diff --git a/.env b/.env
index 75f3437..07fb3df 100644
--- a/.env
+++ b/.env
@@ -11,12 +11,12 @@ LOCAL_PORT=5433
 DB_NAME=postgres
 DB_USER=postgres
 DB_PASSWORD=SolarFalconBreeze
-BACKUP_FILE=/home/boundys/data4/django/agile_predict/.local/backup.sql
+BACKUP_DIR=/home/boundys/data4/django/agile_predict/.local
+LOG_DIR=/home/boundys/data4/django/agile_predict/logs
 APP_NAME=prices-db
 
 # Local PostgreSQL restore
 PGUSER=postgres
 PGPASSWORD=RainbowHenryGibbon
 DBNAME=agile_predict
-WIN_BACKUP_FILE=.local\backup.sql
-
+WIN_BACKUP_FILE=.local\backup.sql
\ No newline at end of file
diff --git a/.github/workflows/fly-deploy.yml b/.github/workflows/fly-deploy.yml
deleted file mode 100644
index b0c246e..0000000
--- a/.github/workflows/fly-deploy.yml
+++ /dev/null
@@ -1,18 +0,0 @@
-# See https://fly.io/docs/app-guides/continuous-deployment-with-github-actions/
-
-name: Fly Deploy
-on:
-  push:
-    branches:
-      - main
-jobs:
-  deploy:
-    name: Deploy app
-    runs-on: ubuntu-latest
-    concurrency: deploy-group    # optional: ensure only one action runs at a time
-    steps:
-      - uses: actions/checkout@v4
-      - uses: superfly/flyctl-actions/setup-flyctl@master
-      - run: flyctl deploy --remote-only
-        env:
-          FLY_API_TOKEN: ${{ secrets.FLY_API_TOKEN }}
diff --git a/README.md b/README.md
index 8fbacef..7f29bc6 100644
--- a/README.md
+++ b/README.md
@@ -1,4 +1,4 @@
-# Agile Predict v2.1.0
+# Agile Predict v2.1.1
 
 This model forecasts Octopus Agile electricity prices up to 14 days in advance using a Machine Learning model trained
 on data from the Balancing Mechanism Reporting System (<a href="https://bmrs.elexon.co.uk/">BRMS</a>), National Grid
diff --git a/bin/backup.sh b/bin/backup.sh
index 3f38ac8..42b0227 100644
--- a/bin/backup.sh
+++ b/bin/backup.sh
@@ -1,21 +1,41 @@
 #!/bin/bash
 
+# Timestamp for logs and backup file naming
+NOW=$(date +"%Y-%m-%d_%H-%M-%S")
+
 # Load environment
-if [ -f .env ]; then
+if [ -f /home/boundys/data4/django/agile_predict/.env ]; then
     set -a
-    . .env
+    . /home/boundys/data4/django/agile_predict/.env
     set +a
 else
     echo "Missing .env file"
     exit 1
 fi
 
+# Ensure LOG_DIR, BACKUP_DIR, and BACKUPS_TO_KEEP are set
+: "${LOG_DIR:?LOG_DIR not set in .env}"
+: "${BACKUP_DIR:?BACKUP_DIR not set in .env}"
+BACKUPS_TO_KEEP="${BACKUPS_TO_KEEP:-3}"
+
+mkdir -p "$LOG_DIR"
+LOG_FILE="$LOG_DIR/backup_$NOW.log"
+
+# Redirect all output to log file
+exec > >(tee -a "$LOG_FILE") 2>&1
+
+echo "================ $(date) ================"
+echo "Starting backup script..."
+
+# Ensure PATH includes location for fly and pg_dump
+export PATH="/home/boundys/.fly/bin:/usr/bin:/bin:$PATH"
+
 echo "APP_NAME: $APP_NAME"
 echo "LOCAL_PORT: $LOCAL_PORT"
 echo "Starting Fly proxy to database..."
 
 # Start Fly proxy
-fly proxy ${LOCAL_PORT}:5432 -a $APP_NAME &
+/home/boundys/.fly/bin/fly proxy ${LOCAL_PORT}:5432 -a $APP_NAME &
 PROXY_PID=$!
 
