While playing this, a thought occurred: this, so far (20 hours in), is what I wanted out of genshin impact as far as gameplay and multiplayer goes. Especially multiplayer. And I know it's just early access, so it'll get even better.
I hope this game does well, because it's pretty solid and fun. Is there a date for 1.0 yet?
I am building a fully systematic, deterministic execution engine on my homelab, effectively automating a deep-value strategy based on a strict 10-year "Decade of Stability" criteria (Benjamin Graham's defensive parameters).
I recently finished engineering the data infrastructure: a bitemporal "Temporal Rollback Ledger" that unwinds SEC 10-K/A amendments on the fly to ensure my fundamental data is 100% Point-in-Time (PIT) and free of lookahead bias.
Now that the data is pristine, I am refining the mathematical construction of my primary alpha factors, specifically my custom Cash Return on Invested Capital (CROIC) gate. I'm running into the classic GAAP distortion problem regarding intangibles, and I'd love some insight from researchers working on systematic value factors.
Standard GAAP accounting treats R&D and SG&A (which often includes software development/customer acquisition) as operating expenses. In the modern market, this brutally distorts Book Value and Invested Capital, penalizing compounders that heavily reinvest in intangible assets.
To fix this, my engine intercepts the raw XBRL data and mathematically reconstructs the balance sheet before calculating the CROIC 5-year trailing trend.
1. Sector-Specific Amortization Rates Currently, I am applying a naive 5-year straight-line amortization rate for capitalized R&D across the board. Obviously, a dollar of R&D at a pharmaceutical company (10-year drug pipelines) has a vastly different decay rate than a dollar of R&D at a SaaS company (2-year software lifecycle). How are you guys systematically assigning amortization schedules for custom intangible factors across a broad universe without hard-coding rules for every single ticker?
2. The "Value Trap" & Sentiment Orthogonalization Systematic deep-value factors notoriously suffer in high-liquidity, momentum-driven regimes. To mitigate value traps, I built a local FinBERT NLP sidecar. It reads the unstructured "Risk Factors" from the 10-K and recent news. If the sentiment score is brutally negative, the engine treats it as a "toxicity" flag and vetos the mathematically sound trade.
For those combining fundamental value factors with alternative NLP data, do you strictly use NLP as a binary filter (Fail-Closed circuit breaker), or do you orthogonalize the sentiment factor against the value factor to dynamically scale portfolio weights?
3. Volatility vs. Fundamental Sizing Right now, if an asset clears the 10-year CROIC and valuation gates, I use a strict Kelly/Barbell sizing mechanism. However, this ignores the covariance between selected assets. When constructing a concentrated, fundamental value portfolio, do you transition to a standard mean-variance optimization (or Risk Parity) for final sizing, or do you find that historical price covariance dilutes the edge of a pure fundamental factor?
Any critiques on the math, or literature recommendations on systematic intangible capitalization (beyond standard Damodaran papers), would be highly appreciated.
I’m running a localized quantitative execution engine on my homelab, heavily focused on fundamental value metrics (Benjamin Graham's criteria).
My biggest hurdle was that standard financial APIs (and most datasets) suffer from massive SEC Lookahead Bias. If a company restated their earnings two years later, legacy datasets silently overwrite the historical row, ruining backtests. I solved this by building a Temporal Rollback Ledger in Postgres. My ingestion engine does a chronological walk, pulling the original 10-K XBRL data and mathematically unwinding any 10-K/A amendments filed after the simulation date.
My deterministic numerical data (pricing, fundamentals, ratios) is now perfectly bitemporal and lookahead-free.
However, I have a local Llama 3 / FinBERT sidecar acting as a qualitative "Risk Manager" (it reads text to detect off-balance-sheet risks or toxic PR to veto trades). To backtest this sidecar, I need historical, unstructured text datasets that are as strictly time-stamped as my numerical data.
I am hunting for three specific datasets:
1. Archival Earnings Call Transcripts (with exact timestamps) I need a massive dump of Q1-Q4 earnings call transcripts for the S&P 500 going back 10 years. Crucially, they need to be mapped to the exact date/time the call occurred, and ideally include speaker diarization (separating Management vs. Analyst Q&A) so I can prompt the LLM to analyze management evasion. Does a bulk archive of this exist outside of $20k/year Bloomberg terminals?
2. Point-In-Time Macroeconomic Indicators (Unrevised) Things like CPI, Non-Farm Payrolls, and GDP are notoriously revised months after the initial print. If I use FRED data, my backtest sees the revised numbers, not the initial panic-inducing print. Is there a repository of raw, unrevised macro releases mapped to the exact day they hit the wire?
3. Corporate Crisis / "Toxic" PR Archives I need a labeled dataset of major corporate PR disasters, product recalls, or C-suite scandals with the exact text of the breaking news articles. I need this to benchmark my FinBERT model's ability to act as a "Fail-Closed" circuit breaker.
I am happy to share/open-source my Python ingestion scripts and Postgres schema for the SEC Temporal Rollback engine if anyone needs to scrub lookahead bias from their own financial datasets. Any pointers on where to scrape or torrent these text archives would be highly appreciated.
Quick shoutout to this sub! Last week you guys completely roasted my anomaly filter and saved me from non-stationarity traps by shifting my logic to log-returns. The engine is finally surviving synthetic flash crashes!!!
I’m now moving down the pipeline to rebuild my fundamental data ingestion (Layer 1.5...ish), and I keep running into a massive normalization trap with API providers (I’m currently using EODHD, but I assume FMP/Polygon do this too).
To serve data at scale, the API tries to force, say, a regional bank and a cloud SaaS company into the exact same JSON schema. Keys get silently renamed overnight (e.g., TotalRevenue becomes operating_revenue), or line items like "Provision for Credit Losses" get rolled up into generic "Operating Expenses."
If my ingestion script just blindly parses the JSON payload and inserts it into my Postgres ledger, my engine calculates a mathematically perfect Piotroski F-Score based on complete hallucinations. I’ll have a script screaming that a tech stock is a "deep value trap" just because the API silently changed the researchDevelopment key to research_development and it defaulted to $0.
How are you guys locking this down?
I'm currently trying to build a strict perimeter shield using Pydantic AliasChoices to catch the variations and force a validation error before the data ever touches my database, but maintaining the aliases feels like an endless game of whack-a-mole.
Do you guys just maintain massive dictionary maps for every sector, or is there an institutional design pattern for standardizing raw fundamental JSON that I am completely missing?