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import gradio as gr |
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''' |
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https://huggingface.co/spaces/kevinhug/clientX |
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https://hits.seeyoufarm.com/ |
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''' |
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''' |
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PORTFOLIO OPTIMIZATION |
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''' |
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from aiOpt import Asset |
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import numpy as np |
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def optimize(cost, prob, its): |
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s = Asset(np.asfarray(cost.split()), |
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np.asfarray(prob.split())) |
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return s.random_restart(int(its)) |
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''' |
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TIME SERIES ANALYTICS |
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''' |
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import pandas as pd |
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import plotly.express as px |
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def trend(t): |
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''' |
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import yfinance as yf |
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from sklearn.preprocessing import StandardScaler |
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data = yf.download(t, period="3mo") |
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for c in t.split(' '): |
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q=data.loc[:,('Close',c)] |
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data.loc[:,('Close_MA',c)]=q.rolling(9).mean() -q.rolling(42).mean() |
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q=data.loc[:,('Volume',c)] |
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data.loc[:,('Volume_MA',c)]=q.rolling(9).mean() -q.rolling(42).mean() |
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ma=data.loc[:,["Volume_MA","Close_MA"]].tail(15) |
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std=StandardScaler() |
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result=std.fit_transform(ma) |
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df=pd.DataFrame(result,columns=ma.columns) |
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d=df.tail(1).stack(level=-1).droplevel(0, axis=0) |
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''' |
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d=pd.read_pickle("data.pkl") |
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''' |
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https://www.gradio.app/docs/plot |
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fig = px.line(df, x="day", y=countries) |
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fig.update_layout( |
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title="Outbreak in " + month, |
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xaxis_title="Cases", |
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yaxis_title="Days Since Day 0", |
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) |
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return fig |
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''' |
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fig=px.scatter(d, x="Close_MA", y="Volume_MA",color='ticker') |
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fig.update_layout( |
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title="Top Right is the Growth Industry", |
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xaxis_title="Trend in Price", |
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yaxis_title="Trend in Volume", |
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) |
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return fig |
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''' |
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SIMILAR VECTOR DB SEARCH |
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''' |
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import chromadb |
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client = chromadb.PersistentClient(path="chroma.db") |
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db = client.get_collection(name="banks") |
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def similar(issue): |
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global db |
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docs = db.query(query_texts=issue, n_results=5) |
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return docs |
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''' |
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FINE TUNE LLM LIKE SCORE |
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''' |
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from fastai.text.all import * |
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import pathlib |
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p=pathlib.Path('./banks_txt_like.pkl').resolve() |
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''' |
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NotImplementedError: cannot instantiate ‘WindowsPath’ on your system |
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''' |
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import platform |
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plt = platform.system() |
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if plt == 'Windows': |
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pathlib.PosixPath = pathlib.WindowsPath |
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else: |
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pathlib.WindowsPath = pathlib.PosixPath |
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learn = load_learner(p) |
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def like(issue): |
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pred,idx,probs = learn.predict(issue) |
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return pred |
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''' |
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EXPLAINABLE AI |
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''' |
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''' |
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https://www.gradio.app/docs/interface |
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''' |
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with gr.Blocks() as demo: |
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''' |
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https://hits.seeyoufarm.com/ |
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https://dash.elfsight.com |
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''' |
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counter=""" |
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<script src="https://static.elfsight.com/platform/platform.js" data-use-service-core defer></script> |
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<div class="elfsight-app-5f3e8eb9-9103-490e-9999-e20aa4157dc7" data-elfsight-app-lazy></div> |
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""" |
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gr.Markdown("""Enhancing Customer Engagement and Operational Efficiency with NLP |
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========= |
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1) Semantic Similarity Document Search (SSDS) |
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2) Fine Tune LLM |
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3) Trading Analytic: Using Time Series Data to Identify Growth |
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4) Portfolio Optimization with cost, probabilities |
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#### Data Scientist: Kevin Wong, [email protected], 416-903-7937 |
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##### Open source ml bank dataset, |
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__I'm just using a small sample of this data set for demo__ |
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https://www.kaggle.com/datasets/trainingdatapro/20000-customers-reviews-on-banks/?select=Banks.csv |