 # Wait for proxy to be ready
@@ -37,18 +57,38 @@ if ! nc -z localhost "$LOCAL_PORT"; then
     exit 1
 fi
 
-
 echo "Starting backup..."
 export PGPASSWORD=$DB_PASSWORD
 
-pg_dump -h localhost -p $LOCAL_PORT -U $DB_USER -d $DB_NAME > "$BACKUP_FILE"
+mkdir -p "$BACKUP_DIR"
+BACKUP_FILE="$BACKUP_DIR/${DB_NAME}_backup_$NOW.sql"
+LATEST_BACKUP_FILE="$BACKUP_DIR/backup.sql"
+
+/usr/bin/pg_dump -h localhost -p $LOCAL_PORT -U $DB_USER -d $DB_NAME > "$BACKUP_FILE"
 
 if [ $? -eq 0 ]; then
     echo "Backup completed successfully: $BACKUP_FILE"
+    cp "$BACKUP_FILE" "$LATEST_BACKUP_FILE"
+    echo "Updated latest backup symlink: $LATEST_BACKUP_FILE"
 else
     echo "Backup FAILED"
 fi
 
+# Cleanup old backups and their logs beyond $BACKUPS_TO_KEEP
+BACKUP_PATTERN="$BACKUP_DIR/${DB_NAME}_backup_*.sql"
+LOG_PATTERN="$LOG_DIR/backup_*.log"
+
+BACKUP_LIST=$(ls -1t $BACKUP_PATTERN 2>/dev/null)
+LOG_LIST=$(ls -1t $LOG_PATTERN 2>/dev/null)
+
+DELETE_BACKUPS=$(echo "$BACKUP_LIST" | tail -n +$((BACKUPS_TO_KEEP + 1)))
+DELETE_LOGS=$(echo "$LOG_LIST" | tail -n +$((BACKUPS_TO_KEEP + 1)))
+
+echo "$DELETE_BACKUPS" | xargs -r rm --
+echo "$DELETE_LOGS" | xargs -r rm --
+
+echo "Old backups and logs cleaned. Keeping last $BACKUPS_TO_KEEP."
+
 # Kill proxy if still running
 if kill -0 $PROXY_PID 2>/dev/null; then
     kill $PROXY_PID
diff --git a/bin/update.sh b/bin/update.sh
index 6f2a1fd..f92ac71 100644
--- a/bin/update.sh
+++ b/bin/update.sh
@@ -1,3 +1,3 @@
-/home/boundys/.fly/bin/fly ssh console -C "python manage.py update3 --debug" -a prices 
+/home/boundys/.fly/bin/fly ssh console -C "python manage.py update --debug" -a prices 
 /home/boundys/.fly/bin/fly app restart prices
 
