Spaces:
Sleeping
Sleeping
File size: 7,656 Bytes
65a8bf3 | 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 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 | import os
import glob
import json
import csv
from datetime import datetime
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
DATA_DIR = os.path.join(BASE_DIR, "data")
HISTORICAL_DIR = os.path.join(DATA_DIR, "simulation_historical_data")
PRICE_DIR = os.path.join(DATA_DIR, "simulation_price_data_July_1-Aug_30")
NEWS_DIR = os.path.join(DATA_DIR, "simulation_news_data_July_1-Aug_30")
def extract_symbol(filename):
name = os.path.basename(filename)
name = name.replace(".csv", "").replace(".json", "")
for prefix in ["simulated_"]:
if name.startswith(prefix):
name = name[len(prefix):]
for suffix in ["_2026_historical", "_historical", "_price_data", "_live"]:
if name.endswith(suffix):
name = name[:-len(suffix)]
import re
match = re.match(r'^[A-Za-z]+', name)
return match.group(0).upper() if match else name.upper()
def load_historical_csvs():
historical = {}
if os.path.exists(HISTORICAL_DIR):
for filepath in glob.glob(os.path.join(HISTORICAL_DIR, "*.csv")):
sym = extract_symbol(filepath)
bars = []
with open(filepath, "r", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
try:
c = float(row["close"])
bars.append({
"date": row["timestamp"].strip(),
"open": round(float(row["open"]), 2),
"high": round(float(row["high"]), 2),
"low": round(float(row["low"]), 2),
"close": round(c, 2),
"volume": int(row.get("volume", 0))
})
except (ValueError, KeyError):
continue
if bars:
# Compute ma20, ma50, rsi
for i, b in enumerate(bars):
if i >= 19:
slice_c = [x["close"] for x in bars[i-19:i+1]]
b["ma20"] = round(sum(slice_c) / 20.0, 2)
else:
b["ma20"] = None
if i >= 49:
slice_c = [x["close"] for x in bars[i-49:i+1]]
b["ma50"] = round(sum(slice_c) / 50.0, 2)
else:
b["ma50"] = None
if i >= 14:
gains = []
losses = []
for j in range(i-13, i+1):
prev = bars[j-1]["close"] if j > 0 else bars[j]["close"]
diff = bars[j]["close"] - prev
if diff > 0:
gains.append(diff)
else:
losses.append(abs(diff))
avg_gain = sum(gains) / 14.0
avg_loss = sum(losses) / 14.0
b["rsi"] = 100.0 if avg_loss == 0 else round(100.0 - (100.0 / (1.0 + (avg_gain / avg_loss))), 1)
else:
b["rsi"] = None
historical[sym] = bars
return historical
def load_price_csvs():
prices = {}
if os.path.exists(PRICE_DIR):
for filepath in glob.glob(os.path.join(PRICE_DIR, "*.csv")):
sym = extract_symbol(filepath)
ticks = []
with open(filepath, "r", encoding="utf-8") as f:
reader = csv.DictReader(f)
for row in reader:
try:
o = float(row["open"])
c = float(row["close"])
ticks.append({
"date": row["timestamp"].strip(),
"open": round(o, 2),
"high": round(float(row["high"]), 2),
"low": round(float(row["low"]), 2),
"close": round(c, 2),
"volume": int(row.get("volume", 0)),
"vwap": round((o + c) / 2.0, 2)
})
except (ValueError, KeyError):
continue
if ticks:
prices[sym] = ticks
return prices
def load_news_jsons():
news_items = []
if os.path.exists(NEWS_DIR):
id_counter = 1
for filepath in glob.glob(os.path.join(NEWS_DIR, "*.json")):
try:
with open(filepath, "r", encoding="utf-8") as f:
data = json.load(f)
items = []
if isinstance(data, list):
items = data
elif isinstance(data, dict):
if "feed" in data and isinstance(data["feed"], list):
items = data["feed"]
else:
for val in data.values():
if isinstance(val, list):
items.extend(val)
for item in items:
syms = [ts.get("ticker") for ts in item.get("ticker_sentiment", []) if ts.get("ticker")]
time_pub = item.get("time_published", "")
if len(time_pub) == 15 and "T" in time_pub:
time_pub = f"{time_pub[:4]}-{time_pub[4:6]}-{time_pub[6:8]}T{time_pub[9:11]}:{time_pub[11:13]}:{time_pub[13:15]}Z"
sent_obj = item.get("ticker_sentiment", [{}])[0] if item.get("ticker_sentiment") else {}
sent_label = sent_obj.get("ticker_sentiment_label", "")
sentiment = "bullish" if "bullish" in sent_label.lower() else ("bearish" if "bearish" in sent_label.lower() else "neutral")
raw_score = float(sent_obj.get("ticker_sentiment_score", 0))
rel_score = float(sent_obj.get("relevance_score", 0.8))
conf_pct = min(99, max(65, int((abs(raw_score) * 0.5 + rel_score * 0.5) * 100)))
topics = item.get("topics", [])
cat = topics[0].get("topic", "General") if topics else "General"
news_items.append({
"id": f"news-backend-{id_counter}",
"headline": item.get("title", "Market Update"),
"summary": item.get("summary", item.get("title", "")),
"source": item.get("source", "MarketWatch"),
"timestamp": time_pub,
"symbols": syms if syms else ["AAPL"],
"sentiment": sentiment,
"confidence": f"{conf_pct}%",
"category": cat
})
id_counter += 1
except Exception as e:
print(f"Error reading news JSON {filepath}: {e}")
return news_items
def load_all_csv_data():
return {
"historical": load_historical_csvs(),
"prices": load_price_csvs(),
"news": load_news_jsons()
}
if __name__ == "__main__":
data = load_all_csv_data()
print(f"Loaded {len(data['historical'])} historical symbols, {len(data['prices'])} price symbols, {len(data['news'])} news items.")
|