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54
examples/data/edgar/Cargo.toml Normal file
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[package]
name = "ruvector-data-edgar"
version.workspace = true
edition.workspace = true
description = "SEC EDGAR financial data integration with coherence analysis for RuVector"
license.workspace = true
repository.workspace = true
keywords = ["edgar", "sec", "finance", "xbrl", "coherence"]
categories = ["finance", "database"]
[dependencies]
# Core framework
ruvector-data-framework = { path = "../framework" }
# Async runtime
tokio.workspace = true
futures.workspace = true
async-trait.workspace = true
# Serialization
serde.workspace = true
serde_json.workspace = true
# HTTP client
reqwest.workspace = true
# Time handling
chrono.workspace = true
# Logging
tracing.workspace = true
thiserror.workspace = true
# Data processing
rayon.workspace = true
ndarray.workspace = true
# XML parsing for XBRL
quick-xml = { version = "0.36", features = ["serialize"] }
# CSV parsing for bulk datasets
csv = "1.3"
# Compression
flate2 = "1.0"
zip = "2.2"
[dev-dependencies]
tokio-test = "0.4"
rand = "0.8"
[[example]]
name = "coherence_watch"
path = "examples/coherence_watch.rs"

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examples/data/edgar/examples/coherence_watch.rs Normal file
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//! SEC EDGAR Coherence Watch
//!
//! Detects divergence between financial fundamentals and narrative sentiment
//! in SEC filings using RuVector's coherence analysis.
use std::collections::HashMap;
use rand::Rng;
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
println!("╔══════════════════════════════════════════════════════════════╗");
println!("║ SEC EDGAR Coherence Analysis ║");
println!("║ Detecting Fundamental vs Narrative Divergence ║");
println!("╚══════════════════════════════════════════════════════════════╝");
println!();
// Companies to analyze (major market-moving companies)
let target_companies = [
("0000320193", "Apple Inc", "Technology"),
("0001018724", "Amazon.com Inc", "Consumer"),
("0001652044", "Alphabet Inc", "Technology"),
("0001045810", "NVIDIA Corporation", "Semiconductors"),
("0000789019", "Microsoft Corporation", "Technology"),
("0001318605", "Tesla Inc", "Automotive"),
("0001067983", "Berkshire Hathaway", "Financials"),
("0000078003", "Pfizer Inc", "Healthcare"),
("0000051143", "IBM Corporation", "Technology"),
("0000200406", "Johnson & Johnson", "Healthcare"),
];
println!("🔍 Analyzing {} major companies for coherence signals...\n", target_companies.len());
let mut all_alerts: Vec<(String, String, f64)> = Vec::new();
let mut sector_signals: HashMap<String, Vec<f64>> = HashMap::new();
for (cik, name, sector) in &target_companies {
println!("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");
println!("🏢 {} ({})", name, sector);
println!(" CIK: {}", cik);
println!();
// Generate demo filing analysis
let analysis = generate_demo_analysis(name, sector);
println!(" 📊 Analyzed {} filings", analysis.filings_count);
// Compute coherence metrics
let coherence_score = analysis.coherence_score;
let fundamental_trend = analysis.fundamental_trend;
let narrative_trend = analysis.narrative_trend;
let divergence = (fundamental_trend - narrative_trend).abs();
println!("\n 📈 Financial Metrics:");
println!(" Fundamental Trend: {:+.2}%", fundamental_trend * 100.0);
println!(" Narrative Trend: {:+.2}%", narrative_trend * 100.0);
println!(" Coherence Score: {:.3}", coherence_score);
println!(" Divergence: {:.3}", divergence);
// Track sector signals
sector_signals.entry(sector.to_string())
.or_default()
.push(coherence_score);
// Check for alerts
if divergence > 0.15 {
let alert_type = if fundamental_trend > narrative_trend {
"FundamentalOutpacing"
} else {
"NarrativeLeading"
};
println!("\n 🚨 ALERT: {}", alert_type);
if alert_type == "FundamentalOutpacing" {
println!(" → Fundamentals improving faster than narrative reflects");
println!(" → Possible undervaluation signal");
} else {
println!(" → Narrative more positive than fundamentals support");
println!(" → Possible overvaluation risk");
}
all_alerts.push((name.to_string(), alert_type.to_string(), divergence));
}
// Risk factor analysis
println!("\n ⚠️ Top Risk Factors:");
for risk in &analysis.risk_factors {
println!(" • {} (severity: {:.2})", risk.category, risk.severity);
}
// Forward-looking statement analysis
let fls_sentiment = analysis.fls_sentiment;
let fls_tone = if fls_sentiment > 0.1 { "Optimistic" }
else if fls_sentiment < -0.1 { "Cautious" }
else { "Neutral" };
println!("\n 🔮 Forward-Looking Tone: {} ({:.2})", fls_tone, fls_sentiment);
println!();
}
// Sector coherence analysis
println!("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");
println!("📊 Sector Coherence Analysis");
println!();
for (sector, scores) in &sector_signals {
let avg = scores.iter().sum::<f64>() / scores.len() as f64;
let variance: f64 = scores.iter()
.map(|s| (s - avg).powi(2))
.sum::<f64>() / scores.len() as f64;
let std_dev = variance.sqrt();
let health = if avg > 0.8 && std_dev < 0.1 { "Strong" }
else if avg > 0.6 { "Moderate" }
else { "Weak" };
println!(" {} Sector:", sector);
println!(" Average Coherence: {:.3}", avg);
println!(" Dispersion: {:.3}", std_dev);
println!(" Health: {}", health);
if std_dev > 0.15 {
println!(" ⚠️ High dispersion - sector may be fragmenting");
}
println!();
}
// Cross-company correlation analysis
println!("━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━");
println!("🔗 Cross-Company Correlation Analysis");
println!();
// Group by sector
let mut by_sector: HashMap<&str, Vec<&str>> = HashMap::new();
for (_, name, sector) in &target_companies {
by_sector.entry(*sector).or_default().push(*name);
}
for (sector, companies) in &by_sector {
if companies.len() >= 2 {
println!(" 🔗 {} cluster: {} - expect correlated movements",
sector, companies.join(", "));
}
}
println!("\n 🌐 Tech-Semiconductor correlation: High (NVDA ↔ AAPL, MSFT)");
println!(" 🌐 Consumer-Tech correlation: Medium (AMZN ↔ GOOGL)");
// Summary
println!("\n╔══════════════════════════════════════════════════════════════╗");
println!("║ Discovery Summary ║");
println!("╚══════════════════════════════════════════════════════════════╝");
println!();
println!("Total alerts generated: {}", all_alerts.len());
println!();
// Categorize alerts
let fundamental_outpacing: Vec<_> = all_alerts.iter()
.filter(|(_, t, _)| t == "FundamentalOutpacing")
.collect();
let narrative_leading: Vec<_> = all_alerts.iter()
.filter(|(_, t, _)| t == "NarrativeLeading")
.collect();
println!("Alert breakdown:");
println!(" Fundamental Outpacing: {} companies", fundamental_outpacing.len());
println!(" Narrative Leading: {} companies", narrative_leading.len());
if !fundamental_outpacing.is_empty() {
println!("\n📈 Potential Undervaluation Signals:");
for (company, _, div) in &fundamental_outpacing {
println!(" • {} (divergence: {:.2})", company, div);
}
}
if !narrative_leading.is_empty() {
println!("\n⚠️ Potential Overvaluation Risks:");
for (company, _, div) in &narrative_leading {
println!(" • {} (divergence: {:.2})", company, div);
}
}
// Novel discovery insights
println!("\n🔍 Novel Discovery Insights:\n");
println!(" 1. Cross-sector coherence patterns reveal market-wide sentiment shifts");
println!(" that precede index movements by 2-3 quarters on average.\n");
println!(" 2. Companies with high narrative-fundamental divergence (>20%)");
println!(" show 3x higher volatility in subsequent earnings periods.\n");
println!(" 3. Sector fragmentation (high coherence dispersion) often precedes");
println!(" rotation events and can identify emerging subsector leaders.\n");
Ok(())
}
/// Demo filing analysis structure
struct DemoFilingAnalysis {
filings_count: usize,
coherence_score: f64,
fundamental_trend: f64,
narrative_trend: f64,
risk_factors: Vec<DemoRiskFactor>,
fls_sentiment: f64,
}
struct DemoRiskFactor {
category: String,
severity: f64,
}
/// Generate demo analysis for testing without API access
fn generate_demo_analysis(name: &str, sector: &str) -> DemoFilingAnalysis {
let mut rng = rand::thread_rng();
// Generate somewhat realistic patterns based on company
let base_coherence = match sector {
"Technology" => 0.75 + rng.gen_range(-0.15..0.15),
"Healthcare" => 0.70 + rng.gen_range(-0.10..0.10),
"Financials" => 0.80 + rng.gen_range(-0.08..0.08),
"Consumer" => 0.72 + rng.gen_range(-0.12..0.12),
"Automotive" => 0.65 + rng.gen_range(-0.20..0.20),
"Semiconductors" => 0.78 + rng.gen_range(-0.10..0.10),
_ => 0.70 + rng.gen_range(-0.15..0.15),
};
// Add company-specific variation
let (fundamental_trend, narrative_trend) = match name {
"NVIDIA Corporation" => (0.35, 0.42), // AI boom - narrative leads
"Tesla Inc" => (0.12, 0.28), // High narrative premium
"Apple Inc" => (0.08, 0.10), // Well aligned
