flashalpha-historical

Python SDK for the FlashAlpha Historical API — point-in-time replay of every live analytics endpoint. Ask what GEX, gamma flip, VRP, narrative, max pain, or the full stock summary looked like at any minute back to 2018-04-16, in the same response shape as the live API.

Coverage: SPY 2018-04-16 → today, with daily extensions; more symbols added on demand.

pip install flashalpha-historical

Requires Python 3.10+. Same X-Api-Key you use for api.flashalpha.com. Alpha plan or higher on every endpoint.

Quickstart

from flashalpha_historical import FlashAlphaHistorical

hx = FlashAlphaHistorical("YOUR_API_KEY")

# One snapshot — what dealer positioning looked like during the COVID crash
snap = hx.exposure_summary("SPY", at="2020-03-16T15:30:00")
print(snap["regime"], snap["exposures"]["net_gex"])
# → 'negative_gamma' -2633970601

The at= parameter accepts strings ("2026-03-05T15:30:00" or "2026-03-05" → defaults to 16:00 ET), datetime objects, or date objects.

Backtesting

The SDK ships with replay utilities that turn any endpoint into an iterator over a date / minute range. Holiday calendar is built in (NYSE 2018-2026); gap days are skipped silently by default.

Daily replay

from flashalpha_historical import FlashAlphaHistorical, Backtester, iter_days

hx = FlashAlphaHistorical("YOUR_API_KEY")

def strategy(at, snap):
    """Short vol when VRP rich AND dealers long gamma."""
    vrp = snap["volatility"]["vrp"]
    regime = snap["exposure"]["regime"]
    return {
        "signal": "short_strangle" if vrp > 5 and regime == "positive_gamma" else None,
        "vrp": vrp,
        "regime": regime,
    }

bt = Backtester(hx, method="stock_summary", symbol="SPY")
results = bt.run(iter_days("2024-01-02", "2024-03-29"), strategy)

# Convert to DataFrame
import pandas as pd
df = pd.DataFrame(bt.to_records(results))

Minute-level replay

from flashalpha_historical import iter_minutes, replay

# Walk every 15 minutes through one trading day
for at, snap in replay(hx, "exposure_summary", "SPY",
                       iter_minutes("2025-01-15", "2025-01-15", step_minutes=15)):
    print(at, snap["regime"], snap["gamma_flip"], snap["exposures"]["net_gex"])

Quota note: every call counts against your daily plan quota (shared with the live API). 1-minute replay = 390 calls per analytic per day — coarsen with step_minutes=15 or step_minutes=30 for development loops.

API

Every analytics method takes a required at keyword argument.

Coverage

Market data

Exposure analytics

Composite & vol

Errors

from flashalpha_historical import (
    FlashAlphaHistoricalError,    # base
    AuthenticationError,          # 401
    TierRestrictedError,          # 403 — needs Alpha plan
    InvalidAtError,               # 400 — bad `at` format
    NoDataError,                  # 404 — outside coverage / inside gap
    SymbolNotFoundError,          # 404 — symbol not at this `at`
    NoCoverageError,              # 404 — symbol not in historical dataset
    InsufficientDataError,        # 404 — surface grid too sparse
    RateLimitError,               # 429
    ServerError,                  # 5xx
)

try:
    hx.exposure_summary("SPY", at="2017-01-01")  # before coverage starts
except NoDataError as e:
    print("gap:", e)

Known gaps from live (intentional, documented)

• optionquote.bidSize / askSize — always 0 (minute table has no sizes)
• optionquote.volume / gex.call_volume / put_volume — always 0
• optionquote.svi_vol — null with svi_vol_gated: "backtest_mode"
• narrative.data.top_oi_changes — empty array (no prior-day OI diff yet)
• gex.call_oi_change / put_oi_change — always null
• stock_summary.macro.vix_futures / fear_and_greed — null
• vrp.macro.hy_spread — hard-coded 3.5
• 0DTE intraday greeks (delta/gamma/theta/iv) often 0 / null — chain still listed for OI analysis

License

MIT