update and fix
This commit is contained in:
parent
c3adc37d84
commit
234daebef7
3 changed files with 473 additions and 115 deletions
324
crud.py
324
crud.py
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@ -197,114 +197,228 @@ async def get_client_transactions(
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async def get_client_analytics(user_id: str, time_range: str = "30d") -> Optional[ClientAnalytics]:
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"""Get client performance analytics"""
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# Get client ID
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client = await db.fetchone(
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"SELECT id FROM satmachineadmin.dca_clients WHERE user_id = :user_id",
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{"user_id": user_id}
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)
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try:
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from datetime import datetime
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# Get client ID
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client = await db.fetchone(
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"SELECT id FROM satmachineadmin.dca_clients WHERE user_id = :user_id",
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{"user_id": user_id}
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)
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if not client:
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print(f"No client found for user_id: {user_id}")
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return None
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print(f"Found client {client['id']} for user {user_id}, loading analytics for time_range: {time_range}")
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if not client:
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return None
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# Calculate date range
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if time_range == "7d":
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start_date = datetime.now() - timedelta(days=7)
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elif time_range == "30d":
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start_date = datetime.now() - timedelta(days=30)
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elif time_range == "90d":
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start_date = datetime.now() - timedelta(days=90)
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elif time_range == "1y":
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start_date = datetime.now() - timedelta(days=365)
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else: # "all"
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start_date = datetime(2020, 1, 1) # Arbitrary early date
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# Get cost basis history (running average)
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cost_basis_data = await db.fetchall(
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"""
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SELECT
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created_at,
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amount_sats,
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amount_fiat,
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exchange_rate,
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SUM(amount_sats) OVER (ORDER BY created_at) as cumulative_sats,
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SUM(amount_fiat) OVER (ORDER BY created_at) as cumulative_fiat
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FROM satmachineadmin.dca_payments
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WHERE client_id = :client_id
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AND status = 'confirmed'
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AND created_at >= :start_date
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ORDER BY created_at
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""",
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{"client_id": client["id"], "start_date": start_date}
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)
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# Build cost basis history
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cost_basis_history = []
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for record in cost_basis_data:
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avg_cost_basis = record["cumulative_sats"] / record["cumulative_fiat"] if record["cumulative_fiat"] > 0 else 0
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cost_basis_history.append({
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"date": record["created_at"].isoformat(),
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"average_cost_basis": avg_cost_basis,
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"cumulative_sats": record["cumulative_sats"],
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"cumulative_fiat": record["cumulative_fiat"]
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})
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# Get accumulation timeline (daily/weekly aggregation)
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accumulation_data = await db.fetchall(
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"""
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SELECT
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DATE(created_at) as date,
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SUM(amount_sats) as daily_sats,
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SUM(amount_fiat) as daily_fiat,
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COUNT(*) as daily_transactions
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FROM satmachineadmin.dca_payments
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WHERE client_id = :client_id
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AND status = 'confirmed'
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AND created_at >= :start_date
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GROUP BY DATE(created_at)
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ORDER BY date
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""",
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{"client_id": client["id"], "start_date": start_date}
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)
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accumulation_timeline = [
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{
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"date": record["date"],
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"sats": record["daily_sats"],
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"fiat": record["daily_fiat"],
