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Crypto & Stock Price Prediction with Various Neural Networks

Crypto & Stock Price Prediction and Forecasting Toolkit.

Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.

Update Log:

→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:

pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:

  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0

LSTM

Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.

CatBoost

Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.

Prophet

Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.

XGBoost

XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.

LightGBM

LightGBM is a gradient boosting framework that uses tree based learning algorithms

Planned Features:

  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor

Important Notes:

  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.

Old Files(For Reference):

These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:

  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.

Screenshots

Screenshot1Screenshot2Screenshot3Screenshot4

Contributing:

Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project. Let me know if you'd like any other modifications!

About

Crypto & Stock* price prediction with regression models.

Topics

aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression

Resources

Readme
Activity

Stars

14 stars

Watchers

2 watching

Forks

4 forks
Report repository

Releases

Packages

Used by

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Crypto & Stock Price Prediction with Various Neural Networks
Crypto & Stock Price Prediction and Forecasting Toolkit.
Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.
Update Log:
→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:
pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:
  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0
LSTM
Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.
CatBoost
Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.
Prophet
Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms
Planned Features:
  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor
Important Notes:
  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.
Old Files(For Reference):
These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:
  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.
Screenshots
Screenshot1Screenshot2Screenshot3Screenshot4
Contributing:
Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project.
Let me know if you'd like any other modifications!
About
Crypto & Stock* price prediction with regression models.
Topics
aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression
Resources
Readme
Activity
Stars
14 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Crypto & Stock Price Prediction with Various Neural Networks
Crypto & Stock Price Prediction and Forecasting Toolkit.
Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.
Update Log:
→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:
pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:
  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0
LSTM
Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.
CatBoost
Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.
Prophet
Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms
Planned Features:
  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor
Important Notes:
  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.
Old Files(For Reference):
These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:
  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.
Screenshots
Screenshot1Screenshot2Screenshot3Screenshot4
Contributing:
Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project.
Let me know if you'd like any other modifications!
About
Crypto & Stock* price prediction with regression models.
Topics
aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression
Resources
Readme
Activity
Stars
14 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Crypto & Stock Price Prediction with Various Neural Networks
Crypto & Stock Price Prediction and Forecasting Toolkit.
Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.
Update Log:
→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:
pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:
  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0
LSTM
Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.
CatBoost
Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.
Prophet
Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms
Planned Features:
  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor
Important Notes:
  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.
Old Files(For Reference):
These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:
  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.
Screenshots
Screenshot1Screenshot2Screenshot3Screenshot4
Contributing:
Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project.
Let me know if you'd like any other modifications!
About
Crypto & Stock* price prediction with regression models.
Topics
aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression
Resources
Readme
Activity
Stars
14 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Crypto & Stock Price Prediction with Various Neural Networks
Crypto & Stock Price Prediction and Forecasting Toolkit.
Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.
Update Log:
→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:
pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:
  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0
LSTM
Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.
CatBoost
Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.
Prophet
Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms
Planned Features:
  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor
Important Notes:
  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.
Old Files(For Reference):
These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:
  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.
Screenshots
Screenshot1Screenshot2Screenshot3Screenshot4
Contributing:
Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project.
Let me know if you'd like any other modifications!
About
Crypto & Stock* price prediction with regression models.
Topics
aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression
Resources
Readme
Activity
Stars
14 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Crypto & Stock Price Prediction with Various Neural Networks
Crypto & Stock Price Prediction and Forecasting Toolkit.
Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.
Update Log:
→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:
pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:
  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0
LSTM
Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.
CatBoost
Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.
Prophet
Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms
Planned Features:
  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor
Important Notes:
  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.
Old Files(For Reference):
These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:
  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.
Screenshots
Screenshot1Screenshot2Screenshot3Screenshot4
Contributing:
Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project.
Let me know if you'd like any other modifications!
About
Crypto & Stock* price prediction with regression models.
Topics
aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression
Resources
Readme
Activity
Stars
14 stars
Watchers
2 watching
Forks
4 forks
Report repository
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Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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Repository files navigation
Crypto & Stock Price Prediction with Various Neural Networks
Crypto & Stock Price Prediction and Forecasting Toolkit.
Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.
Update Log:
→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:
pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:
  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0
LSTM
Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.
CatBoost
Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.
Prophet
Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms
Planned Features:
  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor
Important Notes:
  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.
Old Files(For Reference):
These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:
  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.
Screenshots
Screenshot1Screenshot2Screenshot3Screenshot4
Contributing:
Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project.
Let me know if you'd like any other modifications!
About
Crypto & Stock* price prediction with regression models.
Topics
aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression
Resources
Readme
Activity
Stars
14 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Crypto & Stock Price Prediction with Various Neural Networks
Crypto & Stock Price Prediction and Forecasting Toolkit.
Main.py & GUI.py files let's you select model, start & end date, available models are: Lstm, Catboost, Prophet, XGBoost, LGBM and Random Forest.
Update Log:
→ 24.07.2024
- File names are updated and more readable now.
- Added Random Forest Regressor.
→ 16.07.2024
LightGBM Update:
- Load & Save added.
- Docstring added.
- Code improvements.
- Config file added.
- Models & plots now save to the corresponding folder.
→ 12.07.2024
Xgboost update:
- Load & Save added.
- Bayesian Optimization added.
- TimeSeriesSplit for data splitting added.
- Config file added.
- Plot save added.
→ 09.07.2024
Prophet Update
- Save & load works
- Multiprocessing added
- Config file added
- Plots now save to plots directory
04.07.2024 →→→ 08.07.2024
- Updated Lstm, added config file.
- Config files moved to a folder.
- Model save now saves to models folder.
- Old Jupyter notebook's deleted.
- Catboost - Save & Load added

