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updraft / qt3 /config.py

Built-in monthly scheduler (no GitHub needed); README keep-awake notes

ad84bd6 verified about 1 month ago

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"""Settings for the Updraft Space (Quality-Trend 3 strategy). Secrets come from the Space's "Variables and secrets" settings
(never from the repo): NASDAQ_DATA_LINK_API_KEY, ALPACA_API_KEY_ID, ALPACA_API_SECRET_KEY, REBALANCE_TOKEN."""
from __future__ import annotations

import os

NDL_KEY = os.getenv("NASDAQ_DATA_LINK_API_KEY", "")
ALPACA_KEY = os.getenv("ALPACA_API_KEY_ID", "")
ALPACA_SECRET = os.getenv("ALPACA_API_SECRET_KEY", "")
# Paper unless BOTH ALPACA_PAPER=false and ALPACA_ALLOW_LIVE=yes are set.
ALPACA_PAPER = os.getenv("ALPACA_PAPER", "true").strip().lower() != "false" or \
    os.getenv("ALPACA_ALLOW_LIVE", "").strip().lower() != "yes"
DATA_FEED = os.getenv("ALPACA_DATA_FEED", "iex")
REBALANCE_TOKEN = os.getenv("REBALANCE_TOKEN", "")
AUTO_REBALANCE = os.getenv("AUTO_REBALANCE", "1").strip() == "1"   # the Space's own monthly scheduler

# The backtested rule (Quant Model scripts/finalize_qt3_managed.py)
N, KEEP = 3, 6                      # hold 3; keep a holding while it ranks in the top 6
TOPN, MIN_PRICE, MIN_DV = 1000, 5.0, 5e6
POOL = 0.70                         # quality pool = top 30% of the capital-discipline composite
QCD = ["basic_diluted_gap", "quality_score", "equity_financing_neg", "fcf_yield", "roe", "buyback_yield",
       "net_share_reduction_yoy"]

# Execution
CASH_BUFFER = float(os.getenv("CASH_BUFFER", "0.01"))   # keep 1% cash for fills / drift
MIN_TRADE = float(os.getenv("MIN_TRADE", "25"))         # skip adjustments smaller than this ($) …
BAND = float(os.getenv("REBALANCE_BAND", "0.02"))       # … or smaller than 2% of a position's target
TAG = "qt3"                                             # client_order_id prefix of this strategy's orders

CACHE_DIR = os.getenv("QT3_CACHE", "/tmp/qt3")
RANKING_MAX_AGE_H = float(os.getenv("RANKING_MAX_AGE_H", "20"))
INCEPTION = os.getenv("QT3_INCEPTION", "")              # YYYY-MM-DD for public stats; default = first order


def status() -> dict:
    return {"data_key": bool(NDL_KEY), "alpaca_keys": bool(ALPACA_KEY and ALPACA_SECRET),
            "paper": ALPACA_PAPER, "rebalance_enabled": bool(REBALANCE_TOKEN), "auto_rebalance": AUTO_REBALANCE}

	"""Settings for the Updraft Space (Quality-Trend 3 strategy). Secrets come from the Space's "Variables and secrets" settings
	(never from the repo): NASDAQ_DATA_LINK_API_KEY, ALPACA_API_KEY_ID, ALPACA_API_SECRET_KEY, REBALANCE_TOKEN."""
	from __future__ import annotations

	import os

	NDL_KEY = os.getenv("NASDAQ_DATA_LINK_API_KEY", "")
	ALPACA_KEY = os.getenv("ALPACA_API_KEY_ID", "")
	ALPACA_SECRET = os.getenv("ALPACA_API_SECRET_KEY", "")
	# Paper unless BOTH ALPACA_PAPER=false and ALPACA_ALLOW_LIVE=yes are set.
	ALPACA_PAPER = os.getenv("ALPACA_PAPER", "true").strip().lower() != "false" or \
	os.getenv("ALPACA_ALLOW_LIVE", "").strip().lower() != "yes"
	DATA_FEED = os.getenv("ALPACA_DATA_FEED", "iex")
	REBALANCE_TOKEN = os.getenv("REBALANCE_TOKEN", "")
	AUTO_REBALANCE = os.getenv("AUTO_REBALANCE", "1").strip() == "1" # the Space's own monthly scheduler

	# The backtested rule (Quant Model scripts/finalize_qt3_managed.py)
	N, KEEP = 3, 6 # hold 3; keep a holding while it ranks in the top 6
	TOPN, MIN_PRICE, MIN_DV = 1000, 5.0, 5e6
	POOL = 0.70 # quality pool = top 30% of the capital-discipline composite
	QCD = ["basic_diluted_gap", "quality_score", "equity_financing_neg", "fcf_yield", "roe", "buyback_yield",
	"net_share_reduction_yoy"]

	# Execution
	CASH_BUFFER = float(os.getenv("CASH_BUFFER", "0.01")) # keep 1% cash for fills / drift
	MIN_TRADE = float(os.getenv("MIN_TRADE", "25")) # skip adjustments smaller than this ($) …
	BAND = float(os.getenv("REBALANCE_BAND", "0.02")) # … or smaller than 2% of a position's target
	TAG = "qt3" # client_order_id prefix of this strategy's orders

	CACHE_DIR = os.getenv("QT3_CACHE", "/tmp/qt3")
	RANKING_MAX_AGE_H = float(os.getenv("RANKING_MAX_AGE_H", "20"))
	INCEPTION = os.getenv("QT3_INCEPTION", "") # YYYY-MM-DD for public stats; default = first order


	def status() -> dict:
	return {"data_key": bool(NDL_KEY), "alpaca_keys": bool(ALPACA_KEY and ALPACA_SECRET),
	"paper": ALPACA_PAPER, "rebalance_enabled": bool(REBALANCE_TOKEN), "auto_rebalance": AUTO_REBALANCE}