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"transactions": record["daily_transactions"]
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# Calculate date range
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if time_range == "7d":
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start_date = datetime.now() - timedelta(days=7)
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elif time_range == "30d":
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start_date = datetime.now() - timedelta(days=30)
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elif time_range == "90d":
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start_date = datetime.now() - timedelta(days=90)
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elif time_range == "1y":
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start_date = datetime.now() - timedelta(days=365)
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else: # "all"
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start_date = datetime(2020, 1, 1) # Arbitrary early date
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# Get cost basis history (running average)
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cost_basis_data = await db.fetchall(
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"""
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SELECT
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COALESCE(transaction_time, created_at) as transaction_date,
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amount_sats,
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amount_fiat,
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exchange_rate,
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SUM(amount_sats) OVER (ORDER BY COALESCE(transaction_time, created_at)) as cumulative_sats,
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SUM(amount_fiat) OVER (ORDER BY COALESCE(transaction_time, created_at)) as cumulative_fiat
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FROM satmachineadmin.dca_payments
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WHERE client_id = :client_id
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AND status = 'confirmed'
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AND COALESCE(transaction_time, created_at) IS NOT NULL
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AND COALESCE(transaction_time, created_at) >= :start_date
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ORDER BY COALESCE(transaction_time, created_at)
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""",
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{"client_id": client["id"], "start_date": start_date}
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)
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# Build cost basis history
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cost_basis_history = []
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for record in cost_basis_data:
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avg_cost_basis = record["cumulative_sats"] / record["cumulative_fiat"] if record["cumulative_fiat"] > 0 else 0
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# Use transaction_date (which is COALESCE(transaction_time, created_at))
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date_to_use = record["transaction_date"]
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if date_to_use is None:
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print(f"Warning: Null date in cost basis data, skipping record")
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continue
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elif hasattr(date_to_use, 'isoformat'):
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# This is a datetime object
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date_str = date_to_use.isoformat()
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elif hasattr(date_to_use, 'strftime'):
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# This is a date object
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date_str = date_to_use.strftime('%Y-%m-%d')
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elif isinstance(date_to_use, (int, float)):
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# This might be a Unix timestamp - check if it's in a reasonable range
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timestamp = float(date_to_use)
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# Check if this looks like a timestamp (between 1970 and 2100)
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if 0 < timestamp < 4102444800: # Jan 1, 2100
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# Could be seconds or milliseconds
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if timestamp > 1000000000000: # Likely milliseconds
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timestamp = timestamp / 1000
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date_str = datetime.fromtimestamp(timestamp).isoformat()
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else:
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# Not a timestamp, treat as string
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date_str = str(date_to_use)
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print(f"Warning: Numeric date value out of timestamp range: {date_to_use}")
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elif isinstance(date_to_use, str) and date_to_use.isdigit():
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# This is a numeric string - might be a timestamp
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timestamp = float(date_to_use)
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# Check if this looks like a timestamp
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if 0 < timestamp < 4102444800: # Jan 1, 2100
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# Could be seconds or milliseconds
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if timestamp > 1000000000000: # Likely milliseconds
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timestamp = timestamp / 1000
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date_str = datetime.fromtimestamp(timestamp).isoformat()
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else:
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# Not a timestamp, treat as string
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date_str = str(date_to_use)
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print(f"Warning: Numeric date string out of timestamp range: {date_to_use}")
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else:
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# Convert string representation to proper format
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date_str = str(date_to_use)
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print(f"Warning: Unexpected date format: {date_to_use} (type: {type(date_to_use)})")