diff --git a/logs/backup_2025-04-16_14-40-43.log b/logs/backup_2025-04-16_14-40-43.log
new file mode 100644
index 0000000..55870ab
--- /dev/null
+++ b/logs/backup_2025-04-16_14-40-43.log
@@ -0,0 +1,19 @@
+================ Wed Apr 16 14:40:43 BST 2025 ================
+Starting backup script...
+APP_NAME: prices-db
+LOCAL_PORT: 5433
+Starting Fly proxy to database...
+Waiting for proxy to be ready...
+Waiting for proxy to be ready...
+Proxying local port 5433 to remote [prices-db.internal]:5432
+Connection to localhost (127.0.0.1) 5433 port [tcp/*] succeeded!
+Proxy is ready!
+Connection to localhost (127.0.0.1) 5433 port [tcp/*] succeeded!
+Starting backup...
+pg_dump: warning: there are circular foreign-key constraints on this table:
+pg_dump: detail: nodes
+pg_dump: hint: You might not be able to restore the dump without using --disable-triggers or temporarily dropping the constraints.
+pg_dump: hint: Consider using a full dump instead of a --data-only dump to avoid this problem.
+Backup completed successfully: /home/boundys/data4/django/agile_predict/.local/postgres_backup_2025-04-16_14-40-43.sql
+Updated latest backup symlink: /home/boundys/data4/django/agile_predict/.local/backup.sql
+Proxy closed.
diff --git a/logs/backup_2025-04-16_14-43-01.log b/logs/backup_2025-04-16_14-43-01.log
new file mode 100644
index 0000000..72a799d
--- /dev/null
+++ b/logs/backup_2025-04-16_14-43-01.log
@@ -0,0 +1,20 @@
+================ Wed Apr 16 14:43:01 BST 2025 ================
+Starting backup script...
+APP_NAME: prices-db
+LOCAL_PORT: 5433
+Starting Fly proxy to database...
+Waiting for proxy to be ready...
+Waiting for proxy to be ready...
+Proxying local port 5433 to remote [prices-db.internal]:5432
+Connection to localhost (127.0.0.1) 5433 port [tcp/*] succeeded!
+Proxy is ready!
+Connection to localhost (127.0.0.1) 5433 port [tcp/*] succeeded!
+Starting backup...
+pg_dump: warning: there are circular foreign-key constraints on this table:
+pg_dump: detail: nodes
+pg_dump: hint: You might not be able to restore the dump without using --disable-triggers or temporarily dropping the constraints.
+pg_dump: hint: Consider using a full dump instead of a --data-only dump to avoid this problem.
+Backup completed successfully: /home/boundys/data4/django/agile_predict/.local/postgres_backup_2025-04-16_14-43-01.sql
+Updated latest backup symlink: /home/boundys/data4/django/agile_predict/.local/backup.sql
+Old backups and logs cleaned. Keeping last 3.
+Proxy closed.
diff --git a/logs/backup_2025-04-16_14-46-01.log b/logs/backup_2025-04-16_14-46-01.log
new file mode 100644
index 0000000..ff7f2cc
--- /dev/null
+++ b/logs/backup_2025-04-16_14-46-01.log
@@ -0,0 +1,21 @@
+================ Wed Apr 16 14:46:01 BST 2025 ================
+Starting backup script...
+APP_NAME: prices-db
+LOCAL_PORT: 5433
+Starting Fly proxy to database...
+Waiting for proxy to be ready...
+Waiting for proxy to be ready...
+Waiting for proxy to be ready...
+Proxying local port 5433 to remote [prices-db.internal]:5432
+Connection to localhost (127.0.0.1) 5433 port [tcp/*] succeeded!
+Proxy is ready!
+Connection to localhost (127.0.0.1) 5433 port [tcp/*] succeeded!
+Starting backup...
+pg_dump: warning: there are circular foreign-key constraints on this table:
+pg_dump: detail: nodes
+pg_dump: hint: You might not be able to restore the dump without using --disable-triggers or temporarily dropping the constraints.
+pg_dump: hint: Consider using a full dump instead of a --data-only dump to avoid this problem.
+Backup completed successfully: /home/boundys/data4/django/agile_predict/.local/postgres_backup_2025-04-16_14-46-01.sql
+Updated latest backup symlink: /home/boundys/data4/django/agile_predict/.local/backup.sql
+Old backups and logs cleaned. Keeping last 3.
+Proxy closed.
diff --git a/logs/update.log b/logs/update.log
new file mode 100644
index 0000000..928a7f5
