Understand the infrastructure powering research at AlgoGators, then learn to write a testable, falsifiable hypothesis for Section 1 of your IP.
Part one: the AlgoGators data pipeline — what tools ingest data, where it lives, how it flows to strategies, and what the QD approval gate (Section 2b) is checking for. Part two: how to write a strong, falsifiable hypothesis with an economic mechanism — which is Section 1 of your IP.
Understanding this flow is critical. You will describe your data requirements in Section 2a. The QD team will use those requirements to build the pipeline. But you need to understand the constraints at each step.
What it is: A market data provider. Tick data, OHLCV (open, high, low, close, volume), order book snapshots across equities, futures, options, FX.
Coverage: US equities, US futures (CME, CBOT, COMEX), cryptocurrencies, FX futures. International coverage limited.
Cost: Databento charges per data series per month. Novel data sources require approval before you spend time on them.
Frequency: Tick-level to daily OHLCV. You request the granularity you need.
For QR: Know what you're asking for before Section 2b. If you want daily closing prices for the last 10 years, that's cheap and trivial. If you want 1-minute bars for 500 stocks for 5 years, that's expensive and requires approval.
What it is: PostgreSQL extension optimized for time-series data. Time-indexed tables, automatic partitioning, continuous aggregates (pre-computed rolling statistics).
How it works: Raw tick data from Databento is ingested into TimescaleDB. Millions of rows a day. Partitioned by time so queries on recent data are fast.
Continuous aggregates: You can define a continuous aggregate that automatically computes, say, daily OHLCV from tick data. No extra code required — TimescaleDB handles it.
For QR: You don't write SQL. You define your data requirements in Section 2a ("daily OHLCV for corn futures"), and QD writes the queries. But knowing the data lives in TimescaleDB helps you understand data availability and freshness.
What it is: Internal cleaning and feature engineering layer. Handles corporate actions (stock splits, dividends), outlier detection, missing value handling, and feature construction.
Corporate actions: When a stock splits 2-for-1, raw prices from two days don't compare. data-ngin adjusts for this. Uses point-in-time adjustments (historical prices adjusted forward, not backward, to avoid look-ahead bias).
Outliers: Exchange halts, data errors, gaps. data-ngin flags and handles these.
Features: When you describe your signal as "5-day rolling z-score," data-ngin is what computes it. You specify the formula in your IP; QD implements it in data-ngin.
For QR: In Section 2a, you describe the features you need with formulas. In Section 3a, you use those features in your signal. data-ngin makes them available.
This is a critical checkpoint. You cannot move forward without QD sign-off.
Why it exists: Too many QR analysts have written strategies on data that doesn't exist. They spent weeks developing on a dataset that was never subscribed to, or that costs $500k/month, or that isn't available at the frequency needed, or that has only 3 years of history (too short for backtesting).
What Section 2b must contain:
What happens: You submit your IP with Section 2 (both 2a and 2b). QT reviews strategy fit. QD reviews data feasibility and signs off in 2b. If 2b is not signed, the IP is rejected, full stop. This is not punitive — it's a forcing function to catch infeasible research early.
import pandas as pd
import psycopg2
conn = psycopg2.connect(
host="timescaledb.internal",
dbname="algogators",
user="qr_user",
password="***"
)
query = """
SELECT timestamp, open, high, low, close, volume
FROM corn_futures
WHERE timestamp BETWEEN '2020-01-01' AND '2024-01-01'
ORDER BY timestamp
"""
df = pd.read_sql(query, conn, parse_dates=['timestamp'], index_col='timestamp')
def rolling_zscore(series, window=20):
"""Z-score normalization using rolling window."""
mean = series.rolling(window).mean()
std = series.rolling(window).std()
return (series - mean) / std
# Normalized signal for entry/exit
df['signal'] = rolling_zscore(df['close'], window=60)
entry_threshold = 2.0 # Entry when signal < -2
df['signal_entry'] = df['signal'] < -entry_threshold
# data-ngin handles this automatically
# You specify the adjustment method in Section 2a:
# "Prices adjusted for splits/dividends using backward-adjusted method.
# Adjustment factors applied at ex-date."
# QD implements it; you use the adjusted prices in your signal.Raw data: NASA POWER API. Daily solar radiation (ALLSKY_SFC_SW_DWN), temperature (T2M), precipitation (PRECTOTCORR) at lat/lon grid.
Geographic mapping: Corn Belt counties (IL, IA, MN, MO, etc.). Map each county to grid point lat/lon centroids.
Transformation: Compute growing-degree-day (GDD) deviation from 20-year seasonal average. Daily rolling sums, seasonal adjustment.
