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refactor: Move common stock query logic to get_qualified_stocks function

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This commit is contained in:
Bobby (aider) 2025-02-09 00:37:18 -08:00
parent 69c5b12fd3
commit 1e6a05c81d
4 changed files with 73 additions and 137 deletions
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45
src/screener/t_atr_ema.py
View File

@ -71,50 +71,7 @@ def run_atr_ema_scanner(min_price: float, max_price: float, min_volume: int, por
end_ts = int(end_date.timestamp() * 1000000000)
try:
with create_client() as client:
query = f"""
WITH filtered_data AS (
SELECT
ticker,
window_start,
close,
volume,
toDateTime(toDateTime(window_start/1000000000)) as trade_date
FROM stock_db.stock_prices
WHERE window_start BETWEEN {start_ts} AND {end_ts}
AND toDateTime(window_start/1000000000) <= now()
),
daily_data AS (
SELECT
ticker,
toDate(trade_date) as date,
argMax(close, window_start) as daily_close,
sum(volume) as daily_volume
FROM filtered_data
GROUP BY ticker, toDate(trade_date)
HAVING daily_close BETWEEN {min_price} AND {max_price}
AND daily_volume >= {min_volume}
),
latest_data AS (
SELECT
ticker,
argMax(daily_close, date) as last_close,
sum(daily_volume) as total_volume,
max(toUnixTimestamp(date)) as last_update
FROM daily_data
GROUP BY ticker
)
SELECT
ticker,
last_close,
total_volume,
last_update
FROM latest_data
ORDER BY ticker
"""
result = client.query(query)
qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
qualified_stocks = get_qualified_stocks(start_date, end_date, min_price, max_price, min_volume)
if not qualified_stocks:
print("No stocks found matching criteria.")

46
src/screener/t_atr_ema_v2.py
View File

@ -79,51 +79,7 @@ def run_atr_ema_scanner_v2(min_price: float, max_price: float, min_volume: int,
end_ts = int(end_date.timestamp() * 1000000000)
try:
with create_client() as client:
# Query to get qualified stocks
query = f"""
WITH filtered_data AS (
SELECT
ticker,
window_start,
close,
volume,
toDateTime(toDateTime(window_start/1000000000)) as trade_date
FROM stock_db.stock_prices
WHERE window_start BETWEEN {start_ts} AND {end_ts}
AND toDateTime(window_start/1000000000) <= now()
),
daily_data AS (
SELECT
ticker,
toDate(trade_date) as date,
argMax(close, window_start) as daily_close,
sum(volume) as daily_volume
FROM filtered_data
GROUP BY ticker, toDate(trade_date)
HAVING daily_close BETWEEN {min_price} AND {max_price}
AND daily_volume >= {min_volume}
),
latest_data AS (
SELECT
ticker,
argMax(daily_close, date) as last_close,
sum(daily_volume) as total_volume,
max(toUnixTimestamp(date)) as last_update
FROM daily_data
GROUP BY ticker
)
SELECT
ticker,
last_close,
total_volume,
last_update
FROM latest_data
ORDER BY ticker
"""
result = client.query(query)
qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
qualified_stocks = get_qualified_stocks(start_date, end_date, min_price, max_price, min_volume)
if not qualified_stocks:
print("No stocks found matching criteria.")

48
src/screener/t_sunnyband.py
View File

@ -213,53 +213,7 @@ def run_sunny_scanner(min_price: float, max_price: float, min_volume: int, portf
start_ts = int(start_date.timestamp() * 1000000000)
try:
with create_client() as client:
# Query to get stocks meeting criteria with their latest data
query = f"""
WITH filtered_data AS (
SELECT
ticker,
window_start,
close,
volume,
toDateTime(toDateTime(window_start/1000000000)) as trade_date
FROM stock_db.stock_prices
WHERE window_start BETWEEN {start_ts} AND {end_ts}
AND toDateTime(window_start/1000000000) <= now()
),
daily_data AS (
SELECT
ticker,
toDate(trade_date) as trade_date,
argMax(close, window_start) as daily_close,
sum(volume) as daily_volume
FROM filtered_data
GROUP BY ticker, toDate(trade_date)
HAVING daily_close BETWEEN {min_price} AND {max_price}
AND daily_volume >= {min_volume}
),
latest_data AS (
SELECT
ticker,
argMax(daily_close, trade_date) as last_close,
sum(daily_volume) as total_volume,
max(toUnixTimestamp(trade_date)) as last_update
FROM daily_data
GROUP BY ticker
)
SELECT
ticker,
last_close,
total_volume,
last_update
FROM latest_data
ORDER BY ticker
"""
result = client.query(query)
qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
qualified_stocks = get_qualified_stocks(start_date, end_date, min_price, max_price, min_volume)
if not qualified_stocks:
print("No stocks found matching criteria.")

69
src/utils/data_utils.py
View File

@ -38,6 +38,75 @@ def print_signal(signal_data: dict, signal_type: str = "🔍") -> None:
# Print available keys for debugging
print(f"Available keys: {list(signal_data.keys())}")
def get_qualified_stocks(start_date: datetime, end_date: datetime, min_price: float, max_price: float, min_volume: int) -> list:
"""
Get qualified stocks based on price and volume criteria within date range
Args:
start_date (datetime): Start date for data fetch
end_date (datetime): End date for data fetch
min_price (float): Minimum stock price
max_price (float): Maximum stock price
min_volume (int): Minimum trading volume
Returns:
list: List of tuples (ticker, price, volume, last_update)
"""
try:
start_ts = int(start_date.timestamp() * 1000000000)
end_ts = int(end_date.timestamp() * 1000000000)
with create_client() as client:
query = f"""
WITH filtered_data AS (
SELECT
ticker,
window_start,
close,
volume,
toDateTime(toDateTime(window_start/1000000000)) as trade_date
FROM stock_db.stock_prices
WHERE window_start BETWEEN {start_ts} AND {end_ts}
AND toDateTime(window_start/1000000000) <= now()
),
daily_data AS (
SELECT
ticker,
toDate(trade_date) as date,
argMax(close, window_start) as daily_close,
sum(volume) as daily_volume
FROM filtered_data
GROUP BY ticker, toDate(trade_date)
HAVING daily_close BETWEEN {min_price} AND {max_price}
AND daily_volume >= {min_volume}
),
latest_data AS (
SELECT
ticker,
argMax(daily_close, date) as last_close,
sum(daily_volume) as total_volume,
max(toUnixTimestamp(date)) as last_update
FROM daily_data
GROUP BY ticker
)
SELECT
ticker,
last_close,
total_volume,
last_update
FROM latest_data
ORDER BY ticker
"""
result = client.query(query)
qualified_stocks = [(row[0], row[1], row[2], row[3]) for row in result.result_rows]
return qualified_stocks
except Exception as e:
print(f"Error getting qualified stocks: {str(e)}")
return []
def save_signals_to_csv(signals: list, scanner_name: str) -> None:
"""
Save signals to CSV file with standardized format and naming