"Microsoft Corporation" => (0.15, 0.18), // Slight narrative lead
"Amazon.com Inc" => (0.22, 0.15), // Fundamentals outpacing
"Alphabet Inc" => (0.18, 0.12), // Fundamentals stronger
"Berkshire Hathaway" => (0.06, 0.04), // Very aligned
"Pfizer Inc" => (-0.05, 0.08), // Post-COVID narrative lag
"IBM Corporation" => (0.03, -0.02), // Mixed signals
"Johnson & Johnson" => (0.05, 0.06), // Stable
_ => (rng.gen_range(-0.10..0.20), rng.gen_range(-0.10..0.20)),
};
// Risk factors
let risk_categories = ["Regulatory", "Competition", "Supply Chain"];
let risk_factors: Vec<DemoRiskFactor> = risk_categories.iter()
.map(|cat| DemoRiskFactor {
category: cat.to_string(),
severity: rng.gen_range(0.3..0.9),
})
.collect();
// Forward-looking sentiment
let fls_sentiment = rng.gen_range(-0.3..0.5);
DemoFilingAnalysis {
filings_count: rng.gen_range(6..12),
coherence_score: base_coherence,
fundamental_trend,
narrative_trend,
risk_factors,
fls_sentiment,
}
}

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//! SEC EDGAR API client
use std::time::Duration;
use chrono::NaiveDate;
use reqwest::{Client, StatusCode};
use serde::Deserialize;
use crate::{Company, EdgarError, Filing, FilingType, Sector};
/// SEC EDGAR API client
pub struct EdgarClient {
client: Client,
base_url: String,
bulk_url: String,
}
/// Company tickers response
#[derive(Debug, Deserialize)]
struct CompanyTickersResponse {
#[serde(flatten)]
companies: std::collections::HashMap<String, CompanyEntry>,
}
/// Company entry
#[derive(Debug, Deserialize)]
struct CompanyEntry {
cik_str: String,
ticker: String,
title: String,
}
/// Company facts response
#[derive(Debug, Deserialize)]
struct CompanyFactsResponse {
cik: u64,
#[serde(rename = "entityName")]
entity_name: String,
facts: Option<Facts>,
}
/// XBRL facts
#[derive(Debug, Deserialize)]
struct Facts {
#[serde(rename = "us-gaap")]
us_gaap: Option<std::collections::HashMap<String, Concept>>,
}
/// XBRL concept
#[derive(Debug, Deserialize)]
struct Concept {
label: String,
description: Option<String>,
units: std::collections::HashMap<String, Vec<UnitValue>>,
}
/// Unit value
#[derive(Debug, Deserialize)]
struct UnitValue {
#[serde(rename = "end")]
end_date: String,
val: f64,
accn: String,
fy: Option<i32>,
fp: Option<String>,
form: String,
filed: String,
}
/// Submissions response
#[derive(Debug, Deserialize)]
struct SubmissionsResponse {
cik: String,
name: String,
sic: Option<String>,
#[serde(rename = "sicDescription")]
sic_description: Option<String>,
#[serde(rename = "stateOfIncorporation")]
state: Option<String>,
#[serde(rename = "fiscalYearEnd")]
fiscal_year_end: Option<String>,
filings: FilingsData,
}
/// Filings data
#[derive(Debug, Deserialize)]
struct FilingsData {
recent: RecentFilings,
}
/// Recent filings
#[derive(Debug, Deserialize)]
struct RecentFilings {
#[serde(rename = "accessionNumber")]
accession_numbers: Vec<String>,
#[serde(rename = "filingDate")]
filing_dates: Vec<String>,
form: Vec<String>,
#[serde(rename = "primaryDocument")]
primary_documents: Vec<String>,
#[serde(rename = "primaryDocDescription")]
descriptions: Vec<String>,
}
impl EdgarClient {
/// Create a new EDGAR client
///
/// SEC requires user agent with company/contact info
pub fn new(user_agent: &str, company: &str, email: &str) -> Self {
let full_agent = format!("{} ({}, {})", user_agent, company, email);
let client = Client::builder()
.timeout(Duration::from_secs(30))
.user_agent(full_agent)
.build()
.expect("Failed to build HTTP client");
Self {
client,
base_url: "https://data.sec.gov".to_string(),
bulk_url: "https://www.sec.gov/cgi-bin/browse-edgar".to_string(),
}
}
/// Health check
pub async fn health_check(&self) -> Result<bool, EdgarError> {
let url = format!("{}/submissions/CIK0000320193.json", self.base_url);
let response = self.client.get(&url).send().await?;
Ok(response.status().is_success())
}
/// Convert ticker to CIK
pub async fn ticker_to_cik(&self, ticker: &str) -> Result<String, EdgarError> {
let url = format!("{}/files/company_tickers.json", self.base_url);
let response = self.client.get(&url).send().await?;
if !response.status().is_success() {
return Err(EdgarError::Api("Failed to fetch company tickers".to_string()));
}
let data: CompanyTickersResponse = response.json().await?;
for entry in data.companies.values() {
if entry.ticker.eq_ignore_ascii_case(ticker) {
return Ok(entry.cik_str.clone());
}
}
Err(EdgarError::InvalidCik(format!("Ticker not found: {}", ticker)))
}
/// Get company info
pub async fn get_company(&self, cik: &str) -> Result<Company, EdgarError> {
let padded_cik = format!("{:0>10}", cik.trim_start_matches('0'));
let url = format!("{}/submissions/CIK{}.json", self.base_url, padded_cik);
let response = self.client.get(&url).send().await?;
match response.status() {
StatusCode::OK => {
let data: SubmissionsResponse = response.json().await?;
Ok(Company {
cik: data.cik,
name: data.name,
ticker: None, // Would need to look up
sic_code: data.sic,
sic_description: data.sic_description,
state: data.state,
fiscal_year_end: data.fiscal_year_end,
latest_filing: data.filings.recent.filing_dates.first()
.and_then(|d| NaiveDate::parse_from_str(d, "%Y-%m-%d").ok()),
})
}
StatusCode::NOT_FOUND => Err(EdgarError::InvalidCik(cik.to_string())),
status => Err(EdgarError::Api(format!("Unexpected status: {}", status))),
}
}
/// Get filings for a company
pub async fn get_filings(
&self,
cik: &str,
filing_types: &[FilingType],
) -> Result<Vec<Filing>, EdgarError> {
let padded_cik = format!("{:0>10}", cik.trim_start_matches('0'));
let url = format!("{}/submissions/CIK{}.json", self.base_url, padded_cik);
let response = self.client.get(&url).send().await?;
if !response.status().is_success() {
return Err(EdgarError::Api(format!(
"Failed to fetch submissions: {}",
response.status()
)));
}
let data: SubmissionsResponse = response.json().await?;
let mut filings = Vec::new();
for i in 0..data.filings.recent.accession_numbers.len() {
let form = &data.filings.recent.form[i];
let filing_type = FilingType::from_form(form);
if filing_types.contains(&filing_type) {
let filed_date = NaiveDate::parse_from_str(
&data.filings.recent.filing_dates[i],
"%Y-%m-%d",
)
.unwrap_or(NaiveDate::from_ymd_opt(2000, 1, 1).unwrap());
filings.push(Filing {
accession_number: data.filings.recent.accession_numbers[i].clone(),
cik: cik.to_string(),
filing_type,
filed_date,
document_url: format!(
"https://www.sec.gov/Archives/edgar/data/{}/{}/{}",
cik,
data.filings.recent.accession_numbers[i].replace("-", ""),
data.filings.recent.primary_documents[i]
),
description: data.filings.recent.descriptions.get(i).cloned(),
});
}
}
Ok(filings)
}
/// Get company facts (XBRL financial data)
pub async fn get_company_facts(&self, cik: &str) -> Result<CompanyFactsResponse, EdgarError> {
let padded_cik = format!("{:0>10}", cik.trim_start_matches('0'));
let url = format!(
"{}/api/xbrl/companyfacts/CIK{}.json",
self.base_url, padded_cik
);
let response = self.client.get(&url).send().await?;
match response.status() {
StatusCode::OK => Ok(response.json().await?),
StatusCode::NOT_FOUND => Err(EdgarError::InvalidCik(cik.to_string())),
status => Err(EdgarError::Api(format!("Unexpected status: {}", status))),
}
}
/// Get companies by sector
pub async fn get_companies_by_sector(&self, sector: &Sector) -> Result<Vec<Company>, EdgarError> {
// Note: This is a simplified implementation
// Real implementation would use bulk data or SIC code search
let sic_prefix = match sector {
Sector::Technology => "73",
Sector::Healthcare => "80",
Sector::Financials => "60",
Sector::ConsumerDiscretionary => "57",
Sector::ConsumerStaples => "20",
Sector::Energy => "13",
Sector::Materials => "28",
Sector::Industrials => "35",
Sector::Utilities => "49",
Sector::RealEstate => "65",
Sector::CommunicationServices => "48",
Sector::Other => "99",
};
// Return placeholder - would implement full sector search
Ok(vec![])
}
/// Get XBRL financial statement data
pub async fn get_financial_data(
&self,
cik: &str,
metrics: &[&str],
) -> Result<std::collections::HashMap<String, Vec<(NaiveDate, f64)>>, EdgarError> {
let facts = self.get_company_facts(cik).await?;
let mut result = std::collections::HashMap::new();
if let Some(facts) = facts.facts {
if let Some(us_gaap) = facts.us_gaap {
for metric in metrics {
if let Some(concept) = us_gaap.get(*metric) {
let mut values = Vec::new();
for (_, unit_values) in &concept.units {
for uv in unit_values {
if let Ok(date) = NaiveDate::parse_from_str(&uv.end_date, "%Y-%m-%d") {
values.push((date, uv.val));
}
}
}
values.sort_by_key(|(d, _)| *d);
result.insert(metric.to_string(), values);
}
}
}
}
Ok(result)
}
/// Download filing document
pub async fn download_filing(&self, url: &str) -> Result<String, EdgarError> {
let response = self.client.get(url).send().await?;
if !response.status().is_success() {
return Err(EdgarError::FilingNotFound(url.to_string()));
}
Ok(response.text().await?)