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cost_basis_history.append({
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"date": date_str,
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"average_cost_basis": avg_cost_basis,
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"cumulative_sats": record["cumulative_sats"],
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"cumulative_fiat": record["cumulative_fiat"]
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})
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# Get accumulation timeline (daily/weekly aggregation)
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accumulation_data = await db.fetchall(
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"""
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SELECT
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DATE(COALESCE(transaction_time, created_at)) as date,
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SUM(amount_sats) as daily_sats,
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SUM(amount_fiat) as daily_fiat,
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COUNT(*) as daily_transactions
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FROM satmachineadmin.dca_payments
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WHERE client_id = :client_id
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AND status = 'confirmed'
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AND COALESCE(transaction_time, created_at) IS NOT NULL
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AND COALESCE(transaction_time, created_at) >= :start_date
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GROUP BY DATE(COALESCE(transaction_time, created_at))
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ORDER BY date
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""",
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{"client_id": client["id"], "start_date": start_date}
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)
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accumulation_timeline = []
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for record in accumulation_data:
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# Handle date conversion safely
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date_value = record["date"]
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if date_value is None:
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print(f"Warning: Null date in accumulation data, skipping record")
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continue
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elif hasattr(date_value, 'isoformat'):
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# This is a datetime object
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date_str = date_value.isoformat()
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elif hasattr(date_value, 'strftime'):
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# This is a date object (from DATE() function)
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date_str = date_value.strftime('%Y-%m-%d')
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elif isinstance(date_value, (int, float)):
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# This might be a Unix timestamp - check if it's in a reasonable range
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timestamp = float(date_value)
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# Check if this looks like a timestamp (between 1970 and 2100)
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if 0 < timestamp < 4102444800: # Jan 1, 2100
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# Could be seconds or milliseconds
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if timestamp > 1000000000000: # Likely milliseconds
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timestamp = timestamp / 1000
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date_str = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d')
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else:
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# Not a timestamp, treat as string
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date_str = str(date_value)
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print(f"Warning: Numeric accumulation date out of timestamp range: {date_value}")
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elif isinstance(date_value, str) and date_value.isdigit():
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# This is a numeric string - might be a timestamp
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timestamp = float(date_value)
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# Check if this looks like a timestamp
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if 0 < timestamp < 4102444800: # Jan 1, 2100
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# Could be seconds or milliseconds
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if timestamp > 1000000000000: # Likely milliseconds
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timestamp = timestamp / 1000
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date_str = datetime.fromtimestamp(timestamp).strftime('%Y-%m-%d')
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else:
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# Not a timestamp, treat as string
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date_str = str(date_value)
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print(f"Warning: Numeric accumulation date string out of timestamp range: {date_value}")
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else:
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# Convert string representation to proper format
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date_str = str(date_value)
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print(f"Warning: Unexpected accumulation date format: {date_value} (type: {type(date_value)})")
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accumulation_timeline.append({
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"date": date_str,
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"sats": record["daily_sats"],
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"fiat": record["daily_fiat"],
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"transactions": record["daily_transactions"]
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})
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# Get transaction frequency metrics
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frequency_stats = await db.fetchone(
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"""
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SELECT
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COUNT(*) as total_transactions,
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AVG(amount_sats) as avg_sats_per_tx,