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[](https://hits.seeyoufarm.com) |
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""") |
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with gr.Tab("Semantic Similarity Document Search (SSDS)"): |
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in_similar = gr.Textbox(placeholder="having credit card problem", |
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label="Issue", |
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info="issue you want to explore about" |
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) |
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out_similar = gr.JSON(label="Similar Verbatim") |
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btn_similar = gr.Button("Find Similar Verbatim") |
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btn_similar.click(fn=similar, inputs=in_similar, outputs=out_similar) |
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gr.Examples( |
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[ |
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["having credit card problem"], |
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["low interest credit card"], |
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["loan"], |
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["upset customer"], |
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["what is the password"], |
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], |
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[in_similar] |
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) |
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gr.Markdown(""" |
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Description: |
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======= |
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In today's dynamic financial landscape, the Semantic Similarity Document Search (SSDS) capability is a practical innovation to improve client experience, marketing leads, and sentiment analysis. As a Data Scientist with a decades in the financial industry, I see the value of SSDS in action. |
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Investment Portfolio Construction/Marketing Leads: |
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------ |
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To enhance marketing strategies, SSDS identifies market trends and consumer preferences, such as the demand for low-interest credit cards, and GIC. It's a treasure trove for refining our product offerings to the targeted customer according to their credit score, risk appetite, demographic, collateral, capital, and economic conditions, enhancing the lift and efficiency of the recommendation process. |
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Combining **SingleStore MEMSQL/Kafka structured streaming** for a real-time recommendation for closing sales at the right time in the right channel. |
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Optimize your Asset Allocation with your objective function and cost function. |
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### issue: |
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- low interest credit card |
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- GIC |
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AML/KYC/Compliance/Audit/Cyber Security/Fraud Analytics/Observability: |
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------ |
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### vite vue chart.js UI demo |
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https://kevinwkc.github.io/davinci/ |
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### Proactive Detection: Identify potential fraud threats and vulnerabilities in real-time. |
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Customer-Centric Approach: Gain insights into customer concerns, allowing us to address them promptly. |
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#### issue: |
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- what is the password |
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Client Experience: |
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------ |
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When a client faces a bad experience, SSDS helps us swiftly locate relevant documents to understand and address their concerns, be it credit card issues, late payment fees, or credit score drops. |
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### issue: |
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- having bad client experience |
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- having credit card problem |
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- late payment fee |
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- credit score dropping |
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Sentiments: |
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------ |
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SSDS tracks customer sentiment, empowering us to swiftly respond to upset customers. It ensures we address their issues promptly, enhancing trust and loyalty. |
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With no need for jargon, SSDS delivers tangible value to our fintech operations. It's about staying agile, informed, and customer-centric in a rapidly changing financial world. |
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### issue: |
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- upset customer |
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""") |
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with gr.Accordion("Future Improvement"): |
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gr.Markdown(""" |
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tuning the distance for use case |
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""") |
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with gr.Tab("Explainable AI"): |
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gr.Markdown(""" |
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Explain by Dataset |
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============= |
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sorted feature from top(most import) |
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dist_subway when at low value(green) make big impact to price |
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Explain by Feature |
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============= |
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dist lower than 900 spike the price f(x) |
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also highlighted the shap value for record[20] at around 6500 |
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Explain by Record |
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============= |
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the largest contribution to positive price is dist_subway |
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second contribution is age |
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""") |
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with gr.Tab("Fine Tune LLM"): |
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in_like = gr.Textbox(placeholder="having credit card problem" , label="Issue", |
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info="issue you want to explore about") |
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out_like = gr.Textbox(placeholder="like score in range [2 to 248] from fine tuning data", |
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label="like score", |
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info="like score") |
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btn_like = gr.Button("Classify Like Score") |
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btn_like.click(fn=like, inputs=in_like, outputs=out_like) |
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gr.Examples( |
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[ |
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["having credit card problem"], |
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["low interest credit card"], |
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["loan"], |
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["upset customer"], |
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["what is the password"], |
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], |
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[in_like] |