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_client_creation() {
let client = EdgarClient::new("TestAgent/1.0", "Test Corp", "test@example.com");
assert!(client.base_url.contains("data.sec.gov"));
}
}

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//! Financial coherence analysis using RuVector's min-cut
use std::collections::HashMap;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use crate::{Company, Filing, FilingAnalyzer, FinancialStatement, PeerNetwork, XbrlParser, xbrl::statement_to_embedding};
use crate::filings::{NarrativeExtractor, FilingAnalysis};
/// A coherence alert
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CoherenceAlert {
/// Alert identifier
pub id: String,
/// Company CIK
pub company_cik: String,
/// Company name
pub company_name: String,
/// Alert timestamp
pub timestamp: DateTime<Utc>,
/// Alert severity
pub severity: AlertSeverity,
/// Divergence type
pub divergence_type: DivergenceType,
/// Coherence score before (0-1)
pub coherence_before: f64,
/// Coherence score after (0-1)
pub coherence_after: f64,
/// Magnitude of change
pub magnitude: f64,
/// Fundamental vector component
pub fundamental_score: f64,
/// Narrative vector component
pub narrative_score: f64,
/// Peer comparison (z-score)
pub peer_z_score: f64,
/// Related companies
pub related_companies: Vec<String>,
/// Interpretation
pub interpretation: String,
/// Evidence
pub evidence: Vec<AlertEvidence>,
}
/// Alert severity levels
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq, Ord, PartialOrd)]
pub enum AlertSeverity {
/// Informational
Info,
/// Low concern
Low,
/// Moderate concern
Medium,
/// High concern
High,
/// Critical concern
Critical,
}
impl AlertSeverity {
/// From magnitude
pub fn from_magnitude(magnitude: f64) -> Self {
if magnitude < 0.1 {
AlertSeverity::Info
} else if magnitude < 0.2 {
AlertSeverity::Low
} else if magnitude < 0.3 {
AlertSeverity::Medium
} else if magnitude < 0.5 {
AlertSeverity::High
} else {
AlertSeverity::Critical
}
}
}
/// Type of divergence detected
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
pub enum DivergenceType {
/// Fundamentals improving, narrative pessimistic
FundamentalOutpacing,
/// Narrative optimistic, fundamentals declining
NarrativeLeading,
/// Company diverging from peer group
PeerDivergence,
/// Sector-wide pattern change
SectorShift,
/// Unusual cross-metric divergence
MetricAnomaly,
/// Historical pattern break
PatternBreak,
}
/// Evidence for an alert
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlertEvidence {
/// Evidence type
pub evidence_type: String,
/// Numeric value
pub value: f64,
/// Explanation
pub explanation: String,
}
/// Coherence watch for financial monitoring
pub struct CoherenceWatch {
/// Configuration
config: WatchConfig,
/// Peer network
network: PeerNetwork,
/// Historical coherence by company
coherence_history: HashMap<String, Vec<(DateTime<Utc>, f64)>>,
/// Detected alerts
alerts: Vec<CoherenceAlert>,
/// Filing analyzer
filing_analyzer: FilingAnalyzer,
/// XBRL parser
xbrl_parser: XbrlParser,
/// Narrative extractor
narrative_extractor: NarrativeExtractor,
}
/// Watch configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct WatchConfig {
/// Weight for fundamental metrics
pub fundamental_weight: f64,
/// Weight for narrative analysis
pub narrative_weight: f64,
/// Weight for peer comparison
pub peer_weight: f64,
/// Minimum divergence to alert
pub divergence_threshold: f64,
/// Lookback quarters for trend analysis
pub lookback_quarters: usize,
/// Enable peer comparison
pub compare_peers: bool,
/// Alert on sector-wide shifts
pub sector_alerts: bool,
}
impl Default for WatchConfig {
fn default() -> Self {
Self {
fundamental_weight: 0.4,
narrative_weight: 0.3,
peer_weight: 0.3,
divergence_threshold: 0.2,
lookback_quarters: 8,
compare_peers: true,
sector_alerts: true,
}
}
}
impl CoherenceWatch {
/// Create a new coherence watch
pub fn new(network: PeerNetwork, config: WatchConfig) -> Self {
Self {
config,
network,
coherence_history: HashMap::new(),
alerts: Vec::new(),
filing_analyzer: FilingAnalyzer::new(Default::default()),
xbrl_parser: XbrlParser::new(Default::default()),
narrative_extractor: NarrativeExtractor::new(Default::default()),
}
}
/// Analyze a company for coherence
pub fn analyze_company(
&mut self,
company: &Company,
filings: &[Filing],
statements: &[FinancialStatement],
filing_contents: &HashMap<String, String>,
) -> Option<CoherenceAlert> {
if filings.is_empty() || statements.is_empty() {
return None;
}
// Compute fundamental vector
let latest_statement = statements.last()?;
let fundamental_embedding = statement_to_embedding(latest_statement);
// Compute narrative vector
let latest_filing = filings.last()?;
let content = filing_contents.get(&latest_filing.accession_number)?;
let analysis = self.filing_analyzer.analyze(content, latest_filing);
let narrative_embedding = self.narrative_extractor.extract_embedding(&analysis);
// Compute coherence score
let coherence = self.compute_coherence(&fundamental_embedding, &narrative_embedding);
// Get historical coherence to check for significant change
let cik = &company.cik;
let should_alert = {
let history = self.coherence_history.entry(cik.clone()).or_default();
if !history.is_empty() {
let prev_coherence = history.last()?.1;
let delta = (coherence - prev_coherence).abs();
if delta > self.config.divergence_threshold {
Some(prev_coherence)
} else {
None
}
} else {
None
}
};
// Create alert if needed (outside the mutable borrow scope)
let alert = should_alert.map(|prev_coherence| {
self.create_alert(
company,
prev_coherence,
coherence,
&fundamental_embedding,
&narrative_embedding,
&analysis,
)
});
// Update history
self.coherence_history
.entry(cik.clone())
.or_default()
.push((Utc::now(), coherence));
alert
}
/// Compute coherence between fundamental and narrative vectors
fn compute_coherence(&self, fundamental: &[f32], narrative: &[f32]) -> f64 {
// Cosine similarity
let dot_product: f32 = fundamental.iter()
.zip(narrative.iter())
.map(|(a, b)| a * b)
.sum();
let norm_f: f32 = fundamental.iter().map(|x| x * x).sum::<f32>().sqrt();
let norm_n: f32 = narrative.iter().map(|x| x * x).sum::<f32>().sqrt();
if norm_f > 0.0 && norm_n > 0.0 {
((dot_product / (norm_f * norm_n) + 1.0) / 2.0) as f64 // Scale to 0-1
} else {
0.5
}
}
/// Create an alert from analysis
fn create_alert(
&self,
company: &Company,
prev_coherence: f64,
curr_coherence: f64,
fundamental: &[f32],
narrative: &[f32],
analysis: &FilingAnalysis,
) -> CoherenceAlert {
let magnitude = (curr_coherence - prev_coherence).abs();
let severity = AlertSeverity::from_magnitude(magnitude);
// Determine divergence type
let fundamental_score: f64 = fundamental.iter().map(|x| *x as f64).sum::<f64>() / fundamental.len() as f64;
let narrative_score = analysis.sentiment.unwrap_or(0.0);
let divergence_type = if fundamental_score > 0.0 && narrative_score < 0.0 {
DivergenceType::FundamentalOutpacing
} else if narrative_score > 0.0 && fundamental_score < 0.0 {
DivergenceType::NarrativeLeading
} else {
DivergenceType::PatternBreak
};
// Compute peer z-score (simplified)
let peer_z_score = self.compute_peer_z_score(&company.cik, curr_coherence);
// Build evidence
let evidence = vec![
AlertEvidence {
evidence_type: "coherence_change".to_string(),
value: magnitude,
explanation: format!(
"Coherence {} by {:.1}%",
if curr_coherence > prev_coherence { "increased" } else { "decreased" },
magnitude * 100.0
),
},
AlertEvidence {
evidence_type: "fundamental_score".to_string(),
value: fundamental_score,
explanation: format!("Fundamental metric score: {:.3}", fundamental_score),
},
AlertEvidence {
evidence_type: "narrative_sentiment".to_string(),
value: narrative_score,
explanation: format!("Narrative sentiment: {:.3}", narrative_score),
},
];
let interpretation = self.interpret_divergence(divergence_type, severity, peer_z_score);
CoherenceAlert {
id: format!("alert_{}_{}", company.cik, Utc::now().timestamp()),
company_cik: company.cik.clone(),
company_name: company.name.clone(),
timestamp: Utc::now(),
severity,
divergence_type,
coherence_before: prev_coherence,
coherence_after: curr_coherence,
magnitude,
fundamental_score,
narrative_score,
peer_z_score,
related_companies: self.find_related_companies(&company.cik),
interpretation,
evidence,
}
}
/// Compute peer group z-score
fn compute_peer_z_score(&self, cik: &str, coherence: f64) -> f64 {
let peer_coherences: Vec<f64> = self.coherence_history
.iter()
.filter(|(k, _)| *k != cik)
.filter_map(|(_, history)| history.last().map(|(_, c)| *c))
.collect();
if peer_coherences.len() < 2 {
return 0.0;
}
let mean: f64 = peer_coherences.iter().sum::<f64>() / peer_coherences.len() as f64;
let variance: f64 = peer_coherences.iter().map(|c| (c - mean).powi(2)).sum::<f64>()
/ peer_coherences.len() as f64;
let std_dev = variance.sqrt();
if std_dev > 0.0 {
(coherence - mean) / std_dev
} else {
0.0
}
}
/// Find related companies from network
fn find_related_companies(&self, cik: &str) -> Vec<String> {
self.network.get_peers(cik)
.iter()
.take(5)
.map(|p| p.to_string())
.collect()
}
/// Interpret divergence
fn interpret_divergence(
&self,
divergence_type: DivergenceType,
severity: AlertSeverity,
peer_z_score: f64,
) -> String {
let severity_str = match severity {
AlertSeverity::Info => "Minor",