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AVG(amount_fiat) as avg_fiat_per_tx,
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MIN(COALESCE(transaction_time, created_at)) as first_tx,
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MAX(COALESCE(transaction_time, created_at)) as last_tx
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FROM satmachineadmin.dca_payments
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WHERE client_id = :client_id AND status = 'confirmed'
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""",
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{"client_id": client["id"]}
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)
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# Build transaction frequency with safe date handling
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transaction_frequency = {
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"total_transactions": frequency_stats["total_transactions"] if frequency_stats else 0,
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"avg_sats_per_transaction": frequency_stats["avg_sats_per_tx"] if frequency_stats else 0,
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"avg_fiat_per_transaction": frequency_stats["avg_fiat_per_tx"] if frequency_stats else 0,
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"first_transaction": None,
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"last_transaction": None
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}
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for record in accumulation_data
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]
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# Handle first_tx date safely
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if frequency_stats and frequency_stats["first_tx"]:
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first_tx = frequency_stats["first_tx"]
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if hasattr(first_tx, 'isoformat'):
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transaction_frequency["first_transaction"] = first_tx.isoformat()
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else:
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transaction_frequency["first_transaction"] = str(first_tx)
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# Handle last_tx date safely
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if frequency_stats and frequency_stats["last_tx"]:
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last_tx = frequency_stats["last_tx"]
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if hasattr(last_tx, 'isoformat'):
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transaction_frequency["last_transaction"] = last_tx.isoformat()
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else:
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transaction_frequency["last_transaction"] = str(last_tx)
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# Get transaction frequency metrics
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frequency_stats = await db.fetchone(
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"""
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SELECT
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COUNT(*) as total_transactions,
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AVG(amount_sats) as avg_sats_per_tx,
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AVG(amount_fiat) as avg_fiat_per_tx,
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MIN(created_at) as first_tx,
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MAX(created_at) as last_tx
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FROM satmachineadmin.dca_payments
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WHERE client_id = :client_id AND status = 'confirmed'
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""",
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{"client_id": client["id"]}
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)
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transaction_frequency = {
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"total_transactions": frequency_stats["total_transactions"] if frequency_stats else 0,
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"avg_sats_per_transaction": frequency_stats["avg_sats_per_tx"] if frequency_stats else 0,
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"avg_fiat_per_transaction": frequency_stats["avg_fiat_per_tx"] if frequency_stats else 0,
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"first_transaction": frequency_stats["first_tx"].isoformat() if frequency_stats and frequency_stats["first_tx"] else None,
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"last_transaction": frequency_stats["last_tx"].isoformat() if frequency_stats and frequency_stats["last_tx"] else None
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}
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return ClientAnalytics(
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user_id=user_id,
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cost_basis_history=cost_basis_history,
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accumulation_timeline=accumulation_timeline,
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transaction_frequency=transaction_frequency
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)
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return ClientAnalytics(
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user_id=user_id,
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cost_basis_history=cost_basis_history,
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accumulation_timeline=accumulation_timeline,
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transaction_frequency=transaction_frequency
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)
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except Exception as e:
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print(f"Error in get_client_analytics for user {user_id}: {str(e)}")
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import traceback
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traceback.print_exc()
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return None
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async def get_client_by_user_id(user_id: str):
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@ -51,7 +51,10 @@ window.app = Vue.createApp({
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descending: true,
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page: 1,
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rowsPerPage: 10
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}
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},
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chartTimeRange: '30d',
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dcaChart: null,
