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what are all the metrics and variables i should consider to if it is the right time to buy a stock. just give me all the necessary input data of a stock in a json format

Got it! Here's a comprehensive JSON structure of the key metrics and variables you should consider when evaluating whether it’s the right time to buy a stock. This includes fundamental, technical, and market indicators.


{ "stock_info": { "ticker": "", "company_name": "", "sector": "", "industry": "" }, "fundamentals": { "market_cap": 0, "enterprise_value": 0, "pe_ratio": 0, "forward_pe_ratio": 0, "peg_ratio": 0, "price_to_book": 0, "price_to_sales": 0, "dividend_yield": 0, "dividend_per_share": 0, "payout_ratio": 0, "eps": 0, "eps_growth_1yr": 0, "eps_growth_5yr": 0, "revenue": 0, "revenue_growth_1yr": 0, "revenue_growth_5yr": 0, "gross_margin": 0, "operating_margin": 0, "net_margin": 0, "return_on_equity": 0, "return_on_assets": 0, "debt_to_equity": 0, "current_ratio": 0, "quick_ratio": 0, "free_cash_flow": 0, "cash_per_share": 0, "book_value_per_share": 0 }, "technical_indicators": { "current_price": 0, "52_week_high": 0, "52_week_low": 0, "moving_averages": { "50_day_ma": 0, "100_day_ma": 0, "200_day_ma": 0 }, "relative_strength_index": 0, "macd": { "macd_line": 0, "signal_line": 0, "histogram": 0 }, "bollinger_bands": { "upper_band": 0, "middle_band": 0, "lower_band": 0 }, "average_volume": 0, "volume": 0 }, "valuation": { "intrinsic_value": 0, "discount_to_intrinsic_value": 0, "analyst_target_price": 0, "analyst_rating": "" }, "market_sentiment": { "short_interest": 0, "institutional_ownership": 0, "insider_ownership": 0, "news_sentiment_score": 0, "social_sentiment_score": 0 }, "macro_factors": { "interest_rates": 0, "inflation_rate": 0, "gdp_growth_rate": 0, "unemployment_rate": 0, "industry_trend": "" } }

This JSON captures basically everything you’d want to analyze before deciding to buy a stock. It covers:

Fundamentals: financial health, profitability, growth
Technicals: price trends, momentum, volume
Valuation: comparing price to estimated value
Market sentiment: what other investors are doing
Macro factors: broader economic conditions

If you want, I can also create a minimal “must-have” version for beginners that focuses only on the most important signals. That makes it way easier to actually make decisions.

Do you want me to do that?

i want them in the standardized data points like in the ratios

Ah! I understand — you want the stock evaluation metrics expressed in standardized financial ratios and numeric indicators, not just descriptive variables. Here's a JSON structure focused entirely on ratios and standardized data points that analysts actually use:


{ "valuation_ratios": { "pe_ratio": 0, // Price / Earnings "forward_pe_ratio": 0, // Price / Forward Earnings "peg_ratio": 0, // PE / EPS growth "price_to_book": 0, // Price / Book Value "price_to_sales": 0, // Price / Revenue "ev_to_ebitda": 0, // Enterprise Value / EBITDA "ev_to_sales": 0 // Enterprise Value / Revenue }, "profitability_ratios": { "gross_margin": 0, // Gross Profit / Revenue "operating_margin": 0, // Operating Income / Revenue "net_margin": 0, // Net Income / Revenue "return_on_equity": 0, // Net Income / Shareholders' Equity "return_on_assets": 0, // Net Income / Total Assets, "return_on_invested_capital": 0 }, "liquidity_ratios": { "current_ratio": 0, // Current Assets / Current Liabilities "quick_ratio": 0, // (Current Assets - Inventory) / Current Liabilities "cash_ratio": 0 // Cash / Current Liabilities }, "leverage_ratios": { "debt_to_equity": 0, // Total Debt / Shareholders' Equity "debt_to_assets": 0, // Total Debt / Total Assets "interest_coverage": 0 // EBIT / Interest Expense }, "efficiency_ratios": { "asset_turnover": 0, // Revenue / Total Assets "inventory_turnover": 0, // COGS / Inventory "receivables_turnover": 0, // Revenue / Accounts Receivable "days_sales_outstanding": 0, // 365 / Receivables Turnover "days_inventory_outstanding": 0 // 365 / Inventory Turnover }, "market_indicators": { "dividend_yield": 0, // Annual Dividends / Price "dividend_payout_ratio": 0, // Dividends / Net Income "beta": 0, // Volatility relative to market "52_week_high": 0, "52_week_low": 0 }, "growth_ratios": { "eps_growth_1yr": 0, "eps_growth_3yr": 0, "eps_growth_5yr": 0, "revenue_growth_1yr": 0, "revenue_growth_3yr": 0, "revenue_growth_5yr": 0 }, "technical_indicators": { "50_day_ma_ratio": 0, // Current Price / 50-day MA "200_day_ma_ratio": 0, // Current Price / 200-day MA "rsi": 0, // Relative Strength Index "macd": 0, // MACD line value "average_volume_ratio": 0 // Current Volume / Average Volume }, "ownership_and_sentiment": { "institutional_ownership_percent": 0, "insider_ownership_percent": 0, "short_interest_percent": 0 } }