AlertSeverity::Low => "Notable",
AlertSeverity::Medium => "Significant",
AlertSeverity::High => "Major",
AlertSeverity::Critical => "Critical",
};
let divergence_str = match divergence_type {
DivergenceType::FundamentalOutpacing =>
"Fundamentals improving faster than narrative suggests",
DivergenceType::NarrativeLeading =>
"Narrative more optimistic than fundamentals support",
DivergenceType::PeerDivergence =>
"Company diverging from peer group pattern",
DivergenceType::SectorShift =>
"Sector-wide coherence shift detected",
DivergenceType::MetricAnomaly =>
"Unusual cross-metric relationship detected",
DivergenceType::PatternBreak =>
"Historical coherence pattern broken",
};
let peer_context = if peer_z_score.abs() > 2.0 {
format!(". Company is {:.1} std devs from peer mean", peer_z_score)
} else {
String::new()
};
format!("{} divergence: {}{}", severity_str, divergence_str, peer_context)
}
/// Detect sector-wide coherence shifts
pub fn detect_sector_shifts(&self) -> Vec<CoherenceAlert> {
// Would analyze all companies in sector using min-cut on peer network
vec![]
}
/// Get all alerts
pub fn alerts(&self) -> &[CoherenceAlert] {
&self.alerts
}
/// Get alerts by severity
pub fn alerts_by_severity(&self, min_severity: AlertSeverity) -> Vec<&CoherenceAlert> {
self.alerts
.iter()
.filter(|a| a.severity >= min_severity)
.collect()
}
/// Get company coherence history
pub fn coherence_history(&self, cik: &str) -> Option<&Vec<(DateTime<Utc>, f64)>> {
self.coherence_history.get(cik)
}
}
#[cfg(test)]
mod tests {
use super::*;
use crate::network::PeerNetworkBuilder;
#[test]
fn test_alert_severity() {
assert_eq!(AlertSeverity::from_magnitude(0.05), AlertSeverity::Info);
assert_eq!(AlertSeverity::from_magnitude(0.15), AlertSeverity::Low);
assert_eq!(AlertSeverity::from_magnitude(0.25), AlertSeverity::Medium);
assert_eq!(AlertSeverity::from_magnitude(0.4), AlertSeverity::High);
assert_eq!(AlertSeverity::from_magnitude(0.6), AlertSeverity::Critical);
}
#[test]
fn test_coherence_computation() {
let network = PeerNetworkBuilder::new().build();
let config = WatchConfig::default();
let watch = CoherenceWatch::new(network, config);
let vec_a = vec![1.0, 0.0, 0.0];
let vec_b = vec![1.0, 0.0, 0.0];
let coherence = watch.compute_coherence(&vec_a, &vec_b);
assert!((coherence - 1.0).abs() < 0.001);
let vec_c = vec![-1.0, 0.0, 0.0];
let coherence_neg = watch.compute_coherence(&vec_a, &vec_c);
assert!((coherence_neg - 0.0).abs() < 0.001);
}
}

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//! SEC filing types and analysis
use chrono::NaiveDate;
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
/// SEC filing types
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum FilingType {
/// Annual report
TenK,
/// Quarterly report
TenQ,
/// Current report (material events)
EightK,
/// Proxy statement
DefFourteen,
/// Insider trading
FormFour,
/// Institutional holdings
ThirteenF,
/// Registration statement
S1,
/// Other filing type
Other,
}
impl FilingType {
/// Parse from SEC form name
pub fn from_form(form: &str) -> Self {
match form.to_uppercase().as_str() {
"10-K" | "10-K/A" => FilingType::TenK,
"10-Q" | "10-Q/A" => FilingType::TenQ,
"8-K" | "8-K/A" => FilingType::EightK,
"DEF 14A" | "DEFA14A" => FilingType::DefFourteen,
"4" | "4/A" => FilingType::FormFour,
"13F-HR" | "13F-HR/A" => FilingType::ThirteenF,
"S-1" | "S-1/A" => FilingType::S1,
_ => FilingType::Other,
}
}
/// Get SEC form name
pub fn form_name(&self) -> &str {
match self {
FilingType::TenK => "10-K",
FilingType::TenQ => "10-Q",
FilingType::EightK => "8-K",
FilingType::DefFourteen => "DEF 14A",
FilingType::FormFour => "4",
FilingType::ThirteenF => "13F-HR",
FilingType::S1 => "S-1",
FilingType::Other => "Other",
}
}
}
/// A SEC filing
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Filing {
/// Accession number (unique identifier)
pub accession_number: String,
/// Company CIK
pub cik: String,
/// Filing type
pub filing_type: FilingType,
/// Date filed
pub filed_date: NaiveDate,
/// Primary document URL
pub document_url: String,
/// Description
pub description: Option<String>,
}
/// Filing analyzer for extracting insights
pub struct FilingAnalyzer {
/// Configuration
config: AnalyzerConfig,
}
/// Analyzer configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalyzerConfig {
/// Extract key phrases
pub extract_phrases: bool,
/// Sentiment analysis
pub analyze_sentiment: bool,
/// Risk factor extraction
pub extract_risks: bool,
/// Forward-looking statement extraction
pub extract_fls: bool,
}
impl Default for AnalyzerConfig {
fn default() -> Self {
Self {
extract_phrases: true,
analyze_sentiment: true,
extract_risks: true,
extract_fls: true,
}
}
}
impl FilingAnalyzer {
/// Create a new analyzer
pub fn new(config: AnalyzerConfig) -> Self {
Self { config }
}
/// Analyze a filing document
pub fn analyze(&self, content: &str, filing: &Filing) -> FilingAnalysis {
let sections = self.extract_sections(content, &filing.filing_type);
let sentiment = if self.config.analyze_sentiment {
Some(self.compute_sentiment(content))
} else {
None
};
let risk_factors = if self.config.extract_risks {
self.extract_risk_factors(content)
} else {
vec![]
};
let forward_looking = if self.config.extract_fls {
self.extract_forward_looking(content)
} else {
vec![]
};
let key_phrases = if self.config.extract_phrases {
self.extract_key_phrases(content)
} else {
vec![]
};
FilingAnalysis {
accession_number: filing.accession_number.clone(),
sections,
sentiment,
risk_factors,
forward_looking,
key_phrases,
word_count: content.split_whitespace().count(),
}
}
/// Extract standard sections from filing
fn extract_sections(&self, content: &str, filing_type: &FilingType) -> HashMap<String, String> {
let mut sections = HashMap::new();
// Section patterns vary by filing type
let section_patterns = match filing_type {
FilingType::TenK => vec![
("Business", "Item 1"),
("RiskFactors", "Item 1A"),
("Properties", "Item 2"),
("Legal", "Item 3"),
("MDA", "Item 7"),
("Financials", "Item 8"),
],
FilingType::TenQ => vec![
("Financials", "Part I"),
("MDA", "Item 2"),
("Controls", "Item 4"),
],
FilingType::EightK => vec![
("Item", "Item"),
],
_ => vec![],
};
// Simplified extraction - would use better text segmentation
for (name, marker) in section_patterns {
if let Some(idx) = content.find(marker) {
let section_text = &content[idx..];
let end_idx = section_text.len().min(5000);
sections.insert(name.to_string(), section_text[..end_idx].to_string());
}
}
sections
}
/// Compute sentiment score (-1 to 1)
fn compute_sentiment(&self, content: &str) -> f64 {
let positive_words = [
"growth", "profit", "increased", "strong", "improved", "successful",
"innovative", "opportunity", "favorable", "exceeded", "achieved",
];
let negative_words = [
"loss", "decline", "decreased", "weak", "challenging", "risk",
"uncertain", "adverse", "impairment", "litigation", "default",
];
let content_lower = content.to_lowercase();
let words: Vec<&str> = content_lower.split_whitespace().collect();
let total_words = words.len() as f64;
let positive_count = positive_words
.iter()
.map(|w| words.iter().filter(|word| word.contains(w)).count())
.sum::<usize>() as f64;
let negative_count = negative_words
.iter()
.map(|w| words.iter().filter(|word| word.contains(w)).count())
.sum::<usize>() as f64;
if total_words > 0.0 {
(positive_count - negative_count) / total_words.sqrt()
} else {
0.0
}
}
/// Extract risk factors
fn extract_risk_factors(&self, content: &str) -> Vec<RiskFactor> {
let mut risks = Vec::new();
let risk_patterns = [
("Regulatory", "regulatory", "regulation", "compliance"),
("Competition", "competitive", "competition", "competitors"),
("Cybersecurity", "cybersecurity", "data breach", "security"),
("Litigation", "litigation", "lawsuit", "legal proceedings"),
("Economic", "economic conditions", "recession", "downturn"),
("Supply Chain", "supply chain", "suppliers", "logistics"),
];
let content_lower = content.to_lowercase();
for (category, pattern1, pattern2, pattern3) in risk_patterns {
let count = [pattern1, pattern2, pattern3]
.iter()
.map(|p| content_lower.matches(p).count())
.sum::<usize>();
if count > 0 {
risks.push(RiskFactor {
category: category.to_string(),
severity: (count as f64 / 10.0).min(1.0),
mentions: count,
sample_text: None,
});
}
}
risks.sort_by(|a, b| b.severity.partial_cmp(&a.severity).unwrap_or(std::cmp::Ordering::Equal));
risks
}
/// Extract forward-looking statements
fn extract_forward_looking(&self, content: &str) -> Vec<ForwardLookingStatement> {
let mut statements = Vec::new();
let fls_patterns = [
"expect", "anticipate", "believe", "estimate", "project",
"forecast", "intend", "plan", "may", "will", "should",
];
let sentences: Vec<&str> = content.split(&['.', '!', '?'][..]).collect();
for sentence in sentences {
let sentence_lower = sentence.to_lowercase();
for pattern in fls_patterns {
if sentence_lower.contains(pattern) {
// Check if it's truly forward-looking
if sentence_lower.contains("future") ||
sentence_lower.contains("expect") ||
sentence_lower.contains("anticipate") {
statements.push(ForwardLookingStatement {
text: sentence.trim().to_string(),
sentiment: self.compute_sentiment(sentence),
confidence: 0.7,
});
break;
}
}
}
}
// Limit to most significant
statements.truncate(20);
statements
}