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analyticsData: null
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}
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},
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@ -81,14 +84,24 @@ window.app = Vue.createApp({
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formatDate(dateString) {
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if (!dateString) return ''
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return new Date(dateString).toLocaleDateString()
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const date = new Date(dateString)
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if (isNaN(date.getTime())) {
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console.warn('Invalid date string:', dateString)
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return 'Invalid Date'
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}
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return date.toLocaleDateString()
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},
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formatTime(dateString) {
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if (!dateString) return ''
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return new Date(dateString).toLocaleTimeString('en-US', {
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hour: '2-digit',
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minute: '2-digit'
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const date = new Date(dateString)
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if (isNaN(date.getTime())) {
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console.warn('Invalid time string:', dateString)
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return 'Invalid Time'
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}
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return date.toLocaleTimeString('en-US', {
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hour: '2-digit',
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minute: '2-digit'
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})
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},
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@ -104,7 +117,7 @@ window.app = Vue.createApp({
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async loadDashboardData() {
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try {
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const {data} = await LNbits.api.request(
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const { data } = await LNbits.api.request(
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'GET',
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'/satmachineclient/api/v1/dashboard/summary',
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this.g.user.wallets[0].inkey
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@ -118,11 +131,19 @@ window.app = Vue.createApp({
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async loadTransactions() {
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try {
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const {data} = await LNbits.api.request(
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const { data } = await LNbits.api.request(
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'GET',
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'/satmachineclient/api/v1/dashboard/transactions?limit=50',
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this.g.user.wallets[0].inkey
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)
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// Debug: Log the first transaction to see date format
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if (data.length > 0) {
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console.log('Sample transaction data:', data[0])
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console.log('transaction_time:', data[0].transaction_time)
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console.log('created_at:', data[0].created_at)
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}
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// Sort by most recent first and store
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this.transactions = data.sort((a, b) => {
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const dateA = new Date(a.transaction_time || a.created_at)
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@ -167,7 +188,7 @@ window.app = Vue.createApp({
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getNextMilestone() {
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if (!this.dashboardData) return { target: 100000, name: '100k sats' }
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const sats = this.dashboardData.total_sats_accumulated
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if (sats < 10000) return { target: 10000, name: '10k sats' }
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if (sats < 100000) return { target: 100000, name: '100k sats' }
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if (sats < 500000) return { target: 500000, name: '500k sats' }
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@ -180,10 +201,195 @@ window.app = Vue.createApp({
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if (!this.dashboardData) return 0
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const sats = this.dashboardData.total_sats_accumulated
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const milestone = this.getNextMilestone()
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// Show total progress toward the next milestone (from 0)
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const progress = (sats / milestone.target) * 100
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return Math.min(Math.max(progress, 0), 100)
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},
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async loadChartData() {
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try {
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const { data } = await LNbits.api.request(
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'GET',
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`/satmachineclient/api/v1/dashboard/analytics?time_range=${this.chartTimeRange}`,
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this.g.user.wallets[0].inkey
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)
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// Debug: Log analytics data
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console.log('Analytics data received:', data)
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if (data && data.cost_basis_history && data.cost_basis_history.length > 0) {
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console.log('Sample cost basis point:', data.cost_basis_history[0])