Feature: GDD_deviation_30d = 30-day rolling sum of (daily GDD - 20-year seasonal average for that calendar window).
Section 2b sign-off: "Data sourced from NASA POWER REST API (public, no licensing cost). Daily resolution, available back to 1981. Ingestible via Python requests → CSV → TimescaleDB. QD: Confirmed."
Why does this pattern exist? Not "I found it in the data." Why would market participants allow this inefficiency to persist?
Good mechanisms explain:
What signal predicts what return, in what direction, over what horizon?
Example: "When 30-day rolling soil moisture deviation is more than 1 standard deviation below the 20-year seasonal average, corn futures are expected to have negative returns over the next 5–10 trading days because low soil moisture predicts lower yields, which the market will reprice when USDA yield reports are released."
Specify:
What result would convince you the hypothesis is wrong? If you can't answer this, you don't have a hypothesis.
Example: "This hypothesis is falsified if (a) the Information Coefficient between soil moisture deviation and forward corn returns is less than 0.02 (no predictive power), or (b) the backtest Sharpe on a 2023 hold-out period is less than 0.5 (results don't hold out-of-sample)."
Good falsifiability conditions are:
Copy this template into Section 1 of your IP. Fill in each section with your hypothesis.
HYPOTHESIS TEMPLATE
Economic mechanism:
[Market participants systematically _____ because _____, which causes prices to _____ .]
Predicted relationship:
[When [signal] is [high/low/rising/falling], [asset] returns are expected to be [positive/negative] over [horizon], because [mechanism above].]
Falsifiability:
[This hypothesis would be rejected if [specific quantitative condition — e.g., Information Coefficient < 0.02, or Sharpe < 0.5 on hold-out period, or directional accuracy < 55% on out-of-sample data].]
Example: Post-earnings drift (momentum)
Economic mechanism: Investors systematically underreact to earnings news. The market reprices gradually over weeks or months, not immediately at announcement. Short-selling constraints and institutional limitations on volatility exposure prevent arbitrageurs from eliminating this drift immediately.
Predicted relationship: When earnings surprise is large and positive (actual EPS > consensus EPS by more than 1 standard deviation), the stock is expected to have positive abnormal returns for 3–6 months post-announcement.
Falsifiability: This hypothesis is rejected if the average post-announcement drift is not significantly different from zero (t-stat < 1.96) over a 3-month hold period, or if the drift reverses (negative returns) in the first week after announcement.
Example: Commodity roll yield (contango/backwardation)
Economic mechanism: Commodity futures contracts are rolled before expiry. When the term structure is in backwardation (near-month contract trading at a premium to far-month), rolling captures positive yield. This is not an arbitrage — it reflects the physical convenience value of holding inventory. Hedgers (producers and consumers) are willing to pay this premium because holding physical commodity has value. The premium is persistent because the underlying convenience value doesn't disappear.
Predicted relationship: When the front/second-month spread is in backwardation (front price > second-month price), a rolling long position in the front-month contract is expected to earn positive roll yield equal to the spread minus the cost of financing. Expected return is positive.
Falsifiability: This hypothesis is rejected if average roll yield is zero or negative over a 5-year period, or if roll yield does not persist after transaction costs.
Example: Satellite weather data predicting crop yields
Economic mechanism: NASA satellite data provides daily measurements of soil moisture and temperature at the field level. These measurements predict crop yields. The market prices crops based on USDA forecasts, which are survey-based and release once per month. There is an information gap: satellite data predicts yields before USDA reports them. The market does not instantaneously incorporate satellite data because (a) the data is not in standard market feeds, (b) processing it requires domain expertise, (c) few market participants have access or motivation to use it.
Predicted relationship: When growing-season soil moisture anomalies (satellite GDD deviation from 20-year seasonal average) are large and negative, corn prices are expected to decline in the 5–10 days preceding the next USDA yield report, capturing the market's repricing as the report data becomes known.
Falsifiability: This hypothesis is rejected if the Information Coefficient between satellite moisture deviation and forward corn returns is < 0.02, or if the strategy has negative Sharpe on a 2023 hold-out period.
IC measures the correlation between your signal and forward returns. It's the core metric for evaluating whether your hypothesis has predictive power before you build the full backtest.
\[ IC = \text{Corr}(\text{signal}_t,\ r_{t+h}) \]Where signal_t is your signal at time t, r_{t+h} is the return from t to t+h (your chosen horizon).
Interpretation:
IC Information Ratio (ICIR):
\[ ICIR = \frac{\overline{IC}}{\sigma_{IC}} \]Average IC divided by the standard deviation of IC. ICIR > 0.5 suggests a consistently predictive signal.