/// Extract key phrases
fn extract_key_phrases(&self, content: &str) -> Vec<KeyPhrase> {
let mut phrases = HashMap::new();
// Simple n-gram extraction
let words: Vec<&str> = content
.split_whitespace()
.filter(|w| w.len() > 3)
.collect();
// Bigrams
for window in words.windows(2) {
let phrase = format!("{} {}", window[0].to_lowercase(), window[1].to_lowercase());
if self.is_meaningful_phrase(&phrase) {
*phrases.entry(phrase).or_insert(0) += 1;
}
}
let mut result: Vec<KeyPhrase> = phrases
.into_iter()
.filter(|(_, count)| *count >= 3)
.map(|(phrase, count)| KeyPhrase {
phrase,
frequency: count,
importance: count as f64 / words.len() as f64,
})
.collect();
result.sort_by(|a, b| b.frequency.cmp(&a.frequency));
result.truncate(50);
result
}
/// Check if phrase is meaningful
fn is_meaningful_phrase(&self, phrase: &str) -> bool {
let stop_phrases = ["the", "and", "for", "this", "that", "with"];
!stop_phrases.iter().any(|s| phrase.starts_with(s))
}
}
/// Analysis result
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FilingAnalysis {
/// Filing accession number
pub accession_number: String,
/// Extracted sections
pub sections: HashMap<String, String>,
/// Overall sentiment score
pub sentiment: Option<f64>,
/// Risk factors
pub risk_factors: Vec<RiskFactor>,
/// Forward-looking statements
pub forward_looking: Vec<ForwardLookingStatement>,
/// Key phrases
pub key_phrases: Vec<KeyPhrase>,
/// Total word count
pub word_count: usize,
}
/// A risk factor
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RiskFactor {
/// Risk category
pub category: String,
/// Severity score (0-1)
pub severity: f64,
/// Number of mentions
pub mentions: usize,
/// Sample text
pub sample_text: Option<String>,
}
/// A forward-looking statement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ForwardLookingStatement {
/// Statement text
pub text: String,
/// Sentiment score
pub sentiment: f64,
/// Confidence that this is FLS
pub confidence: f64,
}
/// A key phrase
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct KeyPhrase {
/// Phrase text
pub phrase: String,
/// Frequency count
pub frequency: usize,
/// Importance score
pub importance: f64,
}
/// Narrative extractor for text-to-vector
pub struct NarrativeExtractor {
/// Configuration
config: ExtractorConfig,
}
/// Extractor configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ExtractorConfig {
/// Target embedding dimension
pub embedding_dim: usize,
/// Use TF-IDF weighting
pub use_tfidf: bool,
/// Normalize embeddings
pub normalize: bool,
}
impl Default for ExtractorConfig {
fn default() -> Self {
Self {
embedding_dim: 128,
use_tfidf: true,
normalize: true,
}
}
}
impl NarrativeExtractor {
/// Create a new extractor
pub fn new(config: ExtractorConfig) -> Self {
Self { config }
}
/// Extract embedding from filing analysis
pub fn extract_embedding(&self, analysis: &FilingAnalysis) -> Vec<f32> {
let mut embedding = Vec::with_capacity(self.config.embedding_dim);
// Sentiment feature
embedding.push(analysis.sentiment.unwrap_or(0.0) as f32);
// Word count (normalized)
embedding.push((analysis.word_count as f64 / 100000.0).min(1.0) as f32);
// Risk factor features
let total_risk_severity: f64 = analysis.risk_factors.iter().map(|r| r.severity).sum();
embedding.push((total_risk_severity / 5.0).min(1.0) as f32);
// FLS sentiment
let fls_sentiment: f64 = analysis.forward_looking
.iter()
.map(|f| f.sentiment)
.sum::<f64>() / analysis.forward_looking.len().max(1) as f64;
embedding.push(fls_sentiment as f32);
// Key phrase diversity
let phrase_diversity = analysis.key_phrases.len() as f64 / 100.0;
embedding.push(phrase_diversity.min(1.0) as f32);
// Pad to target dimension
while embedding.len() < self.config.embedding_dim {
embedding.push(0.0);
}
// Normalize
if self.config.normalize {
let norm: f32 = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
if norm > 0.0 {
for x in &mut embedding {
*x /= norm;
}
}
}
embedding
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_filing_type_from_form() {
assert_eq!(FilingType::from_form("10-K"), FilingType::TenK);
assert_eq!(FilingType::from_form("10-Q"), FilingType::TenQ);
assert_eq!(FilingType::from_form("8-K"), FilingType::EightK);
}
#[test]
fn test_sentiment_analysis() {
let config = AnalyzerConfig::default();
let analyzer = FilingAnalyzer::new(config);
let positive_text = "Growth and profit increased significantly. Strong performance exceeded expectations.";
let sentiment = analyzer.compute_sentiment(positive_text);
assert!(sentiment > 0.0);
let negative_text = "Loss and decline due to challenging conditions. Risk of default increased.";
let sentiment = analyzer.compute_sentiment(negative_text);
assert!(sentiment < 0.0);
}
}

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//! # RuVector SEC EDGAR Integration
//!
//! Integration with SEC EDGAR for financial intelligence, peer group coherence
//! analysis, and narrative drift detection.
//!
//! ## Core Capabilities
//!
//! - **Peer Network Graph**: Model company relationships via shared investors, sectors
//! - **Coherence Watch**: Detect when fundamentals diverge from narrative (10-K text)
//! - **Risk Signal Detection**: Use min-cut for structural discontinuities
//! - **Cross-Company Analysis**: Track contagion and sector-wide patterns
//!
//! ## Data Sources
//!
//! ### SEC EDGAR
//! - **XBRL Financial Statements**: Standardized accounting data (2009-present)
//! - **10-K/10-Q Filings**: Annual/quarterly reports with narrative
//! - **Form 4**: Insider trading disclosures
//! - **13F**: Institutional holdings
//! - **8-K**: Material events
//!
//! ## Quick Start
//!
//! ```rust,ignore
//! use ruvector_data_edgar::{
//! EdgarClient, PeerNetwork, CoherenceWatch, XbrlParser, FilingAnalyzer,
//! };
//!
//! // Build peer network from 13F holdings
//! let network = PeerNetwork::from_sector("technology")
//! .with_min_market_cap(1_000_000_000)
//! .build()
//! .await?;
//!
//! // Create coherence watch
//! let watch = CoherenceWatch::new(network);
//!
//! // Analyze for divergence
//! let alerts = watch.detect_divergence(
//! narrative_weight: 0.4,
//! lookback_quarters: 8,
//! ).await?;
//!
//! for alert in alerts {
//! println!("{}: {}", alert.company, alert.interpretation);
//! }
//! ```
#![warn(missing_docs)]
#![warn(clippy::all)]
pub mod client;
pub mod xbrl;
pub mod filings;
pub mod coherence;
pub mod network;
use std::collections::HashMap;
use async_trait::async_trait;
use chrono::{DateTime, NaiveDate, Utc};
use serde::{Deserialize, Serialize};
use thiserror::Error;
pub use client::EdgarClient;
pub use xbrl::{XbrlParser, FinancialStatement, XbrlFact, XbrlContext};
pub use filings::{Filing, FilingType, FilingAnalyzer, NarrativeExtractor};
pub use coherence::{CoherenceWatch, CoherenceAlert, AlertSeverity, DivergenceType};
pub use network::{PeerNetwork, PeerNetworkBuilder, CompanyNode, PeerEdge};
use ruvector_data_framework::{DataRecord, DataSource, FrameworkError, Relationship, Result};
/// EDGAR-specific error types
#[derive(Error, Debug)]
pub enum EdgarError {
/// API request failed
#[error("API error: {0}")]
Api(String),
/// Invalid CIK
#[error("Invalid CIK: {0}")]
InvalidCik(String),
/// XBRL parsing failed
#[error("XBRL parse error: {0}")]
XbrlParse(String),
/// Filing not found
#[error("Filing not found: {0}")]
FilingNotFound(String),
/// Network error
#[error("Network error: {0}")]
Network(#[from] reqwest::Error),
/// Data format error
#[error("Data format error: {0}")]
DataFormat(String),
}
impl From<EdgarError> for FrameworkError {
fn from(e: EdgarError) -> Self {
FrameworkError::Ingestion(e.to_string())
}
}
/// Configuration for EDGAR data source
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct EdgarConfig {
/// User agent (required by SEC)
pub user_agent: String,
/// Company name for user agent
pub company_name: String,
/// Contact email (required by SEC)
pub contact_email: String,
/// Rate limit (requests per second)
pub rate_limit: u32,
/// Include historical data
pub include_historical: bool,
/// Filing types to fetch
pub filing_types: Vec<FilingType>,
}
impl Default for EdgarConfig {
fn default() -> Self {
Self {
user_agent: "RuVector/0.1.0".to_string(),
company_name: "Research Project".to_string(),
contact_email: "contact@example.com".to_string(),
rate_limit: 10, // SEC allows 10 requests/second
include_historical: true,
filing_types: vec![FilingType::TenK, FilingType::TenQ],
}
}
}
/// A company entity
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Company {
/// CIK (Central Index Key)
pub cik: String,
/// Company name
pub name: String,
/// Ticker symbol
pub ticker: Option<String>,
/// SIC code (industry)
pub sic_code: Option<String>,
/// SIC description
pub sic_description: Option<String>,
/// State of incorporation
pub state: Option<String>,
/// Fiscal year end
pub fiscal_year_end: Option<String>,
/// Latest filing date
pub latest_filing: Option<NaiveDate>,
}
/// A financial metric
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FinancialMetric {
/// Company CIK
pub cik: String,
/// Filing accession number
pub accession: String,
/// Report date
pub report_date: NaiveDate,
/// Metric name (XBRL tag)
pub metric_name: String,
/// Value
pub value: f64,
/// Unit
pub unit: String,
/// Is audited
pub audited: bool,
/// Context (annual, quarterly, etc.)
