My Portfolio,
Analyzed through Conversation

Why Analyze with wiiiv

Portfolio reviews always need the same things — returns, volatility, sector concentration, benchmark comparison. Yet every time, you either fire up pandas and write code, settle for the limited charts in your brokerage app, or wrestle with VLOOKUP in a spreadsheet.

With wiiiv, just say "Show me a pie chart of sector allocation". The AI generates pandas + matplotlib code, executes it in an isolated Python sandbox, and displays charts and tables right in the chat. You focus only on what to look at.

Preparation: 3 CSVs

Drop 3 CSV files into your mounted folder, and you're ready to analyze.

Full Analysis Flow in 10 Turns

Below is the actual conversation in order. At each turn, the user makes a request, the AI generates and runs Python code, then presents results as tables and charts.

wiiiv

Sky

Skytree

Open portfolio_holdings.csv and stock_daily_prices.csv from the stock portfolio folder and show me

wiiiv

I've opened and verified both CSV files.

1. portfolio_holdings.csv — Rows: 5, Columns: 6
2. stock_daily_prices.csv — Rows: 750, Columns: 8

Python script used: pandas read_csv + to_markdown (11 lines)

wiiiv

Sky

Skytree

Calculate market value and return for each stock based on the latest closing price

wiiiv

Here are the market values and returns calculated based on the latest closing price.

Stock Name	Shares	Buy Price	Current Price	Cost Basis	Market Value	Return
Samsung Electronics	30	71,000	65,600	2,130,000	1,968,000	-7.61%
SK Hynix	10	178,000	258,700	1,780,000	2,587,000	+45.34%
Naver	15	210,000	165,258	3,150,000	2,478,870	-21.31%
Kakao	40	42,000	34,292	1,680,000	1,371,680	-18.35%
LG Energy Solution	5	380,000	396,823	1,900,000	1,984,115	+4.43%

Total Cost	Total Value	Total P&L	Overall Return
10,640,000	10,389,665	-250,335	-2.35%

Python script used: merge + return calculation (25 lines)

wiiiv

Sky

Skytree

Show me a pie chart of sector allocation

wiiiv

Here is the sector allocation pie chart.

Sector	Shares	Weight
Platform (Naver + Kakao)	55	55%
Semiconductor (Samsung Electronics + SK Hynix)	40	40%
EV Battery (LG Energy Solution)	5	5%

Sector Allocation by Shares

Platform55%

Semiconductor40%

EV Battery5%

wiiiv

Sky

Skytree

Draw a line chart showing the daily return trend for each stock

wiiiv

The daily return trend line chart has been generated.

stock_daily_return_trend.png

Date	LG Energy Solution	SK Hynix	Naver	Samsung Electronics	Kakao
2025-11-03	-1.26	-0.13	-0.97	-0.90	0.05
2025-11-04	-1.44	-2.84	0.89	-2.27	-1.61
2025-11-05	-3.73	-2.80	-2.31	-1.37	-3.74

Python script used: matplotlib line plot + figsize=(12,6) (28 lines)

wiiiv

Sky

Skytree

Analyze the correlation of daily returns between stocks and draw a heatmap

wiiiv

The daily return correlations have been calculated and a heatmap has been generated.

stock_return_correlation_heatmap.png

Correlation Matrix

LGES

SKH

Naver

SEC

Kakao

LGES

1.00

0.16

-0.06

-0.42

-0.03

SKH

0.16

1.00

-0.83

-0.31

-0.37

Naver

-0.06

-0.83

1.00

0.11

0.24

SEC

-0.42

-0.31

0.11

1.00

-0.03

Kakao

-0.03

-0.37

0.24

-0.03

1.00

Interpretation:
• Strongest negative correlation: SK Hynix ↔ Naver (-0.8285)
• Relatively weak positive correlation: LG Energy Solution ↔ SK Hynix (0.1553)
• Samsung Electronics and LG Energy Solution show a notable negative correlation (-0.4248)

wiiiv

Sky

Skytree

Read kospi_daily_index.csv from the stock portfolio folder and compare my portfolio's cumulative return with KOSPI on the same chart

wiiiv

The portfolio vs. KOSPI cumulative return comparison chart has been generated.

portfolio_vs_kospi_cumulative_return.png

Date	Portfolio Cumulative Return	KOSPI Cumulative Return
2025-11-03	-0.71	0.00
2025-11-04	-1.18	0.39
2025-11-05	-2.68	0.74
2025-11-06	-2.07	1.44
2025-11-07	-1.10	1.50

Python script used: daily valuation sum → cumulative return calc → matplotlib comparison plot (47 lines)

wiiiv

Sky

Skytree

Analyze the portfolio's daily returns, annualized volatility, and maximum drawdown (MDD)

wiiiv

The portfolio's daily returns, annualized volatility, and maximum drawdown (MDD) have been calculated.