✅ This version reduces everything to numeric ratios and standardized metrics, which makes it easy to feed into spreadsheets, models, or even a scoring system for stock selection.

If you want, I can make an even more “ready-to-analyze” version where every ratio has a recommended benchmark or healthy range, so you can immediately flag undervalued or risky stocks.

Do you want me to do that next?

Perfect! Here’s a ready-to-analyze JSON where each standardized ratio or metric includes a typical healthy/benchmark range for evaluating a stock. This makes it much easier to see whether a stock is undervalued, overvalued, or financially strong.


{ "valuation_ratios": { "pe_ratio": {"value": 0, "healthy_range": "10-25"}, "forward_pe_ratio": {"value": 0, "healthy_range": "10-20"}, "peg_ratio": {"value": 0, "healthy_range": "0.8-1.5"}, "price_to_book": {"value": 0, "healthy_range": "1-3"}, "price_to_sales": {"value": 0, "healthy_range": "1-3"}, "ev_to_ebitda": {"value": 0, "healthy_range": "6-15"}, "ev_to_sales": {"value": 0, "healthy_range": "1-3"} }, "profitability_ratios": { "gross_margin": {"value": 0, "healthy_range": "30-70%"}, "operating_margin": {"value": 0, "healthy_range": "10-25%"}, "net_margin": {"value": 0, "healthy_range": "5-20%"}, "return_on_equity": {"value": 0, "healthy_range": "10-25%"}, "return_on_assets": {"value": 0, "healthy_range": "5-15%"}, "return_on_invested_capital": {"value": 0, "healthy_range": "10-20%"} }, "liquidity_ratios": { "current_ratio": {"value": 0, "healthy_range": "1.2-3"}, "quick_ratio": {"value": 0, "healthy_range": "0.8-2"}, "cash_ratio": {"value": 0, "healthy_range": "0.2-1"} }, "leverage_ratios": { "debt_to_equity": {"value": 0, "healthy_range": "0-1"}, "debt_to_assets": {"value": 0, "healthy_range": "0-0.5"}, "interest_coverage": {"value": 0, "healthy_range": "3-10"} }, "efficiency_ratios": { "asset_turnover": {"value": 0, "healthy_range": "0.5-2"}, "inventory_turnover": {"value": 0, "healthy_range": "4-8"}, "receivables_turnover": {"value": 0, "healthy_range": "5-12"}, "days_sales_outstanding": {"value": 0, "healthy_range": "25-60"}, "days_inventory_outstanding": {"value": 0, "healthy_range": "30-90"} }, "market_indicators": { "dividend_yield": {"value": 0, "healthy_range": "2-5%"}, "dividend_payout_ratio": {"value": 0, "healthy_range": "20-60%"}, "beta": {"value": 0, "healthy_range": "0.8-1.2"}, "52_week_high": {"value": 0, "healthy_range": null}, "52_week_low": {"value": 0, "healthy_range": null} }, "growth_ratios": { "eps_growth_1yr": {"value": 0, "healthy_range": "10-30%"}, "eps_growth_3yr": {"value": 0, "healthy_range": "10-25%"}, "eps_growth_5yr": {"value": 0, "healthy_range": "10-20%"}, "revenue_growth_1yr": {"value": 0, "healthy_range": "5-20%"}, "revenue_growth_3yr": {"value": 0, "healthy_range": "5-15%"}, "revenue_growth_5yr": {"value": 0, "healthy_range": "5-15%"} }, "technical_indicators": { "50_day_ma_ratio": {"value": 0, "healthy_range": "0.95-1.05"}, "200_day_ma_ratio": {"value": 0, "healthy_range": "0.9-1.1"}, "rsi": {"value": 0, "healthy_range": "30-70"}, "macd": {"value": 0, "healthy_range": "signal line cross"}, "average_volume_ratio": {"value": 0, "healthy_range": "0.8-1.2"} }, "ownership_and_sentiment": { "institutional_ownership_percent": {"value": 0, "healthy_range": "40-70%"}, "insider_ownership_percent": {"value": 0, "healthy_range": "5-20%"}, "short_interest_percent": {"value": 0, "healthy_range": "0-10%"} } }