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) |
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gr.Markdown(""" |
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Smart Insights: Elevating Customer Engagement Through Sentiment Analysis |
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========= |
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As a Data Scientist with a decades of financial industry experience, I recognize the paramount importance of staying closely tuned to our customer's needs and opinions. In this app, Fine Tune LLM, we have shown how fine-tuning a Language Model (LLM) on a custom dataset can provide valuable insights into customer sentiment across crucial areas such as service, sales, point of failure, product, and emerging trends. |
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Objective: |
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--------- |
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Our aim is to extract meaningful insights from customer interactions to improve our services, products, and overall customer experience. This analysis will help us understand what our customers are discussing and how they feel about different aspects of our business. |
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Use Case: |
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- intervene attrition through incentive |
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""") |
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with gr.Tab("Trading Analyics"): |
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in_ts = gr.Textbox(placeholder="XLE XLV XLY XLK XLF XLP XLI XLRE XLU", |
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label="Ticker", |
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info="Technical Difficult: Currently it only works with these tickers due to data pulling constraints" |
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) |
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plot = gr.Plot() |
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btn_ts = gr.Button("Find Trending Industry") |
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btn_ts.click(fn=trend, inputs=in_ts, outputs=plot) |
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gr.Markdown(""" |
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Maximizing Trading Efficiency: Personalize Your Asset Allocation for Optimal Growth |
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========= |
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The industry life cycle is a useful tool for traders to identify growth and decline industries. It describes the evolution of an industry based on its stages of growth and decline |
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#### There are four phases of the industry life cycle: introduction, growth, maturity, and decline |
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By identifying growth and decline industries, traders can make informed investment decisions and speed up trading by investing in companies that are likely to experience growth in the future and avoiding companies that are likely to experience a decline in the future. |
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- Long Trader: buy growth industry |
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- Short Trader: sell decline industry |
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#### Personalize objective function and cost function for each trader |
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- cost function can prevent selecting decline industry |
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we can use this to filter out blacklisted firms for compliance |
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we can increase the cost for highly correlated stock for diversity |
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- objective function can identify potential industry |
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#### Personalize UI to fit each trader |
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customize UI for secret sauce formula for stock picking: |
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- metric: moving average for the price, moving average for volume, ...etc |
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- timeframe chain: monthly, weekly, daily, 4h, 15 min |
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##### tableau portfolio |
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https://public.tableau.com/app/profile/kevin1619 |
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##### vite vue chart.js UI demo |
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https://kevinwkc.github.io/davinci/ |
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#### Personalize Alert with Twilio |
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The trader can set their price to buy at the right price at the right time, without missing the right entry in a high stress environment |
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#### Keeping record for compliance |
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The System can save data for reporting or compliance purpose |
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""") |
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with gr.Tab("Portfolio Optimization"): |
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in_p_cost = gr.Textbox(value="4 30 2 3 5", |
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label="Cost", |
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info="cost for the asset" |
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) |
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in_p_prob = gr.Textbox(value="0.3 0.4 0.5 0.6 0.7", |
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label="Probabilities", |
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info="P(success) for the asset" |
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) |
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in_p_its = gr.Textbox(value="10", |
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label="Number of Iteration", |
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info="number of trial for optimal" |
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) |
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out_p = gr.Textbox(label="Asset Allocation Approx Optimization using AI") |
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btn_p = gr.Button("Optimize Asset Allocation") |
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btn_p.click(fn=optimize, inputs=[in_p_cost, in_p_prob, in_p_its], outputs=out_p) |
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gr.Markdown(""" |
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Objective: To allocate assets in a way that maximizes expected profit while minimizing costs and risks. |
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Inputs: |
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- List of available assets and their associated probabilities and costs. |
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Outputs: |
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- Allocation of assets that maximizes expected profit while minimizing costs and risks. |
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Constraints: |
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Assume volume is bound by 0 to 100 |
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Objective: max SUM [ vol_s * prob_s - cost_s ] |
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- The total cost of the allocation must not exceed the available budget. |
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- The risk level of the allocation must not exceed the maximum allowable risk level. |
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- The profit margin of the allocation must not fall below the minimum allowable profit margin. |
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Method: |
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Using search algorithm to find the approx. optimial allocation |
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Assumptions: |
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- The probabilities and costs of the assets are known with certainty. |
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- The expected return and risk level of the allocation are calculated using historical data and statistical models. |
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""") |
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demo.launch() |
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