From e1465539d25bec56c8e4e9b110f121a935a13978 Mon Sep 17 00:00:00 2001 From: Simon Moisy Date: Wed, 19 Mar 2025 15:28:30 +0800 Subject: [PATCH] article analyzer with finbert working --- .idea/.gitignore | 3 + article_analyzer.py | 34 +- bitcoin_trend_analysis.py | 56 +- main_article_analyzer.py | 2 +- main_trend_analysis.py | 2 +- poetry.lock | 1903 +++++++++++++++++++++++++++++++++++++ pyproject.toml | 30 + 7 files changed, 1975 insertions(+), 55 deletions(-) create mode 100644 .idea/.gitignore create mode 100644 poetry.lock create mode 100644 pyproject.toml diff --git a/.idea/.gitignore b/.idea/.gitignore new file mode 100644 index 0000000..26d3352 --- /dev/null +++ b/.idea/.gitignore @@ -0,0 +1,3 @@ +# Default ignored files +/shelf/ +/workspace.xml diff --git a/article_analyzer.py b/article_analyzer.py index 9ca60b0..ea75917 100644 --- a/article_analyzer.py +++ b/article_analyzer.py @@ -1,7 +1,8 @@ from enum import Enum - +from transformers import pipeline import ollama from pydantic import BaseModel +import markdownify class Category(str, Enum): REGULATORY_NEWS = "Regulatory News" @@ -23,6 +24,7 @@ class ArticleClassification(BaseModel): class ArticleAnalyzer: def __init__(self): + self.classifier = pipeline("text-classification", model="ProsusAI/finbert") self.base_prompt = """ Classify the following article into one of these categories: - Regulatory News @@ -35,9 +37,28 @@ class ArticleAnalyzer: Also, assign a sentiment (Positive, Neutral or Negative) """ - print(f"This JSON model is going to be used for structured ouput classification : {ArticleClassification.model_json_schema()}") + print(f"This JSON model is going to be used for structured output classification : {ArticleClassification.model_json_schema()}") - def classify_article(self, article_text): + @staticmethod + def convert_to_markdown(html_content: str) -> str: + """ + Convert HTML content to Markdown format. + + Args: + html_content: Cleaned HTML content + + Returns: + Markdown content + """ + return markdownify.markdownify( + html_content, + strip=["script", "style", "img", "svg"], + strip_tags=True, + heading_style="atx", + code_block=True + ) + + def classify_article_llm(self, article_text): prompt = f"""{self.base_prompt} ARTICLE: {article_text} @@ -49,4 +70,9 @@ class ArticleAnalyzer: response = ollama.chat(model="llama3.2", messages=[{"role": "user", "content": prompt}], format=ArticleClassification.model_json_schema()) - return response['message']['content'] \ No newline at end of file + return response['message']['content'] + + def classify_article_finbert(self, article_html): + article_md = self.convert_to_markdown(article_html) + result = self.classifier(article_md) + return result \ No newline at end of file diff --git a/bitcoin_trend_analysis.py b/bitcoin_trend_analysis.py index 00f964b..a52b240 100644 --- a/bitcoin_trend_analysis.py +++ b/bitcoin_trend_analysis.py @@ -56,67 +56,25 @@ class BitcoinTrendAnalysis: smooth_prices = pd.Series(prices).rolling(window=smoothing_window).mean() fig, ax = plt.subplots(figsize=(14, 7)) - plt.subplots_adjust(bottom=0.25) # Space for widgets - ax2 = ax.twinx() # Secondary axis for prominence + plt.subplots_adjust(bottom=0.25) - # Initial peaks and prominences peaks, valleys, prominences = self.adaptive_find_peaks(smooth_prices, window=window, factor=prominence_factor, distance=distance) - # Plot main price curve - price_line, = ax.plot(self.df.index, smooth_prices, label='Bitcoin Smooth Price') + ax.plot(self.df.index, smooth_prices, label='Bitcoin Smooth Price') + ax.scatter(self.df.index[peaks], smooth_prices[peaks], color='green', s=100, marker='^', label='Local Maxima') + ax.scatter(self.df.index[valleys], smooth_prices[valleys], color='red', s=100, marker='v', label='Local Minima') - # Scatter plots for peaks/valleys - peaks_plot = ax.scatter(self.df.index[peaks], smooth_prices[peaks], color='green', s=100, marker='^', - label='Local Maxima') - valleys_plot = ax.scatter(self.df.index[valleys], smooth_prices[valleys], color='red', s=100, marker='v', - label='Local Minima') - - # Prominence line on secondary y-axis - prominence_line, = ax2.plot(self.df.index, prominences, color="purple", linestyle="dashed", alpha=0.7, - label="Prominence") - - ax2.set_ylabel("Prominence") + for peak, valley in zip(peaks, valleys): + ax.plot([self.df.index[peak], self.df.index[valley]], [smooth_prices[peak], smooth_prices[valley]], + color='orange', lw=1) ax.set_title(f'Bitcoin Price Trends Analysis\nfactor={prominence_factor}') ax.set_xlabel('Date') ax.set_ylabel('Price') ax.legend() - ax2.legend(loc="upper right") ax.grid(True) - # Slider setup - ax_slider = plt.axes([0.2, 0.05, 0.65, 0.03]) # Positioning of slider - slider = Slider(ax_slider, 'Prom Factor', 0.1, 2.0, valinit=prominence_factor, valstep=0.05) - - # Update function for slider - def