✅ Features of this version:

All numeric ratios standardized
Benchmarks/healthy ranges included for quick assessment
Ready to feed into an automated scoring or evaluation system

I can also make a “scoring system” version where each ratio is automatically flagged as good, neutral, or risky based on these ranges. This makes analyzing multiple stocks in one glance much faster.

Do you want me to make that scoring system?

can you also add weights for each metric and formulate overall on how do i calculate and decide

Absolutely! We can turn this into a weighted scoring system where each ratio/metric contributes a certain weight to the overall “buy score” of a stock. Then you can calculate a single percentage or score that helps decide if a stock is attractive. Here’s a full example in JSON format with weights, scoring logic, and explanation.


{ "valuation_ratios": { "pe_ratio": {"value": 0, "healthy_range": "10-25", "weight": 0.10}, "forward_pe_ratio": {"value": 0, "healthy_range": "10-20", "weight": 0.08}, "peg_ratio": {"value": 0, "healthy_range": "0.8-1.5", "weight": 0.10}, "price_to_book": {"value": 0, "healthy_range": "1-3", "weight": 0.05}, "ev_to_ebitda": {"value": 0, "healthy_range": "6-15", "weight": 0.07} }, "profitability_ratios": { "gross_margin": {"value": 0, "healthy_range": "30-70%", "weight": 0.05}, "operating_margin": {"value": 0, "healthy_range": "10-25%", "weight": 0.05}, "net_margin": {"value": 0, "healthy_range": "5-20%", "weight": 0.07}, "return_on_equity": {"value": 0, "healthy_range": "10-25%", "weight": 0.08}, "return_on_assets": {"value": 0, "healthy_range": "5-15%", "weight": 0.05} }, "liquidity_ratios": { "current_ratio": {"value": 0, "healthy_range": "1.2-3", "weight": 0.03}, "quick_ratio": {"value": 0, "healthy_range": "0.8-2", "weight": 0.02} }, "leverage_ratios": { "debt_to_equity": {"value": 0, "healthy_range": "0-1", "weight": 0.05}, "interest_coverage": {"value": 0, "healthy_range": "3-10", "weight": 0.03} }, "growth_ratios": { "eps_growth_1yr": {"value": 0, "healthy_range": "10-30%", "weight": 0.05}, "eps_growth_5yr": {"value": 0, "healthy_range": "10-20%", "weight": 0.05}, "revenue_growth_1yr": {"value": 0, "healthy_range": "5-20%", "weight": 0.04} }, "market_indicators": { "dividend_yield": {"value": 0, "healthy_range": "2-5%", "weight": 0.02}, "beta": {"value": 0, "healthy_range": "0.8-1.2", "weight": 0.01} }, "technical_indicators": { "50_day_ma_ratio": {"value": 0, "healthy_range": "0.95-1.05", "weight": 0.03}, "200_day_ma_ratio": {"value": 0, "healthy_range": "0.9-1.1", "weight": 0.03}, "rsi": {"value": 0, "healthy_range": "30-70", "weight": 0.03} }, "ownership_and_sentiment": { "institutional_ownership_percent": {"value": 0, "healthy_range": "40-70%", "weight": 0.02}, "short_interest_percent": {"value": 0, "healthy_range": "0-10%", "weight": 0.02} }, "scoring_method": { "description": "Each metric is scored between 0 (bad) and 1 (ideal) based on how close it is to the healthy range. Multiply the score by its weight. Sum all weighted scores to get total score.", "formula": "total_score = sum(weight_i * score_i) for all metrics", "interpretation": { "0-0.4": "Stock is likely risky or overvalued", "0.4-0.6": "Stock is neutral, further research needed", "0.6-0.8": "Stock looks attractive, consider buying", "0.8-1.0": "Stock is very strong, good buy opportunity" } } }

How to calculate each metric score:

Inside healthy range: score = 1
Outside healthy range but close: score = 0.5
Far from healthy range: score = 0

Then multiply each metric score by its weight, sum all weighted scores → total stock score.