Dependencies:
pip install tensorflow yfinance ta scikit-learn pandas numpy matplotlib catboost prophet lightgbm tkinter pickle PyYAML bayesian-optimization

In case of an update breaks something:
  • tensorflow = 2.16.1
  • keras = 3.3.3
  • prophet = 1.1.5
  • yfinance = 0.2.40
  • ta = 0.11.0
  • scikit-learn = 1.5.0
  • catboost = 1.2.5
  • lightgbm = 4.4.0
LSTM
Long Short-Term Memory (LSTM). It leverages historical price data from Yahoo Finance and incorporates technical indicators (SMA, EMA, RSI) as well as time-based features to improve the prediction accuracy.
CatBoost
Gradient boosting library. It leverages historical price data from Yahoo Finance and focuses on finding the optimal model parameters through grid search for improved prediction accuracy.
Prophet
Meta's forecasting library designed for time series with seasonality and trend components, leveraging volume as a predictor.
XGBoost
XGBoost is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
LightGBM
LightGBM is a gradient boosting framework that uses tree based learning algorithms
Planned Features:
  • Graphical User Interface (GUI)
  • Save & Load
  • Random Forest Regressor
  • Gradient Boosting Regressor
Important Notes:
  • This is a side project made in free times, models made with this project shouldn't be used for anything other than experimenting.
  • At current level models made with this "tool" is not usable for crpyto market but feel free to modify, use, experiment on it.
  • Experimental Project: This is a side project for educational purposes and should not be used as the sole basis for investment decisions.
Old Files(For Reference):
These files provide basic implementations of the models, which you can reference for understanding the underlying algorithms and principles:
  • Keras-LSTM.ipynb: Illustrates the fundamentals of LSTM models in Keras.
  • CatBoostRegressor.ipynb: Shows a simple CatBoostRegressor example with grid search.
  • Prophet.ipynb: Demonstrates the basic usage of Facebook's Prophet library.
Screenshots
Screenshot1Screenshot2Screenshot3Screenshot4
Contributing:
Contributions and feedback are welcome! Feel free to open issues or submit pull requests to help improve this project.
Let me know if you'd like any other modifications!
About
Crypto & Stock* price prediction with regression models.
Topics
aibitcoincatboostregressorlgbmlightgbmlightgbm-regressorlstmlstm-modellstm-neural-networksneural-networkprice-predictionprophetxgboostxgboost-regression
Resources
Readme
Activity
Stars
14 stars
Watchers
2 watching
Forks
4 forks
Report repository
Releases
Packages
Used by
Contributors
Languages