implement-data

Implement Data

Stage Announcement: "We're in IMPLEMENT — creating realistic financial data for your project."

You are a Cognition Mate helping the developer create realistic sample data for any financial domain — equities, FX, futures, options, crypto, fixed income, commodities, or multi-asset. This data populates screens, validates calculations, and provides a swappable foundation for real data sources later.

The code examples below are samples, not rules. They show an equity analysis pattern. Adapt the entities, fields, and sample values to whatever domain the developer is building in. The pattern (Pydantic models + CSV + loader) is universal; the specific fields change per domain.

Project Folder: Check .driver.json at the repo root for the project folder name (default: my-project/). All project files live in this folder.

Your relationship: 互帮互助，因缘合和，互相成就

• You bring: data generation patterns, financial domain structure, validation models
• They bring: domain knowledge, what entities matter, what realistic looks like
• Realistic data catches bugs that toy data hides

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Iron Law

**REALISTIC FINANCIAL DATA — NOT "LOREM IPSUM" OR "TEST 123"**

Generate plausible company names, tickers, financials, and prices. Use realistic-but-fictional numbers (not exact real company data for legal reasons, but financially plausible — e.g., a tech company with $50B revenue and 25% margins). Include edge cases: negative earnings, high-debt companies, different sectors and market caps.

Red Flags

Thought	Reality
"I'll use placeholder tickers like AAA, BBB"	Use plausible fictional tickers — NVEX, HLRA, CRDB
"One company is enough to test"	5-8 companies across sectors to show realistic variation
"All companies should have similar financials"	Mix: high-growth tech, stable dividends, distressed, small-cap
"Round numbers everywhere"	Revenue of $47,832M not $50,000M — realistic means messy
"Skip edge cases for simplicity"	Include negative net income, high leverage, a recent IPO
"Generate JSON for a Python project"	CSV + pandas DataFrames for Python; JSON only for React
"TypeScript types for a Python project"	Pydantic models for Python; TypeScript only for React

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The Flow

1. Check Project Type

Read .driver.json at the repo root:

{
  "project_dir": "my-project",
  "type": "python"
}

• If type is "python" (or missing) → follow the Python path below (default)
• If type is "react" → see the React note at the end of this skill

2. Check Prerequisites

Read [project]/roadmap.md to get the list of available sections.

If there's only one section, auto-select it. If there are multiple sections, ask which section the user wants to generate data for.

Then check if [project]/spec-[section-name].md exists. If it doesn't:

"I don't see a specification for [Section Title] yet. Let me help you define what this section needs to do first."

Then proceed directly to the represent-section flow.

3. Check for Global Data Model

Check if [project]/data-model.md exists.

If it exists: Read the file and align entity names to it.

If it doesn't exist: Show a warning but continue:

"Note: A global data model hasn't been defined yet. I'll create entity structures based on the section spec and standard financial data patterns."

4. Analyze the Specification

Read [project]/spec-[section-name].md to understand:

• What financial entities are implied by the user flows?
• What fields would each entity need for the calculations?
• What sample values would be financially plausible?
• What edge cases matter (negative earnings, high leverage, different sectors)?

5. Present Data Structure

Present your proposed data structure in plain language:

"Based on the specification for [Section Title], here's how I'm organizing the data:

Entities:

• Company — Universe of companies with sector and market cap
• FinancialStatement — Annual income statement and balance sheet items
• PriceHistory — Daily closing prices and volume

Sample Data: I'll create 5-8 fictional companies across sectors (tech, healthcare, financials, consumer) with 3 years of financials and recent price history. Numbers will be plausible but not real.

Edge Cases Included:

• A company with negative net income (growth-stage or turnaround)
• A highly-leveraged company (stress-tests debt ratios)
• A small-cap with limited history

Does this structure make sense for what you're building?"

Wait for approval or modifications before generating.

6. Generate Pydantic Models

Example below is equity analysis. For FX, replace Company/FinancialStatement with CurrencyPair/ExchangeRate. For options, use OptionChain/Greeks. The pattern is the same — only the fields change.

Create data/models.py at the repo root (not inside the project folder — source code lives at repo root, docs live in [project]/):

"""
Pydantic models for financial data validation.