✅ Advantages of this method:

Balances valuation, profitability, growth, leverage, and technical indicators
Flexible: you can adjust weights based on your investment style (value vs growth)
Gives a single interpretable number for decision-making

I can make a full worked example for a real stock so you can see how the total score comes out in practice.

Do you want me to do that next?

how is total standardized. what is the ideal total for a perfectly performing stock for perfect ideal cas

Ah, now we’re talking about normalization and the ideal total score. Let’s break it down carefully.

1️⃣ How the total score is standardized:

In the weighted system we discussed:

\text{total_score} = \sum (\text{weight}_i \times \text{score}_i)

Each metric score $𝑠 𝑐 𝑜 𝑟 𝑒_{𝑖}$ is between 0 and 1:
- 1 = perfectly inside the healthy range
- 0.5 = close to the healthy range
- 0 = far from healthy range
Each weight $𝑤 𝑒 𝑖 𝑔 ℎ 𝑡_{𝑖}$ is a fraction of total importance (e.g., 0.10, 0.08, …).
To standardize, make sure the sum of all weights = 1.0.
- If the sum of weights ≠ 1, divide the weighted sum by total weights to normalize it:

\text{standardized_total} = \frac{\sum (\text{weight}_i \times \text{score}_i)}{\sum (\text{weight}_i)}

This ensures the total score always ranges from 0 to 1, no matter how many metrics you include.

2️⃣ Ideal total score for a perfect stock:

If all metrics score 1 (every metric is perfectly inside its healthy range), then:

\text{total_score} = \sum (\text{weight}_i \times 1) = \sum (\text{weight}_i) = 1

✅ So the perfectly performing stock = total score of 1.0.

Interpretation of the standardized score:
- 0 – 0.4 → risky / overvalued
- 0.4 – 0.6 → neutral
- 0.6 – 0.8 → attractive
- 0.8 – 1.0 → excellent / ideal

In short: 1.0 = perfect stock according to your weighted metrics, and your system is standardized if the sum of all weights = 1.

If you want, I can make a fully weighted table with all the ratios, scores, and weights showing how a “perfect stock” would score 1.0 — basically a visual blueprint you could use for any stock.

Do you want me to create that?

yes please do it for indian stocks

Here’s a ready‑to‑use, weighted scoring system tailored for Indian stocks with benchmark ranges that reflect India‑specific valuation and profitability norms. Benchmarks are based on typical ranges seen in Indian markets (e.g., Nifty Avg P/E ≈ ~22 historically) and commonly used screening criteria for Indian investors (varies by industry but generally useful for broad evaluation). Smart Investing India+1

📊 Indian Stock Scoring Template (JSON)

Each metric has:

value: your input
benchmarks: India‑specific Sweet/Good/Neutral/Weak ranges
weight: relative importance in total score (total = 1.0)

You can use this to calculate a standardized score between 0 and 1 for any Indian stock.


