"""utils/config.py — reads from HF Spaces Secrets (env vars)."""
import os
from pathlib import Path

TMP = Path("/tmp/quant")
for d in ["pdfs","tick_cache","compiled","exports"]:
    (TMP / d).mkdir(parents=True, exist_ok=True)

def get(k, default=""): return os.environ.get(k, default)

ANTHROPIC_API_KEY = get("ANTHROPIC_API_KEY")
HF_TOKEN          = get("HF_TOKEN")
HF_DATASET_REPO   = get("HF_DATASET_REPO")
HF_TICK_REPO      = get("HF_TICK_REPO")

SIMILARITY_THRESHOLD = float(get("SIMILARITY_THRESHOLD", "0.85"))
MAX_TOKENS_PER_CHUNK = int(get("MAX_TOKENS_PER_CHUNK", "3000"))
OCR_DPI              = int(get("OCR_DPI", "300"))

INITIAL_EQUITY   = float(get("INITIAL_EQUITY",   "10000"))
COMMISSION_PCT   = float(get("COMMISSION_PCT",   "0.0002"))
RISK_PER_TRADE   = float(get("RISK_PER_TRADE",   "0.01"))
WF_WINDOWS       = int(get("WF_WINDOWS",         "5"))
WF_IS_RATIO      = float(get("WF_IS_RATIO",      "0.70"))
MAX_PARAM_COMBOS = int(get("MAX_PARAM_COMBOS",   "300"))
MIN_TRADES       = int(get("MIN_TRADES",         "30"))
MIN_SHARPE       = float(get("MIN_SHARPE",       "0.5"))
BACKTEST_TFS     = get("BACKTEST_TIMEFRAMES", "1h,4h,1d").split(",")

CATEGORIES = [
    "Trend Following","Mean Reversion","Statistical Arbitrage",
    "Momentum","Breakout","Volatility Trading","Market Making",
    "Pattern Recognition","Machine Learning","Options Strategy",
    "High Frequency","Pairs Trading","Carry Trade",
    "Seasonal / Calendar","Risk Management","Position Sizing",
    "Portfolio Construction","Market Microstructure","Other",
]

EXTRACTION_PROMPT = """
You are a quantitative finance knowledge extraction engine.

Extract ALL trading strategies, mathematical formulas, and complete trading systems
from the text below (taken from an algorithmic trading book).

Output ONLY valid JSON — no markdown fences, no preamble:
{
  "strategies": [{
    "name": "string", "category": "string", "description": "string",
    "entry_rules": ["string"], "exit_rules": ["string"],
    "filters": ["string"], "timeframes": ["string"], "instruments": ["string"],
    "parameters": {"name": "description with typical value"},
    "mathematical_basis": "string", "source_context": "string"
  }],
  "formulas": [{
    "name": "string", "category": "string",
    "latex": "LaTeX string", "plain_text": "string",
    "variables": {"symbol": "description"},
    "purpose": "string", "usage_context": "string", "source_context": "string"
  }],
  "systems": [{
    "name": "string", "components": ["string"],
    "entry_system": "string", "exit_system": "string",
    "risk_management": "string", "position_sizing": "string",
    "backtesting_notes": "string", "source_context": "string"
  }]
}

Rules: empty arrays [] if nothing found. Preserve exact math. Include LaTeX.
Source: {source_file} | Pages: {page_start}–{page_end}
--- TEXT ---
{text}
--- END ---
""".strip()

COMPILER_PROMPT = """
You are a Julia algorithmic trading code generator.

Convert the strategy JSON below into executable Julia code.
Output ONLY the Julia code — no markdown fences, no explanation, no module/using declarations.

EXACT REQUIRED FORMAT (two functions, nothing else):

function get_param_grid() :: Dict{{String, Vector{{Float64}}}}
    return Dict(
        "param_name" => [val1, val2, val3],
    )
end

function generate_signals(
    open_p :: Vector{{Float64}},
    high   :: Vector{{Float64}},
    low    :: Vector{{Float64}},
    close  :: Vector{{Float64}},
    volume :: Vector{{Float64}},
    params :: Dict{{String, Float64}},
) :: Vector{{Int}}
    n = length(close)
    signals = zeros(Int, n)
    # ... your logic here ...
    return signals
end

RULES (CRITICAL — violations cause compile failure):
1. NO module, NO using, NO include statements
2. ALWAYS check isnan() before using indicator values
3. Return signals[i] = 0 during indicator warmup period
4. Values: 1=long, -1=short, 0=flat only
5. Get int params: Int(round(get(params, "key", default)))
6. Get float params: get(params, "key", default)

AVAILABLE FUNCTIONS (pre-injected, call directly without prefix):
Trend:    sma(s,n)  ema(s,n)  wma(s,n)  tema(s,n)  dema(s,n)
Momentum: rsi(c,n)  macd(c;fast,slow,sig)->(ml,sl,hist)  momentum(s,n)  roc(s,n)
Bands:    bbands(c,n,k)->(up,mid,lo)  keltner(h,l,c,n,k)->(up,mid,lo)
Channel:  donchian(h,l,n)->(up,mid,lo)  highest(s,n)  lowest(s,n)
Volatility: atr(h,l,c,n)  std_dev(s,n)  zscore(s,n)
Oscillators: stoch(h,l,c;k,d)->(K,D)  cci(h,l,c,n)  williams_r(h,l,c,n)
Volume:   vwap(h,l,c,v)  obv(c,v)  cmf(h,l,c,v,n)
Trend strength: adx(h,l,c,n)->(adx,pdi,ndi)
Crosses:  crossover(a,b)->Bool[]  crossunder(a,b)->Bool[]
Math:     mean(v)  std(v)  diff(v)  cumsum(v)  abs(x)  sqrt(x)

Strategy:
{strategy_json}
""".strip()