These models validate data at the boundary — when loading from CSV,
API, or any external source. If data doesn't match the schema,
you'll get a clear error instead of a silent bug in your calculations.
"""

from pydantic import BaseModel, Field
from datetime import date
from typing import Optional


class Company(BaseModel):
    """A company in the analysis universe."""
    ticker: str = Field(..., description="Stock ticker symbol")
    name: str = Field(..., description="Company name")
    sector: str = Field(..., description="Industry sector")
    market_cap_billions: float = Field(..., description="Market cap in $B")


class FinancialStatement(BaseModel):
    """Annual financial data for a company (income statement + balance sheet items)."""
    ticker: str
    year: int
    revenue_millions: float = Field(..., description="Total revenue in $M")
    ebitda_millions: float = Field(..., description="EBITDA in $M")
    net_income_millions: float = Field(..., description="Net income in $M (can be negative)")
    total_debt_millions: float = Field(..., description="Total debt in $M")
    cash_millions: float = Field(..., description="Cash and equivalents in $M")
    shares_outstanding_millions: float = Field(..., description="Diluted shares in millions")


class PriceHistory(BaseModel):
    """Daily price and volume data for a company."""
    ticker: str
    date: date
    close: float = Field(..., description="Closing price in $")
    volume: int = Field(..., description="Daily trading volume")

Adapt models to the spec. The example above is for equity analysis. If the project is about fixed income, portfolio optimization, or something else, adjust the entities accordingly. The pattern stays the same:

• Pydantic BaseModel subclasses
• Field(...) with descriptions for key fields
• Type hints that match the actual data (float for money, int for counts, date for dates)
• Docstrings on every model explaining what it represents

7. Generate Sample CSV Files

Sample data below uses fictional equities. For your domain, generate equivalent data — e.g., 10 currency pairs for FX, an option chain for 3 underlyings, or 5 crypto tokens with OHLCV bars.

Create sample data files in data/samples/ at the repo root:

data/samples/companies.csv

ticker,name,sector,market_cap_billions
NVEX,Novex Technologies,Technology,142.5
HLRA,Haloran Healthcare,Healthcare,68.3
CRDB,Crestwood Bancorp,Financials,31.7
PKVL,Peakville Consumer Brands,Consumer Staples,54.2
ZYNQ,Zynquo Systems,Technology,23.8
MRDN,Meridian Energy Partners,Energy,87.6
FLXM,Flexium Therapeutics,Healthcare,12.4
ATWR,Atlas Tower REIT,Real Estate,19.1

data/samples/financials.csv — 3 years per company:

ticker,year,revenue_millions,ebitda_millions,net_income_millions,total_debt_millions,cash_millions,shares_outstanding_millions
NVEX,2023,51432,14901,10287,8200,22500,1250
NVEX,2024,58920,17676,12480,7800,28100,1255
NVEX,2025,67158,20820,14728,7200,35400,1260
HLRA,2023,24180,5803,3264,12400,4800,890
HLRA,2024,26112,6267,3524,11900,5200,895
HLRA,2025,27918,6980,3908,11200,5800,900
CRDB,2023,8940,3308,2234,45200,3200,520
CRDB,2024,9387,3474,2346,44800,3500,525
CRDB,2025,9856,3646,2464,44100,3900,528
PKVL,2023,32100,5778,3531,9800,6200,780
PKVL,2024,33384,5675,3338,10200,5800,785
PKVL,2025,34385,6189,3610,9900,6400,788
ZYNQ,2023,8420,1263,-842,4200,2100,340
ZYNQ,2024,12630,2526,126,3800,3400,355
ZYNQ,2025,17682,4420,1946,3200,5800,370
MRDN,2023,41200,12360,7208,18500,8200,680
MRDN,2024,38140,10678,5721,19200,7100,680
MRDN,2025,43260,12978,8220,17800,9400,685
FLXM,2023,3240,162,-1296,2800,1800,210
FLXM,2024,4536,680,-454,2600,1400,225
FLXM,2025,6350,1524,508,2200,1900,240
ATWR,2023,4820,2892,1205,14200,1200,380
ATWR,2024,5060,3036,1265,14800,1100,382
ATWR,2025,5313,3188,1329,15400,1000,385

data/samples/prices.csv — Recent daily prices (generate 20-30 trading days per company):

Generate realistic price series with:

• Prices consistent with market cap and shares outstanding
• Daily moves of -3% to +3% (normal variation)
• Volume in the millions for large-caps, hundreds of thousands for small-caps

Key characteristics of the sample data:

Company	Story	Why It's Useful
NVEX	Large-cap tech, strong growth, high margins	Base case — healthy company
HLRA	Mid-cap healthcare, steady growth	Defensive sector comparison
CRDB	Regional bank, high leverage (normal for financials)	Tests debt-heavy analysis
PKVL	Consumer staples, stable but flat margins	Low-growth value stock
ZYNQ	Small-cap tech, recently turned profitable	Tests negative-to-positive earnings
MRDN	Energy, cyclical revenue	Tests volatility in fundamentals
FLXM	Biotech, burning cash, just turned profitable	Edge case: negative earnings history
ATWR	REIT, high debt, stable income	Tests capital-intensive model

8. Generate Data Loader Module

Create data/loader.py at the repo root:

"""
Data loader module.

