compare [DVC metrics](https://dvc.org/doc/command-reference/metrics) from different revisions.

![metrics](img/metrics.gif)

## - `dvc_streamlit.dvc_params`

Show/Compare [DVC params](https://dvc.org/doc/command-reference/params) from different revisions.

![params](img/params.gif)

## - `dvc_streamlit.plots`

Show/Compare [DVC plots](https://dvc.org/doc/command-reference/plots) from different revisions.

![plots](img/plots.gif)
gamest-retroachievements-plugin 0.1.0
Add status updates from retroachievements to gamest

# Gamest Retroachievements Plugin

This plugin enables [gamest](https://github.com/sopoforic/gamest) to
get status updates from retroachievements.

## Installation

Install with pip:

```
pip install gamest-retroachievements-plugin
```

## License

Copyright (C) 2018  Tracy Poff

This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.

This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.
coolpackets 0.0.2
Packet system for python TCP sockets.

# coolpackets
A python framework for sending and receiving custom packets

This is used for an extension to Ableton Live. I will add features as I need them for that project.

Usage example:

    import socket
    import time
    from typing import Union, Optional, List

    from coolpackets.packet import Packet, Connection, PacketManager


    class MidiPacket(Packet):
        _packet_group = "mrs"
        _alias = "\x00"

        a = int
        b = Union[int, float]
        c_is_optional = Optional[int]

        def on_recv(self):
            print("RECEIVED MidiPacket!", self.a, self.b, self.c_is_optional)
            self.respond(UselessPacket(cool=["1", "2", "3"]))


    class UselessPacket(Packet):
        _alias = "useless"
        # _dump = False

        cool = List[str]

        def on_recv(self):
            print("RECEIVED UselessPacket!")

        def encode(self) -> bytes:
            return b""

        @classmethod
        def decode(cls, data: bytes):
            return cls(cool=["nice", "cool"])


    PacketManager.dump_packets("./packet_dumps/demo_packet_dump.py")


    # setup
    srv = socket.socket()
    srv.bind(('', 9056))
    srv.listen(1)

    cli = socket.socket()
    cli.connect(('localhost', 9056))
    peer, addr = srv.accept()

    conn1 = Connection(cli)
    conn2 = Connection(peer, addr, {'mrs'})

    # main logic
    pck = MidiPacket(a=3, b=7.4)
    conn1.send(pck, lambda p: print(f"RECEIVED RESPONSE: {p}"))

    # time to process packets. Usually we wouldn't just close the sockets
    time.sleep(.1)

    # cleanup
    conn1.close()
    conn2.close()
    srv.close()

    print("Done")
codecraft-tic-tac-toe-mock-game 0.2.2
This package will allow you to get familiar with the API used in the Codecraft Works Python Tic-Tac-Toe Game
Requires Python: >=3.6

# Codecraft Works Mock Python Tic-Tac-Toe Game

This package will allow you to get familiar with the API used in the
[Codecraft Works Python Tic-Tac-Toe Game](https://app.codecraftworks.com/games/tictactoe)
TESLAforST 1.2.2
TESLA: Deciphering tumor ecosystems at super-resolution from spatial transcriptomics
Requires Python: >=3.6

# TESLA

## TESLA: Deciphering tumor ecosystems at super-resolution from spatial transcriptomics

### Jian Hu,*, Kyle Coleman, Edward B. Lee, Humam Kadara, Linghua Wang,*,  Mingyao Li,*

TESLA is a machine learning framework for multi-level tissue annotation with pixel-level resolution in ST. TESLA integrates histological information with gene expression to annotate heterogeneous immune and tumor cells directly on the histology image. TESLA further detects unique Tumor Microenvironment (TME) features such as tertiary lymphoid structures and differential transcriptome programs in the core or edge of a tumor, which represent a promising avenue for understanding the spatial architecture of the TME. Although we illustrated the applications in cancer, TESLA can also be applied to other diseases. 
For more info, please go to: 
https://github.com/jianhuupenn/TESLA