pub context: String,
}
/// Financial ratio
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum FinancialRatio {
/// Current ratio (current assets / current liabilities)
CurrentRatio,
/// Quick ratio ((current assets - inventory) / current liabilities)
QuickRatio,
/// Debt to equity
DebtToEquity,
/// Return on equity
ReturnOnEquity,
/// Return on assets
ReturnOnAssets,
/// Gross margin
GrossMargin,
/// Operating margin
OperatingMargin,
/// Net margin
NetMargin,
/// Asset turnover
AssetTurnover,
/// Inventory turnover
InventoryTurnover,
/// Price to earnings
PriceToEarnings,
/// Price to book
PriceToBook,
}
impl FinancialRatio {
/// Compute ratio from financial data
pub fn compute(&self, data: &HashMap<String, f64>) -> Option<f64> {
match self {
FinancialRatio::CurrentRatio => {
let current_assets = data.get("Assets Current")?;
let current_liabilities = data.get("Liabilities Current")?;
if *current_liabilities != 0.0 {
Some(current_assets / current_liabilities)
} else {
None
}
}
FinancialRatio::DebtToEquity => {
let total_debt = data.get("Debt")?;
let equity = data.get("Stockholders Equity")?;
if *equity != 0.0 {
Some(total_debt / equity)
} else {
None
}
}
FinancialRatio::NetMargin => {
let net_income = data.get("Net Income")?;
let revenue = data.get("Revenue")?;
if *revenue != 0.0 {
Some(net_income / revenue)
} else {
None
}
}
FinancialRatio::ReturnOnEquity => {
let net_income = data.get("Net Income")?;
let equity = data.get("Stockholders Equity")?;
if *equity != 0.0 {
Some(net_income / equity)
} else {
None
}
}
FinancialRatio::ReturnOnAssets => {
let net_income = data.get("Net Income")?;
let assets = data.get("Assets")?;
if *assets != 0.0 {
Some(net_income / assets)
} else {
None
}
}
_ => None, // Add more implementations as needed
}
}
}
/// Sector classification
#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq, Hash)]
pub enum Sector {
/// Technology
Technology,
/// Healthcare
Healthcare,
/// Financial services
Financials,
/// Consumer discretionary
ConsumerDiscretionary,
/// Consumer staples
ConsumerStaples,
/// Energy
Energy,
/// Materials
Materials,
/// Industrials
Industrials,
/// Utilities
Utilities,
/// Real estate
RealEstate,
/// Communication services
CommunicationServices,
/// Other/Unknown
Other,
}
impl Sector {
/// Get sector from SIC code
pub fn from_sic(sic: &str) -> Self {
match sic.chars().next() {
Some('7') => Sector::Technology,
Some('8') => Sector::Healthcare,
Some('6') => Sector::Financials,
Some('5') => Sector::ConsumerDiscretionary,
Some('2') => Sector::ConsumerStaples,
Some('1') => Sector::Energy,
Some('3') => Sector::Materials,
Some('4') => Sector::Industrials,
_ => Sector::Other,
}
}
}
/// EDGAR data source for the framework
pub struct EdgarSource {
client: EdgarClient,
config: EdgarConfig,
ciks: Vec<String>,
}
impl EdgarSource {
/// Create a new EDGAR data source
pub fn new(config: EdgarConfig) -> Self {
let client = EdgarClient::new(
&config.user_agent,
&config.company_name,
&config.contact_email,
);
Self {
client,
config,
ciks: Vec::new(),
}
}
/// Add CIKs to fetch
pub fn with_ciks(mut self, ciks: Vec<String>) -> Self {
self.ciks = ciks;
self
}
/// Add companies by ticker
pub async fn with_tickers(mut self, tickers: &[&str]) -> Result<Self> {
for ticker in tickers {
if let Ok(cik) = self.client.ticker_to_cik(ticker).await {
self.ciks.push(cik);
}
}
Ok(self)
}
/// Add all companies in a sector
pub async fn with_sector(mut self, sector: Sector) -> Result<Self> {
let companies = self.client.get_companies_by_sector(&sector).await?;
self.ciks.extend(companies.into_iter().map(|c| c.cik));
Ok(self)
}
}
#[async_trait]
impl DataSource for EdgarSource {
fn source_id(&self) -> &str {
"edgar"
}
async fn fetch_batch(
&self,
cursor: Option<String>,
batch_size: usize,
) -> Result<(Vec<DataRecord>, Option<String>)> {
let start_idx: usize = cursor.as_ref().and_then(|c| c.parse().ok()).unwrap_or(0);
let end_idx = (start_idx + batch_size).min(self.ciks.len());
let mut records = Vec::new();
for cik in &self.ciks[start_idx..end_idx] {
// Fetch filings for this CIK
match self.client.get_filings(cik, &self.config.filing_types).await {
Ok(filings) => {
for filing in filings {
records.push(filing_to_record(filing));
}
}
Err(e) => {
tracing::warn!("Failed to fetch filings for CIK {}: {}", cik, e);
}
}
// Rate limiting
if self.config.rate_limit > 0 {
let delay = 1000 / self.config.rate_limit as u64;
tokio::time::sleep(tokio::time::Duration::from_millis(delay)).await;
}
}
let next_cursor = if end_idx < self.ciks.len() {
Some(end_idx.to_string())
} else {
None
};
Ok((records, next_cursor))
}
async fn total_count(&self) -> Result<Option<u64>> {
Ok(Some(self.ciks.len() as u64))
}
async fn health_check(&self) -> Result<bool> {
self.client.health_check().await.map_err(|e| e.into())
}
}
/// Convert a filing to a data record
fn filing_to_record(filing: Filing) -> DataRecord {
let mut relationships = Vec::new();
// Company relationship
relationships.push(Relationship {
target_id: filing.cik.clone(),
rel_type: "filed_by".to_string(),
weight: 1.0,
properties: HashMap::new(),
});
DataRecord {
id: filing.accession_number.clone(),
source: "edgar".to_string(),
record_type: format!("{:?}", filing.filing_type).to_lowercase(),
timestamp: filing.filed_date.and_hms_opt(0, 0, 0)
.map(|dt| DateTime::<Utc>::from_naive_utc_and_offset(dt, Utc))
.unwrap_or_else(Utc::now),
data: serde_json::to_value(&filing).unwrap_or_default(),
embedding: None,
relationships,
}
}
/// Fundamental vs Narrative analyzer
///
/// Detects divergence between quantitative financial data
/// and qualitative narrative in filings.
pub struct FundamentalNarrativeAnalyzer {
/// Configuration
config: AnalyzerConfig,
}
/// Analyzer configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AnalyzerConfig {
/// Weight for fundamental metrics
pub fundamental_weight: f64,
/// Weight for narrative sentiment
pub narrative_weight: f64,
/// Minimum divergence to flag
pub divergence_threshold: f64,
/// Lookback periods
pub lookback_periods: usize,
}
impl Default for AnalyzerConfig {
fn default() -> Self {
Self {
fundamental_weight: 0.6,
narrative_weight: 0.4,
divergence_threshold: 0.3,
lookback_periods: 4,
}
}
}
impl FundamentalNarrativeAnalyzer {
/// Create a new analyzer
pub fn new(config: AnalyzerConfig) -> Self {
Self { config }
}
/// Analyze a company for fundamental vs narrative divergence
pub fn analyze(&self, company: &Company, filings: &[Filing]) -> Option<DivergenceResult> {
if filings.len() < 2 {
return None;
}
// Extract fundamental changes
let fundamental_trend = self.compute_fundamental_trend(filings);
// Extract narrative sentiment changes
let narrative_trend = self.compute_narrative_trend(filings);
// Detect divergence
let divergence = (fundamental_trend - narrative_trend).abs();
if divergence > self.config.divergence_threshold {
Some(DivergenceResult {
company_cik: company.cik.clone(),
company_name: company.name.clone(),
fundamental_trend,
narrative_trend,
divergence_score: divergence,
interpretation: self.interpret_divergence(fundamental_trend, narrative_trend),
})
} else {
None
}
}
/// Compute fundamental trend
fn compute_fundamental_trend(&self, filings: &[Filing]) -> f64 {
// Simplified: would compute from actual XBRL data
// Positive = improving financials, negative = declining
0.0
}
/// Compute narrative sentiment trend
fn compute_narrative_trend(&self, filings: &[Filing]) -> f64 {
// Simplified: would analyze text sentiment
// Positive = optimistic narrative, negative = pessimistic
0.0
}
/// Interpret the divergence
fn interpret_divergence(&self, fundamental: f64, narrative: f64) -> String {
if fundamental > 0.0 && narrative < 0.0 {
"Fundamentals improving but narrative pessimistic - potential undervaluation".to_string()
} else if fundamental < 0.0 && narrative > 0.0 {
"Fundamentals declining but narrative optimistic - potential risk".to_string()
} else if fundamental > narrative {
"Narrative lagging behind fundamental improvement".to_string()
} else {
"Narrative ahead of fundamental reality".to_string()
}
}
}
/// Result of divergence analysis
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct DivergenceResult {
/// Company CIK
pub company_cik: String,
/// Company name
pub company_name: String,
/// Fundamental trend (-1 to 1)
pub fundamental_trend: f64,
/// Narrative trend (-1 to 1)
pub narrative_trend: f64,
/// Divergence score (0 to 2)
pub divergence_score: f64,
/// Human-readable interpretation
pub interpretation: String,
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_sector_from_sic() {
assert_eq!(Sector::from_sic("7370"), Sector::Technology);
assert_eq!(Sector::from_sic("6000"), Sector::Financials);
}
#[test]
fn test_default_config() {
let config = EdgarConfig::default();
assert_eq!(config.rate_limit, 10);
}
#[test]
fn test_financial_ratio_compute() {
let mut data = HashMap::new();
data.insert("Assets Current".to_string(), 100.0);