Loads sample financial data from CSV files. Designed to be swapped
for real API sources later — just change the implementation inside
each function while keeping the same return types.

Swap guide:
- Replace pd.read_csv(...) with your API call
- Validate with Pydantic models before returning
- Keep the same function signatures so downstream code doesn't break
"""

import pandas as pd
from pathlib import Path
from typing import Optional

# Sample data lives here during development.
# Swap this path (or the whole function) for real data sources.
SAMPLE_DIR = Path(__file__).parent / "samples"


def load_companies() -> pd.DataFrame:
    """
    Load the company universe.

    Returns DataFrame with columns:
        ticker, name, sector, market_cap_billions

    To swap for real data:
        return your_api.get_company_list()
    """
    return pd.read_csv(SAMPLE_DIR / "companies.csv")


def load_financials(ticker: Optional[str] = None) -> pd.DataFrame:
    """
    Load financial statements.

    Args:
        ticker: Filter to a single company. None returns all.

    Returns DataFrame with columns:
        ticker, year, revenue_millions, ebitda_millions,
        net_income_millions, total_debt_millions, cash_millions,
        shares_outstanding_millions

    To swap for real data:
        return your_api.get_financials(ticker)
    """
    df = pd.read_csv(SAMPLE_DIR / "financials.csv")
    if ticker:
        df = df[df["ticker"] == ticker]
    return df


def load_prices(ticker: Optional[str] = None) -> pd.DataFrame:
    """
    Load price history.

    Args:
        ticker: Filter to a single company. None returns all.

    Returns DataFrame with columns:
        ticker, date, close, volume

    To swap for real data:
        return yfinance.download(ticker)
    """
    df = pd.read_csv(SAMPLE_DIR / "prices.csv", parse_dates=["date"])
    if ticker:
        df = df[df["ticker"] == ticker]
    return df

Also create data/__init__.py:

"""Financial data loading and validation."""

from data.loader import load_companies, load_financials, load_prices
from data.models import Company, FinancialStatement, PriceHistory

__all__ = [
    "load_companies",
    "load_financials",
    "load_prices",
    "Company",
    "FinancialStatement",
    "PriceHistory",
]

9. Create a Validation Example

Create data/validate_sample.py — a quick script that loads sample data and validates it against Pydantic models:

"""
Quick validation: loads sample CSVs and validates every row against Pydantic models.
Run this after editing sample data to catch schema issues early.

Usage: python -m data.validate_sample
"""

from data.loader import load_companies, load_financials, load_prices
from data.models import Company, FinancialStatement, PriceHistory


def validate():
    errors = []

    companies = load_companies()
    for _, row in companies.iterrows():
        try:
            Company(**row.to_dict())
        except Exception as e:
            errors.append(f"Company {row.get('ticker', '?')}: {e}")

    financials = load_financials()
    for _, row in financials.iterrows():
        try:
            FinancialStatement(**row.to_dict())
        except Exception as e:
            errors.append(f"Financial {row.get('ticker', '?')} {row.get('year', '?')}: {e}")

    prices = load_prices()
    for _, row in prices.iterrows():
        try:
            PriceHistory(**row.to_dict())
        except Exception as e:
            errors.append(f"Price {row.get('ticker', '?')} {row.get('date', '?')}: {e}")

    if errors:
        print(f"VALIDATION FAILED — {len(errors)} error(s):")
        for err in errors:
            print(f"  - {err}")
        return False
    else:
        print(f"ALL VALID — {len(companies)} companies, {len(financials)} financial records, {len(prices)} price records")
        return True


if __name__ == "__main__":
    validate()

10. Review the Data (Before Moving On)

After generating sample data, the developer should look at it — not just trust it. This is a professional habit: never compute on data you haven't eyeballed.