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}
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this.analyticsData = data
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// Use nextTick to ensure DOM is ready
|
||||
this.$nextTick(() => {
|
||||
this.initDCAChart()
|
||||
})
|
||||
} catch (error) {
|
||||
console.error('Error loading chart data:', error)
|
||||
}
|
||||
},
|
||||
|
||||
initDCAChart() {
|
||||
console.log('initDCAChart called')
|
||||
console.log('analyticsData:', this.analyticsData)
|
||||
console.log('dcaChart ref:', this.$refs.dcaChart)
|
||||
|
||||
if (!this.analyticsData) {
|
||||
console.log('No analytics data available')
|
||||
return
|
||||
}
|
||||
|
||||
if (!this.$refs.dcaChart) {
|
||||
console.log('No chart ref available')
|
||||
return
|
||||
}
|
||||
|
||||
// Check if Chart.js is loaded
|
||||
if (typeof Chart === 'undefined') {
|
||||
console.error('Chart.js is not loaded')
|
||||
return
|
||||
}
|
||||
|
||||
// Destroy existing chart
|
||||
if (this.dcaChart) {
|
||||
this.dcaChart.destroy()
|
||||
}
|
||||
|
||||
const ctx = this.$refs.dcaChart.getContext('2d')
|
||||
|
||||
// Use accumulation_timeline data which is already aggregated by day
|
||||
const timelineData = this.analyticsData.accumulation_timeline || []
|
||||
|
||||
console.log('Timeline data:', timelineData)
|
||||
console.log('Timeline data length:', timelineData.length)
|
||||
|
||||
if (timelineData.length === 0) {
|
||||
console.log('No timeline data available, falling back to cost basis data')
|
||||
// Fallback to cost_basis_history if no timeline data
|
||||
const costBasisData = this.analyticsData.cost_basis_history || []
|
||||
if (costBasisData.length === 0) {
|
||||
console.log('No chart data available')
|
||||
return
|
||||
}
|
||||
|
||||
// Group cost basis data by date to avoid duplicates
|
||||
const groupedData = new Map()
|
||||
costBasisData.forEach(point => {
|
||||
const dateStr = new Date(point.date).toDateString()
|
||||
if (!groupedData.has(dateStr)) {
|
||||
groupedData.set(dateStr, point)
|
||||
} else {
|
||||
// Use the latest cumulative values for the same date
|
||||
const existing = groupedData.get(dateStr)
|
||||
if (point.cumulative_sats > existing.cumulative_sats) {
|
||||
groupedData.set(dateStr, point)
|
||||
}
|
||||
}
|
||||
})
|
||||
|
||||
const chartData = Array.from(groupedData.values()).sort((a, b) =>
|
||||
new Date(a.date).getTime() - new Date(b.date).getTime()
|
||||
)
|
||||
|
||||
const labels = chartData.map(point => {
|
||||
const date = new Date(point.date)
|
||||
return date.toLocaleDateString('en-US', {
|
||||
month: 'short',
|
||||
day: 'numeric'
|
||||
})
|
||||
})
|
||||
const cumulativeSats = chartData.map(point => point.cumulative_sats)
|
||||
|
||||
this.createChart(labels, cumulativeSats)
|
||||
return
|
||||
}
|
||||
|
||||
// Calculate running totals for timeline data
|
||||
let runningSats = 0
|
||||
const labels = []
|
||||
const cumulativeSats = []
|
||||
|
||||
timelineData.forEach(point => {
|
||||
runningSats += point.sats
|
||||
|
||||
const date = new Date(point.date)
|
||||
if (!isNaN(date.getTime())) {
|
||||
labels.push(date.toLocaleDateString('en-US', {
|
||||
month: 'short',
|
||||
day: 'numeric'
|
||||
}))
|
||||
cumulativeSats.push(runningSats)
|
||||
}
|
||||
})
|
||||
|
||||
this.createChart(labels, cumulativeSats)
|
||||
},
|
||||
|
||||
createChart(labels, cumulativeSats) {
|
||||
const ctx = this.$refs.dcaChart.getContext('2d')
|
||||
|
||||
this.dcaChart = new Chart(ctx, {
|
||||
type: 'line',
|
||||
data: {
|
||||
labels: labels,
|
||||
datasets: [{
|
||||
label: 'Total Sats Accumulated',
|
||||
data: cumulativeSats,
|
||||
borderColor: '#FF9500', // Bitcoin orange
|
||||
backgroundColor: 'rgba(255, 149, 0, 0.1)',
|
||||
borderWidth: 3,
|
||||
fill: true,
|
||||
tension: 0.4,
|
||||
pointBackgroundColor: '#FF9500',
|
||||
pointBorderColor: '#FF9500',
|
||||
pointRadius: 4,
|
||||
pointHoverRadius: 6
|
||||
}]
|
||||
},
|
||||
options: {
|
||||
responsive: true,
|
||||
maintainAspectRatio: false,
|
||||
plugins: {
|
||||
legend: {
|
||||
display: false // Hide legend to keep it clean
|
||||
},
|
||||
tooltip: {
|
||||
mode: 'index',
|
||||
intersect: false,
|
||||
callbacks: {
|
||||
label: function (context) {
|
||||
return `${context.parsed.y.toLocaleString()} sats`
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
scales: {
|
||||
x: {
|
||||
display: true,
|
||||
grid: {
|
||||
display: false
|
||||
}
|
||||
},
|
||||
y: {
|
||||
display: true,
|
||||
grid: {
|
||||
color: 'rgba(0,0,0,0.1)'
|
||||
},
|
||||
ticks: {
|
||||
callback: function (value) {
|
||||
return value.toLocaleString() + ' sats'
|
||||
}
|
||||
}
|
||||
}
|
||||
},
|
||||
interaction: {
|
||||
mode: 'nearest',
|
||||
axis: 'x',
|
||||
intersect: false
|
||||
}
|
||||
}
|
||||
})
|
||||
}
|
||||
},
|
||||
|
||||
|
|
@ -192,7 +398,8 @@ window.app = Vue.createApp({
|
|||
this.loading = true
|
||||
await Promise.all([
|
||||
this.loadDashboardData(),
|
||||
this.loadTransactions()
|
||||
this.loadTransactions(),
|
||||
this.loadChartData()
|
||||
])
|
||||
} catch (error) {
|
||||
console.error('Error initializing dashboard:', error)
|
||||
|
|
@ -202,6 +409,15 @@ window.app = Vue.createApp({
|
|||
}
|
||||
},
|
||||
|
||||
mounted() {
|
||||
// Initialize chart after DOM is ready
|
||||
this.$nextTick(() => {
|
||||
if (this.analyticsData) {
|
||||
this.initDCAChart()
|
||||
}
|
||||
})
|
||||
},
|
||||
|
||||
computed: {
|
||||
hasData() {
|
||||
return this.dashboardData && !this.loading
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@
|
|||
|
||||
{% extends "base.html" %} {% from "macros.jinja" import window_vars with context
|
||||
%} {% block scripts %} {{ window_vars(user) }}
|
||||
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
|
||||
<script src="{{ static_url_for('satmachineclient/static', path='js/index.js') }}"></script>
|
||||
{% endblock %} {% block page %}
|
||||
<div class="row q-col-gutter-md" id="dcaClient">
|
||||
|
|
@ -213,6 +214,33 @@
|
|||
</q-card-section>
|
||||
</q-card>
|
||||
|
||||
<!-- DCA Performance Chart -->
|
||||
<q-card class="q-mb-md">
|
||||
<q-card-section>
|
||||
<h6 class="text-subtitle2 q-my-none q-mb-md">Bitcoin Accumulation Progress</h6>
|
||||
<div class="chart-container" style="position: relative; height: 300px;">
|
||||
<canvas ref="dcaChart" style="max-height: 300px;"></canvas>
|
||||
</div>
|
||||
<div class="row q-mt-sm">
|
||||
<div class="col">
|
||||
<q-btn-toggle
|
||||
v-model="chartTimeRange"
|
||||
@update:model-value="loadChartData"
|
||||
toggle-color="orange"
|
||||
:options="[
|
||||
{label: '7D', value: '7d'},
|
||||
{label: '30D', value: '30d'},
|
||||
{label: '90D', value: '90d'},
|
||||
{label: 'ALL', value: 'all'}
|
||||
]"
|
||||
size="sm"
|
||||
flat
|
||||
/>
|
||||
</div>
|
||||
</div>
|
||||
</q-card-section>
|
||||
</q-card>
|
||||
|
||||
<!-- Transaction History -->
|
||||
<q-card>
|
||||
<q-card-section>
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue