Merge pull request #4 from deerfieldgreen/3-fixing-code-merge-clobber

3 fixing code merge clobber

.gitignore

@@ -0,0 +1,166 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+
+# MacOS
+.DS_Store
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+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/
+cover/
+
+# 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
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .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
+
+# poetry
+#   Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
+#   This is especially recommended for binary packages to ensure reproducibility, and is more
+#   commonly ignored for libraries.
+#   https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
+#poetry.lock
+
+# pdm
+#   Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
+#pdm.lock
+#   pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
+#   in version control.
+#   https://pdm.fming.dev/latest/usage/project/#working-with-version-control
+.pdm.toml
+.pdm-python
+.pdm-build/
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
+__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/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+
+# PyCharm
+#  JetBrains specific template is maintained in a separate JetBrains.gitignore that can
+#  be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
+#  and can be added to the global gitignore or merged into this file.  For a more nuclear
+#  option (not recommended) you can uncomment the following to ignore the entire idea folder.
+#.idea/

README.md

@@ -1,7 +1,77 @@
 # daily-levels-panting
-Updates daily range levels and save the pine script in daily_range_pine.txt
-We need to create a pine script for TradingView to "paint" the levels each day, to automated creating 8 levels by 8 symbols.
-In tradingview, you add the script to the Pine Editor, Save and Add to Chart, as an indicator. 
+
+- Updates daily range levels and save the pine script in daily_range_pine.txt
+- We need to create a pine script for TradingView to "paint" the levels each day, to automated creating 8 levels by 8 symbols.
+- In tradingview, you add the script to the Pine Editor, Save and Add to Chart, as an indicator. 
+
+## How to use Daily Levels
+- COMPLETE ME 
+
+
+## What each level means
+- that's a long story ...
+- There are 5 prediction levels and 2 straddle levels
+- There are a set of Straddle Percent SMAs (10-50)
+- You can see how these are presented in the screenshot below.
+- this code snippet is from quantconnect/main.py (line #450)
+```
+
+                    prediction_dict = {}
+                    prediction_dict["ticker"] = ticker
+                    prediction_dict["datetime"] = str(self.Time)
+                    prediction_dict["spot_price"] = pred_df['price'].iloc[0]
+                    prediction_dict["straddle_value"] = pred_df['straddle_front_premium'].iloc[0]
+                    prediction_dict["straddle_pct_value"] = pred_df['straddle_front_premium_pct'].iloc[0]
+                    prediction_dict["past_straddle_value"] = model_df['straddle_front_premium'].mean()
+                    prediction_dict["past_straddle_pct_value"] = model_df['straddle_front_premium_pct'].mean()
+                    prediction_dict["past_straddle_value_std"] = model_df['straddle_front_premium'].std()
+                    prediction_dict["past_straddle_pct_value_std"] = model_df['straddle_front_premium_pct'].std()
+                    prediction_dict["upper_value"] = prediction_dict["spot_price"] + prediction_dict["straddle_value"]
+                    prediction_dict["lower_value"] = prediction_dict["spot_price"] - prediction_dict["straddle_value"]
+                    prediction_dict["straddle_pct_sma10"] = data_df['straddle_front_premium_pct'].tail(10).mean()
+                    prediction_dict["straddle_pct_sma20"] = data_df['straddle_front_premium_pct'].tail(20).mean()
+                    prediction_dict["straddle_pct_sma30"] = data_df['straddle_front_premium_pct'].tail(30).mean()
+                    prediction_dict["straddle_pct_sma50"] = data_df['straddle_front_premium_pct'].tail(50).mean()
+
+                    implied_vol_cur = yearly_data_df.tail(1)['implied_vol'].iloc[0]
+                    implied_vol_min = yearly_data_df['implied_vol'].min()
+                    implied_vol_max = yearly_data_df['implied_vol'].max()
+                    prediction_dict["implied_vol_rank"] = (implied_vol_cur - implied_vol_min) / (implied_vol_max - implied_vol_min)
+                    prediction_dict["implied_vol_percentile"] = (yearly_data_df['implied_vol'] < implied_vol_cur).mean()
+
+
+
+
+```
+## What's the Universe?
+- you can find this in code: quantconnect/config.py
+```
+## General Settings
+general_setting = {
+   "tickers": {
+        "QQQ": {"type": "equity"},
+        "SPY": {"type": "equity"},
+        "SPX": {"type": "equity"},
+        "NVDA": {"type": "equity"},
+        "TSLA": {"type": "equity"},
+        "BABA": {"type": "equity"},
+        "META": {"type": "equity"},
+        "AMZN": {"type": "equity"},
+        "TLT": {"type": "equity"},
+        "GLD": {"type": "equity"},
+        "SLV": {"type": "equity"},
+        "EWZ": {"type": "equity"},
+        "IWM": {"type": "equity"}
+    },
+```
+
+## Stock Splits
+- we needed to account for stock splits in the universe set. the price changes based on the ratio of split 2:1 or 4:1, whatever. 
+- The quantconnect code pulls from another github repository () for this list, back 5 years (60 months * 30.5 days to be exact)
+
+``` 
+data = self.download("https://raw.githubusercontent.com/deerfieldgreen/yfinance-scaling-system/refs/heads/main/data/stock_splits_data.csv")
+```
 
 ## Flow
 1) Receives webhook from QC
@@ -10,3 +80,13 @@ In tradingview, you add the script to the Pine Editor, Save and Add to Chart, as
 
 
 
+## Code layout
+- QuantConnect 
+- Docker container code 
+- WebHook running in GCP CloudRun
+
+
+
+## TradingView Screenshot
+
+![Screenshot of TradingView in $META.](images/META_2024-10-31_22-52-47_a1298.png)