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bugfixing

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This commit is contained in:
MuslemRahimi 2025-02-10 18:41:24 +01:00
parent 12b3dc44fe
commit 12ac8395e1
1 changed files with 78 additions and 51 deletions
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129
app/restart_json.py
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@ -204,87 +204,114 @@ def filter_latest_analyst_unique_rating(data):
return [value['entry'] for value in latest_entries.values()]
def process_top_analyst_data(data, current_price):
data = [item for item in data if item.get('analystScore', 0) >= 4] if data else []
data = filter_latest_analyst_unique_rating(data)
# Filter recent data from the last 12 months
recent_data = [
item for item in data
if 'date' in item and datetime.strptime(item['date'], "%Y-%m-%d") >= one_year_ago
][:30] # Consider only the last 30 ratings
try:
# Filter for top analysts with score >= 4
filtered_data = []
for item in data:
try:
if item['analystScore'] >= 4:
filtered_data.append(item)
except:
pass
data = filtered_data
data = filter_latest_analyst_unique_rating(data)
# Filter recent data from last 12 months, limit to 30 most recent
recent_data = []
for item in data:
try:
if 'date' in item and datetime.strptime(item['date'], "%Y-%m-%d") >= one_year_ago:
recent_data.append(item)
except:
pass
recent_data = recent_data[:30]
if not recent_data:
return {
"topAnalystCounter": None,
"topAnalystPriceTarget": None,
"topAnalystUpside": None,
"topAnalystRating": None
}
# Count filtered analysts
if len(recent_data) > 0:
filtered_analyst_count = len(recent_data)
# Extract and filter price targets
price_targets = [
float(item['adjusted_pt_current']) for item in recent_data
if 'adjusted_pt_current' in item and item['adjusted_pt_current'] and not math.isnan(float(item['adjusted_pt_current']))
]
# Extract valid price targets
price_targets = []
for item in recent_data:
try:
pt = item.get('adjusted_pt_current')
if pt and not math.isnan(float(pt)):
price_targets.append(float(pt))
except (ValueError, TypeError):
continue
# Calculate median price target
median_price_target = None
if price_targets:
price_targets.sort()
median_index = len(price_targets) // 2
mid = len(price_targets) // 2
median_price_target = (
price_targets[median_index]
if len(price_targets) % 2 != 0 else
(price_targets[median_index - 1] + price_targets[median_index]) / 2
price_targets[mid] if len(price_targets) % 2
else (price_targets[mid-1] + price_targets[mid]) / 2
)
if median_price_target <= 0:
median_price_target = None
# Calculate changes percentage
if median_price_target <= 0:
median_price_target = None
# Calculate upside percentage
upside = None
if median_price_target != None and current_price is not None:
if median_price_target and current_price and current_price > 0:
upside = round(((median_price_target / current_price - 1) * 100), 2)
# Define rating scores
# Rating scores mapping
rating_scores = {
"Strong Buy": 5,
"Buy": 4,
"Buy": 4,
"Hold": 3,
"Sell": 2,
"Strong Sell": 1,
"Strong Sell": 1
}
# Calculate total rating score
total_rating_score = sum(
rating_scores.get(item.get('rating_current'), 0) for item in recent_data
)
# Calculate average rating score
# Calculate average rating
valid_ratings = [
rating_scores.get(item.get('rating_current', ''), 0)
for item in recent_data
]
average_rating_score = (
round(total_rating_score / filtered_analyst_count,2)
if filtered_analyst_count > 0 else 0
round(sum(valid_ratings) / len(valid_ratings), 2)
if valid_ratings else 0
)
# Determine consensus rating
# Map average score to consensus rating
consensus_rating = None
if average_rating_score >= 4.5:
consensus_rating = "Strong Buy"
elif average_rating_score >= 3.5:
consensus_rating = "Buy"
elif average_rating_score >= 2.5:
consensus_rating = "Hold"
consensus_rating = "Hold"
elif average_rating_score >= 1.5:
consensus_rating = "Sell"
elif average_rating_score >= 1.0:
consensus_rating = "Strong Sell"
else:
consensus_rating = None
return {
"topAnalystCounter": filtered_analyst_count,
"topAnalystPriceTarget": median_price_target,
"topAnalystUpside": upside,
"topAnalystRating": consensus_rating,
"topAnalystRating": consensus_rating
}
else:
except Exception as e:
return {
"topAnalystCounter": None,
"topAnalystPriceTarget": None,
"topAnalystUpside": None,
"topAnalystRating": None,
"topAnalystUpside": None,
"topAnalystRating": None
}
@ -647,7 +674,9 @@ async def get_stock_screener(con):
raw_data = cursor.fetchall()
# Iterate through stock_screener_data and update 'price' and 'changesPercentage' if symbols match
# Add VaR value to stock screener
#test mode
#filtered_data = [item for item in stock_screener_data if item['symbol'] == 'AMD']
for item in tqdm(stock_screener_data):
symbol = item['symbol']
@ -795,7 +824,6 @@ async def get_stock_screener(con):
item['topAnalystRating'] = None
try:
with open(f"json/fail-to-deliver/companies/{symbol}.json", 'r') as file:
res = orjson.loads(file.read())[-1]
@ -1630,6 +1658,12 @@ async def save_json_files():
etf_symbols = [row[0] for row in etf_cursor.fetchall()]
stock_screener_data = await get_stock_screener(con)
with open(f"json/stock-screener/data.json", 'w') as file:
ujson.dump(stock_screener_data, file)
data = await get_congress_rss_feed(symbols, etf_symbols)
with open(f"json/congress-trading/rss-feed/data.json", 'w') as file:
ujson.dump(data, file)
@ -1643,11 +1677,6 @@ async def save_json_files():
data = await get_ipo_calendar(con, symbols)
with open(f"json/ipo-calendar/data.json", 'w') as file:
ujson.dump(data, file)
stock_screener_data = await get_stock_screener(con)
with open(f"json/stock-screener/data.json", 'w') as file:
ujson.dump(stock_screener_data, file)
earnings_list = await get_earnings_calendar(con,symbols)
with open(f"json/earnings-calendar/calendar.json", 'w') as file:
@ -1658,12 +1687,10 @@ async def save_json_files():
with open(f"json/dividends-calendar/calendar.json", 'w') as file:
ujson.dump(dividends_list, file)
data = await etf_providers(etf_con, etf_symbols)
with open(f"json/all-etf-providers/data.json", 'w') as file:
ujson.dump(data, file)