data.insert("Liabilities Current".to_string(), 50.0);
let ratio = FinancialRatio::CurrentRatio.compute(&data);
assert!(ratio.is_some());
assert!((ratio.unwrap() - 2.0).abs() < 0.001);
}
}

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//! Peer network construction for financial coherence analysis
use std::collections::HashMap;
use chrono::{DateTime, Utc};
use serde::{Deserialize, Serialize};
use crate::{Company, Sector};
/// A company node in the peer network
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompanyNode {
/// Company CIK
pub cik: String,
/// Company name
pub name: String,
/// Ticker symbol
pub ticker: Option<String>,
/// Sector
pub sector: Sector,
/// Market cap (if known)
pub market_cap: Option<f64>,
/// Number of peer connections
pub peer_count: usize,
/// Average peer similarity
pub avg_peer_similarity: f64,
}
/// An edge between peer companies
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PeerEdge {
/// Source company CIK
pub source: String,
/// Target company CIK
pub target: String,
/// Similarity score (0-1)
pub similarity: f64,
/// Relationship type
pub relationship_type: PeerRelationType,
/// Edge weight for min-cut
pub weight: f64,
/// Evidence for relationship
pub evidence: Vec<String>,
}
/// Type of peer relationship
#[derive(Debug, Clone, Copy, Serialize, Deserialize, PartialEq, Eq)]
pub enum PeerRelationType {
/// Same sector/industry
SameSector,
/// Shared institutional investors
SharedInvestors,
/// Similar size (market cap)
SimilarSize,
/// Supply chain relationship
SupplyChain,
/// Competitor
Competitor,
/// Multiple relationship types
Multiple,
}
/// Peer network graph
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PeerNetwork {
/// Network identifier
pub id: String,
/// Nodes (companies)
pub nodes: HashMap<String, CompanyNode>,
/// Edges (peer relationships)
pub edges: Vec<PeerEdge>,
/// Creation time
pub created_at: DateTime<Utc>,
/// Network statistics
pub stats: NetworkStats,
}
/// Network statistics
#[derive(Debug, Clone, Default, Serialize, Deserialize)]
pub struct NetworkStats {
/// Number of nodes
pub node_count: usize,
/// Number of edges
pub edge_count: usize,
/// Average similarity
pub avg_similarity: f64,
/// Network density
pub density: f64,
/// Average degree
pub avg_degree: f64,
/// Number of connected components
pub num_components: usize,
/// Computed min-cut value
pub min_cut_value: Option<f64>,
}
impl PeerNetwork {
/// Create an empty network
pub fn new(id: &str) -> Self {
Self {
id: id.to_string(),
nodes: HashMap::new(),
edges: Vec::new(),
created_at: Utc::now(),
stats: NetworkStats::default(),
}
}
/// Add a company node
pub fn add_node(&mut self, node: CompanyNode) {
self.nodes.insert(node.cik.clone(), node);
self.update_stats();
}
/// Add a peer edge
pub fn add_edge(&mut self, edge: PeerEdge) {
self.edges.push(edge);
self.update_stats();
}
/// Get a node by CIK
pub fn get_node(&self, cik: &str) -> Option<&CompanyNode> {
self.nodes.get(cik)
}
/// Get peer CIKs for a company
pub fn get_peers(&self, cik: &str) -> Vec<&str> {
self.edges
.iter()
.filter_map(|e| {
if e.source == cik {
Some(e.target.as_str())
} else if e.target == cik {
Some(e.source.as_str())
} else {
None
}
})
.collect()
}
/// Get edges for a company
pub fn get_edges_for_company(&self, cik: &str) -> Vec<&PeerEdge> {
self.edges
.iter()
.filter(|e| e.source == cik || e.target == cik)
.collect()
}
/// Update statistics
fn update_stats(&mut self) {
self.stats.node_count = self.nodes.len();
self.stats.edge_count = self.edges.len();
if !self.edges.is_empty() {
self.stats.avg_similarity = self.edges.iter().map(|e| e.similarity).sum::<f64>()
/ self.edges.len() as f64;
}
let max_edges = if self.nodes.len() > 1 {
self.nodes.len() * (self.nodes.len() - 1) / 2
} else {
1
};
self.stats.density = self.edges.len() as f64 / max_edges as f64;
if !self.nodes.is_empty() {
self.stats.avg_degree = (2 * self.edges.len()) as f64 / self.nodes.len() as f64;
}
}
/// Convert to format for RuVector min-cut
pub fn to_mincut_edges(&self) -> Vec<(u64, u64, f64)> {
let mut node_ids: HashMap<&str, u64> = HashMap::new();
let mut next_id = 0u64;
for cik in self.nodes.keys() {
node_ids.insert(cik.as_str(), next_id);
next_id += 1;
}
self.edges
.iter()
.filter_map(|e| {
let src_id = node_ids.get(e.source.as_str())?;
let tgt_id = node_ids.get(e.target.as_str())?;
Some((*src_id, *tgt_id, e.weight))
})
.collect()
}
/// Get node ID mapping
pub fn node_id_mapping(&self) -> HashMap<u64, String> {
let mut mapping = HashMap::new();
for (i, cik) in self.nodes.keys().enumerate() {
mapping.insert(i as u64, cik.clone());
}
mapping
}
}
/// Builder for peer networks
pub struct PeerNetworkBuilder {
id: String,
companies: Vec<Company>,
min_similarity: f64,
max_peers: usize,
relationship_types: Vec<PeerRelationType>,
}
impl PeerNetworkBuilder {
/// Create a new builder
pub fn new() -> Self {
Self {
id: format!("network_{}", Utc::now().timestamp()),
companies: Vec::new(),
min_similarity: 0.3,
max_peers: 20,
relationship_types: vec![
PeerRelationType::SameSector,
PeerRelationType::SimilarSize,
],
}
}
/// Set network ID
pub fn with_id(mut self, id: &str) -> Self {
self.id = id.to_string();
self
}
/// Add companies
pub fn add_companies(mut self, companies: Vec<Company>) -> Self {
self.companies.extend(companies);
self
}
/// Set minimum similarity threshold
pub fn min_similarity(mut self, min: f64) -> Self {
self.min_similarity = min;
self
}
/// Set maximum peers per company
pub fn max_peers(mut self, max: usize) -> Self {
self.max_peers = max;
self
}
/// Set relationship types to consider
pub fn relationship_types(mut self, types: Vec<PeerRelationType>) -> Self {
self.relationship_types = types;
self
}
/// Build the network
pub fn build(self) -> PeerNetwork {
let mut network = PeerNetwork::new(&self.id);
// Add nodes
for company in &self.companies {
let sector = company.sic_code
.as_ref()
.map(|s| Sector::from_sic(s))
.unwrap_or(Sector::Other);
let node = CompanyNode {
cik: company.cik.clone(),
name: company.name.clone(),
ticker: company.ticker.clone(),
sector,
market_cap: None,
peer_count: 0,
avg_peer_similarity: 0.0,
};
network.add_node(node);
}
// Add edges based on relationships
for i in 0..self.companies.len() {
for j in (i + 1)..self.companies.len() {
let company_i = &self.companies[i];
let company_j = &self.companies[j];
let (similarity, rel_type) = self.compute_similarity(company_i, company_j);
if similarity >= self.min_similarity {
let edge = PeerEdge {
source: company_i.cik.clone(),
target: company_j.cik.clone(),
similarity,
relationship_type: rel_type,
weight: similarity,
evidence: self.collect_evidence(company_i, company_j),
};
network.add_edge(edge);
}
}
}
// Update node statistics
for (cik, node) in network.nodes.iter_mut() {
let edges = network.edges
.iter()
.filter(|e| e.source == *cik || e.target == *cik)
.collect::<Vec<_>>();
node.peer_count = edges.len();
if !edges.is_empty() {
node.avg_peer_similarity = edges.iter().map(|e| e.similarity).sum::<f64>()
/ edges.len() as f64;
}
}
network
}
/// Compute similarity between two companies
fn compute_similarity(&self, a: &Company, b: &Company) -> (f64, PeerRelationType) {
let mut total_similarity = 0.0;
let mut relationship_count = 0;
let mut rel_type = PeerRelationType::SameSector;
// Sector similarity
if self.relationship_types.contains(&PeerRelationType::SameSector) {
let sector_a = a.sic_code.as_ref().map(|s| Sector::from_sic(s));
let sector_b = b.sic_code.as_ref().map(|s| Sector::from_sic(s));
if sector_a.is_some() && sector_a == sector_b {
total_similarity += 0.5;
relationship_count += 1;
} else if a.sic_code.is_some() && b.sic_code.is_some() {
// Same SIC division (first digit)
let sic_a = a.sic_code.as_ref().unwrap();
let sic_b = b.sic_code.as_ref().unwrap();
if !sic_a.is_empty() && !sic_b.is_empty() &&
sic_a.chars().next() == sic_b.chars().next() {
total_similarity += 0.3;
relationship_count += 1;
}
}
}
// Same state
if a.state.is_some() && a.state == b.state {
total_similarity += 0.2;
relationship_count += 1;
}
let similarity = if relationship_count > 0 {
total_similarity / relationship_count as f64
} else {
0.0
};
if relationship_count > 1 {
rel_type = PeerRelationType::Multiple;
}
(similarity, rel_type)
}
/// Collect evidence for relationship
fn collect_evidence(&self, a: &Company, b: &Company) -> Vec<String> {
let mut evidence = Vec::new();
let sector_a = a.sic_code.as_ref().map(|s| Sector::from_sic(s));
let sector_b = b.sic_code.as_ref().map(|s| Sector::from_sic(s));
if sector_a.is_some() && sector_a == sector_b {
evidence.push(format!("Same sector: {:?}", sector_a.unwrap()));
}
if a.state.is_some() && a.state == b.state {
evidence.push(format!("Same state: {}", a.state.as_ref().unwrap()));
}
evidence
}
}
impl Default for PeerNetworkBuilder {
fn default() -> Self {
Self::new()
}
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_empty_network() {
let network = PeerNetwork::new("test");
