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cleaned up gitignore and edited readme #11

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cleaned up gitignore and edited readme
fengtality Apr 27, 2023
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change default interval to 1m
fengtality Apr 27, 2023
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136 changes: 4 additions & 132 deletions .gitignore
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
**/__pycache__/

# C extensions
*.

*.ipynb

secrets.toml
.idea/*

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# IPython
profile_default/
ipython_config.py

# pyenv
.python-version

# pipenv
# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
# However, in case of collaboration, if having platform-specific dependencies or dependencies
# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

# SageMath parsed files
*.sage.py

# Environments
.env
.venv
env/
venv/
ENV/
env.bak/
venv.bak/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.dmypy.json
dmypy.json

# Pyre type checker
.pyre/
# DB files
/data
**/*.sqlite
56 changes: 33 additions & 23 deletions README.md
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# Hummingbot StreamLit Apps
# Hummingbot Dashboards

Collection of Hummingbot-related data apps and dashboards, built using [StreamLit](https://streamlit.io/).
Collection of Hummingbot-related dashboards, built using [StreamLit](https://streamlit.io/).

This repository is maintained by Hummingbot Foundation as a companion for users of [Hummingbot](https://github.com/hummingbot/hummingbot), the open source framework for building high-frequency crypto trading bots.

### Getting Started Video

Watch this video to understand how it works:
https://www.loom.com/share/72d05bcbaf4048a399e3f9247d756a63

### Deployed Apps

See an example of a live, deployed dashboard:
https://hummingbot-streamlit-apps-main-jnja50.streamlit.app

## Current Data Apps
## Dashboards

Here are the current data apps in the collection:
Here are the current dashboards in the collection:

### XE Token Analyzer
### 🚀 Strategy Performance (WIP)

Visualize the bid-ask spread and volume of different tokens across the crypto exchange landscape. This app is most helpful for Hummingbot users running the [Cross-Exchange Market Making](https://hummingbot.org/strategies/cross-exchange-market-making/) and [Arbitrage](https://hummingbot.org/strategies/arbitrage/) strategies.
Dashboard that helps you analyze the performance of a running Hummingbot instance

### Hummingbot DB
### 🧙 XE Token Analyzer

Drop a SQLite trades file for a specific strategy configuration, so that you can inspect and analyze the trade data.
Dashboard that helps you visualize the bid-ask spread and volume of different tokens across the crypto exchange landscape.

### TVL vs MCAP Analysis
This app is most helpful for Hummingbot users running the [Cross-Exchange Market Making](https://hummingbot.org/strategies/cross-exchange-market-making/) and [Arbitrage](https://hummingbot.org/strategies/arbitrage/) strategies.

Easily compare various DeFi protocols based on their market capitalization and total value locked, using DeFiLlama data.
### 🧳 Hummingbot DB

Inspect and analyze the orders and trades data contained in a SQLite database for a strategy or script.

**How to find**: These files are located in the `/data` folder in Hummingbot, and are named `<strategy_name>.sqlite`.
These files are located in the `/data` folder in Hummingbot, and are named `<strategy_name>.sqlite`.

### 🦉 TVL vs MCAP Analysis

Easily compare various DeFi protocols based on their market capitalization and total value locked, using DeFiLlama data.

### Data
### 📋 Data

Reference data for the various apps this collection.

## Installation

1. Install Anaconda or Miniconda
1. Install [Anaconda](https://www.anaconda.com/) or [Miniconda](https://docs.conda.io/en/latest/miniconda.html)

2. Clone this repo and navigate to the created directory
```
```bash
git clone https://github.com/hummingbot/streamlit-apps.git
cd streamlit-apps
```

3. Run this command to create a `conda` environment and install dependencies
```
```bash
conda env create -f environment.yml
```

4. Activate the environment
```
```bash
conda activate streamlit-apps
```

5. Run the app
```
```bash
streamlit run main.py
```

For more info, see instructions located at https://docs.streamlit.io/library/get-started/installation.
6. Create a symlink to your Hummingbot `/data` directory
```bash
# replace `/path/to/hummingbot` with the actual path
ln -s /path/to/hummingbot/data data
```

For more info about Streamlit installation, see the instructions located at https://docs.streamlit.io/library/get-started/installation.

## Contributions

We welcome new data apps, bug fixes, and improvements by the community!

To submit a contribution, issue a pull request and follow the general guidelines listed at https://hummingbot.org/developers/contributions.
To submit a contribution, issue a pull request, following the guidelines listed at https://hummingbot.org/developers/contributions.
2 changes: 1 addition & 1 deletion pages/2_🚀_Strategy_Performance.py
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Expand Up @@ -74,7 +74,7 @@ def get_ohlc(trading_pair: str, exchange: str, interval: str, start_timestamp: i

c1, c2 = st.columns([1, 5])
with c1:
interval = st.selectbox("Candles Interval:", intervals.keys(), index=3)
interval = st.selectbox("Candles Interval:", intervals.keys(), index=0)

date_array = pd.date_range(start=strategy_data.start_time, end=strategy_data.end_time, periods=60)
ohlc_extra_time = 60
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