File size: 1,685 Bytes
59e0f57 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 | import sys
from pathlib import Path
sys.path.append(str(Path("backend").resolve()))
from nifty_backend.runtime import update_live_accuracy, TPLUS1_LATEST_PATH, NIFTY_1M_PATH, latest_parquet_date, NIFTY_1D_PATH
import pandas as pd
from datetime import date
latest_date = latest_parquet_date(NIFTY_1D_PATH)
session_iso = latest_date.isoformat()
print(f"session_iso: {session_iso}")
try:
t1_row = pd.read_csv(TPLUS1_LATEST_PATH).iloc[-1].to_dict()
print("t1_row target:", str(t1_row.get("target_date", ""))[:10])
if str(t1_row.get("target_date", ""))[:10] == session_iso:
pred = str(t1_row.get("prediction", "")).upper()
input_date_str = str(t1_row.get("input_date", ""))[:10]
input_day = date.fromisoformat(input_date_str)
print("input day:", input_day)
minute = pd.read_parquet(NIFTY_1M_PATH, columns=["date", "close"])
minute["dt"] = pd.to_datetime(minute["date"], errors="coerce")
minute = minute.dropna(subset=["dt"])
minute["session_date"] = minute["dt"].dt.normalize()
minute["time_str"] = minute["dt"].dt.strftime("%H:%M")
window = minute[
(minute["session_date"].dt.date == input_day)
& (minute["time_str"] >= "14:00")
& (minute["time_str"] <= "14:20")
].sort_values("dt")
print("window len:", len(window))
if not window.empty:
w_close = float(window.iloc[-1]["close"])
print("w_close:", w_close)
else:
print("window empty!")
print("minute dates available:", minute["session_date"].dt.date.unique())
except Exception as e:
import traceback
traceback.print_exc()
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