Avg Daily Return(%)	Daily Std Dev(%)	Annualized Volatility(%)	Max Drawdown MDD(%)
-1.4067	2.0069	31.8580	-9.9751

Interpretation:
• Average daily return is -1.4067%, negative
• Annualized volatility of 31.86% indicates considerable daily fluctuation
• Maximum drawdown (MDD) of -9.97% means a roughly 10% decline from peak occurred

Python script used: drawdown = decline from cumulative peak (52 lines)

wiiiv

Sky

Skytree

Compare the Sharpe ratio for each stock. Use 3.5% annual risk-free rate

wiiiv

Here is the annualized Sharpe ratio comparison by stock, using a 3.5% annual risk-free rate.

Stock Name	Avg Daily Return(%)	Daily Std Dev(%)	Annualized Sharpe Ratio
SK Hynix	19.81	15.55	20.21
LG Energy Solution	5.81	6.56	14.03
Naver	-2.73	7.94	-5.48
Samsung Electronics	-3.16	3.38	-14.88
Kakao	-27.36	9.24	-47.00

Only SK Hynix and LG Energy Solution are positive — the only holdings with favorable risk-adjusted performance. Kakao has a Sharpe ratio of -47.00, the highest risk-adjusted loss.

Python script used: (mean_daily - rf_daily) / std_daily * sqrt(252) (31 lines)

wiiiv

Sky

Skytree

Analyze whether any sectors are overweight and suggest a rebalancing plan

wiiiv

I've analyzed sector concentration and calculated rebalancing suggestions based on current market value.

Sector	Stocks	Total Cost	Total Value	Current Weight
Semiconductor	2	3,910,000	4,555,000	43.84%
Platform	2	4,830,000	3,850,550	37.06%
EV Battery	1	1,900,000	1,984,115	19.10%

Rebalancing Suggestion (Equal-weight 33.3% target):

Sector	Target Weight	Adjustment Amount	Action
Semiconductor	33.33%	-1,091,778	Reduce
Platform	33.33%	-387,328	Reduce
EV Battery	33.33%	+1,479,107	Increase

The combined weight of Semiconductor and Platform is 80.90%, excessively concentrated. Rebalancing toward a higher EV Battery allocation is recommended.

wiiiv

Sky

Skytree

Write a comprehensive portfolio review report based on the above analysis

wiiiv

The portfolio review report has been generated.

portfolio_review_report.docx

The report includes:

1. Summary — By market value: Semiconductor 43.84%, Platform 37.06%, EV Battery 19.10%
2. Sector Overview — Number of stocks, cost basis, market value, P&L
3. Stock Performance — SK Hynix +45.34%, LG Energy Solution +4.43%, others negative
4. Risk Metrics — Annualized volatility 31.86%, MDD -9.97%
5. Sharpe Ratio Comparison — Only SK Hynix (20.21) and LG Energy Solution (14.03) positive
6. Rebalancing Suggestion — Trim semiconductor, reduce platform, increase EV battery
7. Conclusion — Clear sector concentration with wide performance gaps between stocks

Python script used: python-docx Document() + 7 sections (68 lines)

Tips for Effective Analysis

1. Be specific in your requests

"Show me a pie chart of sector allocation" works better than "analyze this." Specify the chart type (pie/line/heatmap/bar), reference values (risk-free rate 3.5%), and time period to get exactly what you want.

2. Reference previous turns

Say things like "From the above analysis, pull out only the stocks with a negative Sharpe ratio." Variables remain in memory, so follow-up analysis runs instantly without recalculation.

3. Wrap up with a report

When your analysis is done, say "Write a summary report" to compile everything. A docx file is generated and ready for download, and you can even send it via email.

4. Works with any structured data

This guide uses a stock portfolio as an example, but the same approach works for household budgets, server logs, sales data, survey results, or any structured data in CSV format.