assert_eq!(network.stats.node_count, 0);
assert_eq!(network.stats.edge_count, 0);
}
#[test]
fn test_builder() {
let builder = PeerNetworkBuilder::new()
.min_similarity(0.5)
.max_peers(10);
let network = builder.build();
assert!(network.nodes.is_empty());
}
#[test]
fn test_get_peers() {
let mut network = PeerNetwork::new("test");
network.add_node(CompanyNode {
cik: "A".to_string(),
name: "Company A".to_string(),
ticker: None,
sector: Sector::Technology,
market_cap: None,
peer_count: 0,
avg_peer_similarity: 0.0,
});
network.add_node(CompanyNode {
cik: "B".to_string(),
name: "Company B".to_string(),
ticker: None,
sector: Sector::Technology,
market_cap: None,
peer_count: 0,
avg_peer_similarity: 0.0,
});
network.add_edge(PeerEdge {
source: "A".to_string(),
target: "B".to_string(),
similarity: 0.8,
relationship_type: PeerRelationType::SameSector,
weight: 0.8,
evidence: vec![],
});
let peers = network.get_peers("A");
assert_eq!(peers.len(), 1);
assert_eq!(peers[0], "B");
}
}

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examples/data/edgar/src/xbrl.rs Normal file
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//! XBRL parsing for financial statement extraction
use std::collections::HashMap;
use chrono::NaiveDate;
use serde::{Deserialize, Serialize};
use crate::EdgarError;
/// XBRL parser
pub struct XbrlParser {
/// Configuration
config: ParserConfig,
}
/// Parser configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ParserConfig {
/// Include all numeric facts
pub include_all_facts: bool,
/// Fact name filters (regex patterns)
pub fact_filters: Vec<String>,
/// Merge duplicate contexts
pub merge_contexts: bool,
}
impl Default for ParserConfig {
fn default() -> Self {
Self {
include_all_facts: false,
fact_filters: vec![
"Revenue".to_string(),
"NetIncome".to_string(),
"Assets".to_string(),
"Liabilities".to_string(),
"StockholdersEquity".to_string(),
],
merge_contexts: true,
}
}
}
/// Parsed financial statement
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct FinancialStatement {
/// Company CIK
pub cik: String,
/// Filing accession number
pub accession: String,
/// Report type (10-K, 10-Q)
pub report_type: String,
/// Period end date
pub period_end: NaiveDate,
/// Is annual (vs quarterly)
pub is_annual: bool,
/// Balance sheet items
pub balance_sheet: HashMap<String, f64>,
/// Income statement items
pub income_statement: HashMap<String, f64>,
/// Cash flow items
pub cash_flow: HashMap<String, f64>,
/// All facts
pub all_facts: Vec<XbrlFact>,
/// Contexts
pub contexts: Vec<XbrlContext>,
}
/// An XBRL fact
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct XbrlFact {
/// Concept name
pub name: String,
/// Value
pub value: f64,
/// Unit
pub unit: String,
/// Context reference
pub context_ref: String,
/// Decimals precision
pub decimals: Option<i32>,
/// Is negated
pub is_negated: bool,
}
/// An XBRL context
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct XbrlContext {
/// Context ID
pub id: String,
/// Start date
pub start_date: Option<NaiveDate>,
/// End date / instant
pub end_date: NaiveDate,
/// Is instant (vs duration)
pub is_instant: bool,
/// Segment/scenario dimensions
pub dimensions: HashMap<String, String>,
}
impl XbrlParser {
/// Create a new parser
pub fn new(config: ParserConfig) -> Self {
Self { config }
}
/// Parse XBRL document from string
pub fn parse(&self, content: &str, cik: &str, accession: &str) -> Result<FinancialStatement, EdgarError> {
// This is a simplified parser
// Real implementation would use quick-xml or similar
let contexts = self.parse_contexts(content)?;
let facts = self.parse_facts(content)?;
// Determine period end and type
let (period_end, is_annual) = self.determine_period(&contexts)?;
// Categorize facts
let mut balance_sheet = HashMap::new();
let mut income_statement = HashMap::new();
let mut cash_flow = HashMap::new();
for fact in &facts {
if self.is_balance_sheet_item(&fact.name) {
balance_sheet.insert(fact.name.clone(), fact.value);
} else if self.is_income_statement_item(&fact.name) {
income_statement.insert(fact.name.clone(), fact.value);
} else if self.is_cash_flow_item(&fact.name) {
cash_flow.insert(fact.name.clone(), fact.value);
}
}
Ok(FinancialStatement {
cik: cik.to_string(),
accession: accession.to_string(),
report_type: if is_annual { "10-K".to_string() } else { "10-Q".to_string() },
period_end,
is_annual,
balance_sheet,
income_statement,
cash_flow,
all_facts: facts,
contexts,
})
}
/// Parse contexts from XBRL
fn parse_contexts(&self, content: &str) -> Result<Vec<XbrlContext>, EdgarError> {
// Simplified - would use proper XML parsing
let mut contexts = Vec::new();
// Add placeholder context
contexts.push(XbrlContext {
id: "FY2023".to_string(),
start_date: Some(NaiveDate::from_ymd_opt(2023, 1, 1).unwrap()),
end_date: NaiveDate::from_ymd_opt(2023, 12, 31).unwrap(),
is_instant: false,
dimensions: HashMap::new(),
});
Ok(contexts)
}
/// Parse facts from XBRL
fn parse_facts(&self, content: &str) -> Result<Vec<XbrlFact>, EdgarError> {
// Simplified - would use proper XML parsing
let mut facts = Vec::new();
// Extract numeric values using simple pattern matching
// Real implementation would parse XML properly
Ok(facts)
}
/// Determine period end and whether annual
fn determine_period(&self, contexts: &[XbrlContext]) -> Result<(NaiveDate, bool), EdgarError> {
// Find the main reporting context
for ctx in contexts {
if !ctx.is_instant {
let duration_days = ctx.start_date
.map(|s| (ctx.end_date - s).num_days())
.unwrap_or(0);
let is_annual = duration_days > 300;
return Ok((ctx.end_date, is_annual));
}
}
// Default to latest instant context
if let Some(ctx) = contexts.last() {
return Ok((ctx.end_date, true));
}
Err(EdgarError::XbrlParse("No valid context found".to_string()))
}
/// Check if concept is balance sheet item
fn is_balance_sheet_item(&self, name: &str) -> bool {
let balance_sheet_patterns = [
"Assets",
"Liabilities",
"Equity",
"Cash",
"Inventory",
"Receivable",
"Payable",
"Debt",
"Property",
"Goodwill",
];
balance_sheet_patterns.iter().any(|p| name.contains(p))
}
/// Check if concept is income statement item
fn is_income_statement_item(&self, name: &str) -> bool {
let income_patterns = [
"Revenue",
"Sales",
"Cost",
"Expense",
"Income",
"Profit",
"Loss",
"Earnings",
"EBITDA",
"Margin",
];
income_patterns.iter().any(|p| name.contains(p))
}
/// Check if concept is cash flow item
fn is_cash_flow_item(&self, name: &str) -> bool {
let cash_flow_patterns = [
"CashFlow",
"Operating",
"Investing",
"Financing",
"Depreciation",
"Amortization",
"CapitalExpenditure",
];
cash_flow_patterns.iter().any(|p| name.contains(p))
}
}
/// Convert financial statement to vector embedding
pub fn statement_to_embedding(statement: &FinancialStatement) -> Vec<f32> {
let mut embedding = Vec::with_capacity(64);
// Balance sheet ratios
let total_assets = statement.balance_sheet.get("Assets").copied().unwrap_or(1.0);
let total_liabilities = statement.balance_sheet.get("Liabilities").copied().unwrap_or(0.0);
let equity = statement.balance_sheet.get("StockholdersEquity").copied().unwrap_or(1.0);
let cash = statement.balance_sheet.get("Cash").copied().unwrap_or(0.0);
embedding.push((total_liabilities / total_assets) as f32); // Debt ratio
embedding.push((cash / total_assets) as f32); // Cash ratio
embedding.push((equity / total_assets) as f32); // Equity ratio
// Income statement ratios
let revenue = statement.income_statement.get("Revenue").copied().unwrap_or(1.0);
let net_income = statement.income_statement.get("NetIncome").copied().unwrap_or(0.0);
let operating_income = statement.income_statement.get("OperatingIncome").copied().unwrap_or(0.0);
embedding.push((net_income / revenue) as f32); // Net margin
embedding.push((operating_income / revenue) as f32); // Operating margin
embedding.push((net_income / equity) as f32); // ROE
embedding.push((net_income / total_assets) as f32); // ROA
// Pad to fixed size
while embedding.len() < 64 {
embedding.push(0.0);
}
// Normalize
let norm: f32 = embedding.iter().map(|x| x * x).sum::<f32>().sqrt();
if norm > 0.0 {
for x in &mut embedding {
*x /= norm;
}
}
embedding
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_parser_creation() {
let config = ParserConfig::default();
let parser = XbrlParser::new(config);
assert!(!parser.config.include_all_facts);
}
#[test]
fn test_balance_sheet_detection() {
let config = ParserConfig::default();
let parser = XbrlParser::new(config);
assert!(parser.is_balance_sheet_item("TotalAssets"));
assert!(parser.is_balance_sheet_item("CashAndCashEquivalents"));
assert!(!parser.is_balance_sheet_item("Revenue"));
}
#[test]
fn test_income_statement_detection() {
let config = ParserConfig::default();
let parser = XbrlParser::new(config);
assert!(parser.is_income_statement_item("Revenue"));
assert!(parser.is_income_statement_item("NetIncome"));
assert!(!parser.is_income_statement_item("TotalAssets"));
}
}