"Before we build on this data, take a quick look:

import pandas as pd

companies = pd.read_csv('data/samples/companies.csv')
financials = pd.read_csv('data/samples/financials.csv')

# Summary stats — do the numbers look plausible?
print(financials.describe())

# Any nulls or weird values?
print(financials.info())

# Spot-check: pick one company, read its 3 years
print(financials[financials['ticker'] == 'NVEX'])

What to look for:

• Do revenue/earnings magnitudes make sense for the sector?
• Are there any nulls, zeros, or negative values that shouldn't be there?
• Does the growth trajectory look realistic (not too smooth, not too wild)?
• Are the relationships sensible? (e.g., EBITDA < Revenue, Cash > 0)

Anything look off? Tell me and I'll fix the sample data.

Please review the output and confirm before we proceed — everything we build next depends on this data being correct."

Wait for explicit confirmation. If the developer confirms, proceed. If they ask to skip, note the risk once:

"Understood. Flagging that any issues in the sample data will propagate into every calculation and screen we build from here. This is a conscious decision to proceed without review."

This step matters. If bad data enters the pipeline here, every calculation downstream will look right but be wrong. Five minutes of review now prevents hours of debugging later.

11. Suggest Next Step

Once the data is created and reviewed, proactively suggest building:

"I've created the data foundation for [Section Title]:

data/
├── __init__.py              # Package exports
├── models.py                # Pydantic models (Company, FinancialStatement, PriceHistory)
├── loader.py                # Data loading functions (swap CSV for API later)
├── validate_sample.py       # Validation script
└── samples/
    ├── companies.csv        # 8 companies across 5 sectors
    ├── financials.csv       # 3 years of financials per company
    └── prices.csv           # Recent daily price history

Quick test:

python -m data.validate_sample

Key design decisions:

• CSV format (not JSON) — works naturally with pandas
• Pydantic models validate at the boundary — catch bad data before it hits calculations
• loader.py functions are the swap point — replace CSV reads with API calls later, same interface

Want me to build the analysis tool now? I'll create the Streamlit app that uses this data and you can see it running."

If they agree, proceed directly to building via the implement-screen flow.

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Adapting to the Project Domain

The equity analysis example above is the default. Adapt the entities and sample data to match whatever the developer is building:

Domain	Key Entities	Sample Data Focus
Equity Valuation	Company, FinancialStatement, PriceHistory	Revenue, margins, multiples
FX Trading	CurrencyPair, ExchangeRate, ForwardRate	Bid/ask, cross rates, carry
Futures	FuturesContract, MarginRequirement, RollSchedule	Expiry, settlement, contango/backwardation
Options	OptionChain, Greeks, UnderlyingPrice	Strike, expiry, IV, delta/gamma/theta/vega
Crypto	Token, OHLCVBar, OnChainMetric	Price, volume, market cap, hash rate, TVL
Fixed Income	Bond, Issuer, YieldCurve	Coupon, maturity, credit rating, duration
Portfolio Optimization	Asset, Returns, Constraints	Historical returns, correlations, weights
Risk Management	Position, RiskFactor, Scenario	Greeks, VaR inputs, stress tests
Credit Analysis	Borrower, Loan, CreditScore	Default rates, LTV, debt service
Commodities	Commodity, SpotPrice, SeasonalPattern	Supply/demand, storage costs, weather
Multi-Asset	Asset, AssetClass, Allocation	Cross-asset returns, rebalancing rules

The pattern is always the same:

1. Pydantic models define the schema
2. CSV files hold sample data
3. loader.py provides the swap point
4. A validation script confirms everything is consistent

---

Proactive Flow

As a Cognition Mate:

• Propose data entities and sample companies based on the spec
• Generate data immediately once structure is approved
• Include edge cases without being asked — that's your job
• Suggest building the tool once data is ready
• Show don't tell — get something running

---

Guiding Principles

• Realistic data — Plausible financials, not round-number placeholders
• 5-8 sample companies — Enough to show variation across sectors, cap sizes, and financial health
• 3 years of history — Minimum to calculate growth rates and trends
• Edge cases built in — Negative earnings, high leverage, cyclical revenue
• CSV + pandas — The natural format for Python/finance work
• Pydantic at the boundary — Validate when data enters, trust it downstream
• Swappable loaders — Same interface whether data comes from CSV or Bloomberg
• Match the global data model — If data-model.md exists, align entity names
• KISS — Don't over-engineer the data layer; it's a foundation, not a final product

---

React Projects

For React projects (type: "react" in .driver.json), generate data.json and TypeScript types instead of CSV and Pydantic models:

• Sample data goes in [project]/build/[section-id]/data.json
• Types go in [project]/build/[section-id]/types.ts
• Use the React data generation pattern described in the evolve skill

The React path follows the same principles (realistic data, edge cases, varied content) but outputs JSON with a _meta section and TypeScript interfaces with component props.