update_plot(factor): - # Recalculate peaks and prominences - peaks, valleys, prominences = self.adaptive_find_peaks(smooth_prices.to_numpy(), window=window, - factor=factor, distance=distance) - print(len(peaks)) - # Update scatter points for peaks - peaks_plot.set_offsets(np.column_stack([ - (self.df.index[peaks] - np.datetime64('1970-01-01')) / np.timedelta64(1, 's'), - smooth_prices[peaks] - ])) - - # Update scatter points for valleys - valleys_plot.set_offsets(np.column_stack([ - (self.df.index[valleys] - np.datetime64('1970-01-01')) / np.timedelta64(1, 's'), - smooth_prices[valleys] - ])) - - # Update prominence line - prominence_line.set_ydata(prominences) - - # Update the title to reflect the current prominence factor - ax.set_title(f'Bitcoin Price Trends Analysis\nfactor={factor}') - - # Redraw the figure - fig.canvas.draw_idle() - - slider.on_changed(update_plot) # Update plot when slider changes plt.show() def analyze_trends_linear_regression(self): diff --git a/main_article_analyzer.py b/main_article_analyzer.py index a13a9d8..899c56a 100644 --- a/main_article_analyzer.py +++ b/main_article_analyzer.py @@ -20,5 +20,5 @@ if __name__ == "__main__": print(f"Parsed {len(html_files)} html files") for file, content in html_files.items(): - result = analyzer.classify_article(content) + result = analyzer.classify_article_finbert(content) print(f"article [{file}] - analyzed as [{result}]\n") diff --git a/main_trend_analysis.py b/main_trend_analysis.py index 887732f..18436e8 100644 --- a/main_trend_analysis.py +++ b/main_trend_analysis.py @@ -3,4 +3,4 @@ from bitcoin_trend_analysis import BitcoinTrendAnalysis if __name__ == "__main__": ma = BitcoinTrendAnalysis(db_path='bitcoin_historical_data.db') ma.load_data() - ma.analyze_trends_peaks(distance=1) + ma.analyze_trends_peaks(distance=1, prominence_factor=0.1) diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..58890f2 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,1903 @@ +# This file is automatically @generated by Poetry 2.1.1 and should not be changed by hand. + +[[package]] +name = "annotated-types" +version = "0.7.0" +description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" +groups = ["main"] +files = [ + {file = "annotated_types-0.7.0-py3-none-any.whl", hash = "sha256:1f02e8b43a8fbbc3f3e0d4f0f4bfc8131bcb4eebe8849b8e5c773f3a1c582a53"}, + {file = "annotated_types-0.7.0.tar.gz", hash = "sha256:aff07c09a53a08bc8cfccb9c85b05f1aa9a2a6f23728d790723543408344ce89"}, +] + +[[package]] +name = "anyio" +version = "4.9.0" +description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.9" +groups = ["main"] +files = [ + {file = "anyio-4.9.0-py3-none-any.whl", hash = "sha256:9f76d541cad6e36af7beb62e978876f3b41e3e04f2c1fbf0884604c0a9c4d93c"}, + {file = "anyio-4.9.0.tar.gz", hash = "sha256:673c0c244e15788651a4ff38710fea9675823028a6f08a5eda409e0c9840a028"}, +] + +[package.dependencies] +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} +idna = ">=2.8" +sniffio = ">=1.1" +typing_extensions = {version = ">=4.5", markers = "python_version < \"3.13\""} + +[package.extras] +doc = ["Sphinx (>=8.2,<9.0)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx_rtd_theme"] +test = ["anyio[trio]", "blockbuster (>=1.5.23)", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypothesis (>=4.0)", "psutil (>=5.9)", "pytest (>=7.0)", "trustme", "truststore (>=0.9.1) ; python_version >= \"3.10\"", "uvloop (>=0.21) ; platform_python_implementation == \"CPython\" and platform_system != \"Windows\" and python_version < \"3.14\""] +trio = ["trio (>=0.26.1)"] + +[[package]] +name = "beautifulsoup4" +version = "4.13.3" +description = "Screen-scraping library" +optional = false +python-versions = ">=3.7.0" +groups = ["main"] +files = [ + {file = "beautifulsoup4-4.13.3-py3-none-any.whl", hash = "sha256:99045d7d3f08f91f0d656bc9b7efbae189426cd913d830294a15eefa0ea4df16"}, + {file = "beautifulsoup4-4.13.3.tar.gz", hash = "sha256:1bd32405dacc920b42b83ba01644747ed77456a65760e285fbc47633ceddaf8b"}, +] + +[package.dependencies] +soupsieve = ">1.2" +typing-extensions = ">=4.0.0" + +[package.extras] +cchardet = ["cchardet"] +chardet = ["chardet"] +charset-normalizer = ["charset-normalizer"] +html5lib = ["html5lib"] +lxml = ["lxml"] + +[[package]] +name = "certifi" +version = "2025.1.31" +description = "Python package for providing Mozilla's CA Bundle." +optional = false +python-versions = ">=3.6" +groups = ["main"] +files = [ + {file = "certifi-2025.1.31-py3-none-any.whl", hash = "sha256:ca78db4565a652026a4db2bcdf68f2fb589ea80d0be70e03929ed730746b84fe"}, + {file = "certifi-2025.1.31.tar.gz", hash = "sha256:3d5da6925056f6f18f119200434a4780a94263f10d1c21d032a6f6b2baa20651"}, +] + +[[package]] +name = "charset-normalizer" +version = "3.4.1" +description = "The Real First Universal Charset Detector. 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