Decompose the real reasons behind the R$51B fixed income ETF boom
Brazilian investors lack clear understanding of why fixed income ETFs grew to R$51B beyond just high Selic rates, leading to suboptimal portfolio allocation
FactorFixa uses proprietary models to break down the R$51B growth into regulatory, product, behavioral, and liquidity factors beyond just Selic rates. Investors get interactive visualizations and personalized portfolio recommendations that reveal hidden drivers, leading to smarter allocation decisions.
Brazilian retail and institutional investors allocating to ETFs
First platform with a dedicated Brazilian Fixed Income Factor Database that combines regulatory timeline mapping with performance attribution models specifically trained on post-2017 ETF data.
professional and supportive
Breaks down ETF growth into 8+ distinct factors with attribution percentages
Interactive charts showing AUM evolution and factor contribution over time
Upload portfolio or select ETFs to receive custom factor exposure report
In-depth explainers with Brazilian regulatory context for each factor
Personalized PDF reports delivered via email with updated factor shifts
Side-by-side factor scoring across multiple fixed income ETFs
Model how changes in Selic or regulation would affect your allocation
Export raw factor data or connect via API
Custom regression models trained on user portfolios
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| text | No | |
| name | text | Yes |
| onboarding_data | jsonb | Yes |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| ticker | text | No |
| name | text | No |
| aum_history | jsonb | Yes |
| created_at | timestamp | No |
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| etf_tickers | text | No |
| factor_breakdown | jsonb | No |
| created_at | timestamp | No |
Relationships:
| Column | Type | Nullable |
|---|---|---|
| id | uuid | No |
| user_id | uuid | No |
| analysis_id | uuid | Yes |
| content_url | text | Yes |
| sent_at | timestamp | Yes |
Relationships:
/api/analysesRun new factor decomposition on selected ETFs/portfolio
/api/analyses/[id]Retrieve detailed factor breakdown and charts
/api/reportsFetch user's monthly reports
/api/etf/searchSearch Brazilian fixed income ETFs
/api/portfolio/uploadParse and analyze uploaded portfolio CSV
Limited to 3 popular ETFs
None
Custom volume
| Month | Users | Conversion | MRR | ARR |
|---|---|---|---|---|
| Month 1 | 180 | 9% | $405 | $4,860 |
| Month 6 | 1,450 | 19% | $6,887 | $82,650 |
Stop guessing why fixed income ETFs grew far beyond high Selic rates. Get precise factor attribution and smarter allocation recommendations.
1. Publish a viral LinkedIn thread breaking down the R$51B growth with a free FactorFixa trial link targeting 500+ Brazilian investors. 2. Offer free Institutional tier to 5 boutique asset managers in exchange for testimonials and case studies. 3. Run targeted Google Ads on high-intent Portuguese keywords and personally onboard first 30 Pro signups via Zoom.
Comprehensive Brazilian market data
No factor decomposition or educational 'why' analysis
Deep causal analysis instead of just raw data
Integrated with trading
Biased toward their own products, lacks neutral education
Independent, education-first factor models
Proprietary database of Brazilian regulatory events mapped to ETF AUM changes that compounds with every new analysis run
As Selic rates decline from 2023 peaks, Brazilian investors are actively questioning the 'stickiness' of the R$51B fixed income ETF inflow, creating perfect timing for explanatory tools.
Data sources for ETF AUM become restricted
Build multiple data ingestion paths including public CVM filings and partnerships
Factor models produce inaccurate attributions
Start with transparent rule-based models before adding ML, publish methodology
Slow initial user acquisition in Portuguese market
Heavy focus on LinkedIn and local finance influencers from day one
Success: At least 70% confirm they don't understand drivers beyond Selic
Success: 15% conversion from waitlist to paid in first 30 days
Success: Achieve 450 total users and $800 MRR in first 30 days
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