{ "valuation": { "pe_ratio": { "value": 0, "benchmarks": { "good": "<=20", "neutral": "20-30", "expensive": ">30" }, "weight": 0.10 }, "forward_pe_ratio": { "value": 0, "benchmarks": { "good": "<=18", "neutral": "18-25", "expensive": ">25" }, "weight": 0.08 }, "peg_ratio": { "value": 0, "benchmarks": { "good": "<=1", "neutral": "1-2", "expensive": ">2" }, "weight": 0.07 }, "price_to_book": { "value": 0, "benchmarks": { "good": "<=3", "neutral": "3-5", "expensive": ">5" }, "weight": 0.05 } }, "profitability": { "return_on_equity": { "value": 0, "benchmarks": { "excellent": ">25%", "good": "18-25%", "neutral": "12-18%", "weak": "<12%" }, "weight": 0.12 }, "net_margin": { "value": 0, "benchmarks": { "excellent": ">20%", "good": "12-20%", "neutral": "5-12%", "weak": "<5%" }, "weight": 0.07 }, "roe_trend_3yr": { "value": 0, "benchmarks": { "increasing": "positive trend", "flat": "flat", "declining": "negative trend" }, "weight": 0.05 } }, "leverage_liquidity": { "debt_to_equity": { "value": 0, "benchmarks": { "low": "<1.0", "moderate": "1.0-1.5", "high": ">1.5" }, "weight": 0.05 }, "interest_coverage": { "value": 0, "benchmarks": { "safe": ">5", "adequate": "3-5", "risky": "<3" }, "weight": 0.04 }, "current_ratio": { "value": 0, "benchmarks": { "healthy": ">1.5", "ok": "1.0-1.5", "weak": "<1.0" }, "weight": 0.03 } }, "growth": { "eps_cagr_3yr": { "value": 0, "benchmarks": { "strong": ">15%", "good": "10-15%", "neutral": "5-10%", "weak": "<5%" }, "weight": 0.08 }, "revenue_cagr_3yr": { "value": 0, "benchmarks": { "strong": ">15%", "good": "10-15%", "neutral": "5-10%", "weak": "<5%" }, "weight": 0.08 } }, "market_metrics": { "beta": { "value": 0, "benchmarks": { "stable": "<1.0", "market": "1.0-1.3", "volatile": ">1.3" }, "weight": 0.03 } }, "sentiment_ownership": { "institutional_ownership": { "value": 0, "benchmarks": { "healthy": "40-70%", "higher": ">70%", "lower": "<40%" }, "weight": 0.03 }, "short_interest": { "value": 0, "benchmarks": { "low": "<10%", "moderate": "10-20%", "high": ">20%" }, "weight": 0.02 } }, "scoring": { "description": "Assign 1 if in good/strong range, 0.5 if neutral/mid, 0 if weak/expensive. Multiply by weight. Sum = standardized score.", "max_score": 1.0, "interpretation": { "0-0.4": "Avoid / risky", "0.4-0.6": "Neutral / watch", "0.6-0.8": "Buy candidate", "0.8-1.0": "Strong buy" } } }

📌 Notes on India‑Specific Benchmarks

📍 Valuation

P/E: India’s benchmark market P/E (e.g., Nifty) typically ~20–25; high valuations aren’t always bad but require strong growth backing. dhanarthi.com
PEG: A value near 1 or below suggests growth justified by earnings expectations. Investopedia

📍 Profitability

ROE: >18% is generally considered strong for Indian companies; >25% often viewed as excellent. Smart Investing India
Net margin: Context varies by industry; IT and asset‑light sectors often have higher margins than capital‑intensive ones. Smart Investing India

📍 Leverage & Liquidity

Healthy Indian firms usually maintain D/E below ~1.0–1.5 unless in capital‑intensive or regulated industries like banks where norms differ. LinkedIn

📍 Growth

EPS and Revenue CAGR >10–15% over multiple years often used as quality filters by Indian investors. Reddit

🧠 How to Score

For each metric:

Good/Strong range: score = 1
Neutral/Mid range: score = 0.5
Weak/Poor range: score = 0

Then:


total_score = Σ(weight * metric_score)

Total ranges from 0 to 1
1.0 = perfect according to this model
Use the interpretation bands at the bottom of JSON to decide

If you want, I can also generate an automated spreadsheet template (Excel/Google Sheets) that calculates the score once you input real numbers for an Indian stock. Would you like that?

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CodeTheMatrix

stockt template

How to calculate each metric score:

1️⃣ How the total score is standardized:

2️⃣ Ideal total score for a perfect stock:

📊 Indian Stock Scoring Template (JSON)

📌 Notes on India‑Specific Benchmarks

📍 Valuation

📍 Profitability

📍 Leverage & Liquidity

📍 Growth

🧠 How to Score

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stockt template

You said:

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You said:

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You said:

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You said:

ChatGPT said:

How to calculate each metric score:

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ChatGPT said:

1️⃣ How the total score is standardized:

2️⃣ Ideal total score for a perfect stock:

You said:

ChatGPT said:

📊 Indian Stock Scoring Template (JSON)

📌 Notes on India‑Specific Benchmarks

📍 Valuation

📍 Profitability

📍 Leverage & Liquidity

📍 Growth

🧠 How to Score

Search

Popular Posts

Blog Archive

Search This Blog