--- /dev/null
+++ b/logs/update.log
@@ -0,0 +1,449 @@
+Connecting to fdaa:9:282c:a7b:3ed:1ddf:e2e6:2...
+2025-04-16 10:15:09 INFO: Max days: 60
+2025-04-16 10:15:09 INFO:   ID  |       Name       |  #FD  |   #AD   | Days |
+2025-04-16 10:15:09 INFO: ------+------------------+-------+---------+------+
+2025-04-16 10:15:09 INFO:    25 | 2025-04-16 06:15 |   612 |    9180 |    0 | 
+2025-04-16 10:15:09 INFO:    24 | 2025-04-15 23:06 |   626 |    9390 |    0 | 
+2025-04-16 10:15:09 INFO:    23 | 2025-04-15 17:14 |   638 |    9570 |    0 | 
+2025-04-16 10:15:09 INFO:    22 | 2025-04-15 16:15 |   640 |    9600 |    0 | 
+2025-04-16 10:15:09 INFO:    21 | 2025-04-15 10:15 |   604 |    9060 |    1 | 
+2025-04-16 10:15:09 INFO:    20 | 2025-04-15 06:15 |   612 |    9180 |    1 | 
+2025-04-16 10:15:09 INFO:    19 | 2025-04-14 22:15 |   628 |    9420 |    1 | 
+2025-04-16 10:15:09 INFO:    18 | 2025-04-14 19:21 |   634 |    9510 |    1 | 
+2025-04-16 10:15:09 INFO:    17 | 2025-04-13 19:37 |   634 |      74 |    2 | 
+2025-04-16 10:15:09 INFO:    16 | 2025-04-13 16:04 |   642 |    1284 |    2 | 
+2025-04-16 10:15:09 INFO:    15 | 2025-04-13 15:57 |   642 |    1284 |    2 | 
+2025-04-16 10:15:09 INFO:    14 | 2025-04-13 15:52 |   642 |    1284 |    2 | 
+2025-04-16 10:15:09 INFO:    13 | 2025-04-13 15:49 |   642 |    1284 |    2 | 
+2025-04-16 10:15:09 INFO:    12 | 2025-04-13 15:43 |   642 |    1284 |    2 | 
+2025-04-16 10:15:09 INFO:    11 | 2025-04-01 21:46 |   628 |    1256 |   14 | 
+2025-04-16 10:15:09 INFO: 
+Deleting (<QuerySet []>)
+
+2025-04-16 10:15:09 INFO: Getting Historic Prices
+2025-04-16 10:15:09 INFO: Prices
+                           day_ahead    agile
+date_time                                    
+2023-06-30 00:00:00+01:00      92.80  19.4880
+2023-06-30 00:30:00+01:00      78.75  16.5375
+2023-06-30 01:00:00+01:00      81.90  17.1990
+2023-06-30 01:30:00+01:00      78.75  16.5375
+2023-06-30 02:00:00+01:00      82.85  17.3985
+...                              ...      ...
+2025-04-16 20:30:00+01:00     105.00  22.0500
+2025-04-16 21:00:00+01:00     107.50  22.5750
+2025-04-16 21:30:00+01:00      90.30  18.9630
+2025-04-16 22:00:00+01:00      84.30  17.7030
+2025-04-16 22:30:00+01:00      76.25  16.0125
+
+[31534 rows x 2 columns]
+2025-04-16 10:15:09 INFO: New Prices
+Empty DataFrame
+Columns: [day_ahead, agile]
+Index: []
+2025-04-16 10:15:09 INFO: GB60:
+2025-04-15 23:00:00+01:00     91.55
+2025-04-16 00:00:00+01:00     79.23
+2025-04-16 01:00:00+01:00     66.31
+2025-04-16 02:00:00+01:00     53.99
+2025-04-16 03:00:00+01:00     48.67
+2025-04-16 04:00:00+01:00     39.20
+2025-04-16 05:00:00+01:00     37.93
+2025-04-16 06:00:00+01:00     78.22
+2025-04-16 07:00:00+01:00     92.08
+2025-04-16 08:00:00+01:00     87.93
+2025-04-16 09:00:00+01:00     70.57
+2025-04-16 10:00:00+01:00     45.53
+2025-04-16 11:00:00+01:00     35.73
+2025-04-16 12:00:00+01:00     28.12
+2025-04-16 13:00:00+01:00     28.44
+2025-04-16 14:00:00+01:00     40.12
+2025-04-16 15:00:00+01:00     53.41
+2025-04-16 16:00:00+01:00     74.96
+2025-04-16 17:00:00+01:00     81.40
+2025-04-16 18:00:00+01:00    102.29
+2025-04-16 19:00:00+01:00    111.24
+2025-04-16 20:00:00+01:00     93.43
+2025-04-16 21:00:00+01:00     82.58
+2025-04-16 22:00:00+01:00     81.12
+dtype: float64
+2025-04-16 10:15:09 INFO: Merged prices:
+                           day_ahead    agile
+date_time                                    
+2023-06-30 00:00:00+01:00      92.80  19.4880
+2023-06-30 00:30:00+01:00      78.75  16.5375
+2023-06-30 01:00:00+01:00      81.90  17.1990
+2023-06-30 01:30:00+01:00      78.75  16.5375
+2023-06-30 02:00:00+01:00      82.85  17.3985
+...                              ...      ...
+2025-04-16 20:30:00+01:00     105.00  22.0500
+2025-04-16 21:00:00+01:00     107.50  22.5750
+2025-04-16 21:30:00+01:00      90.30  18.9630
+2025-04-16 22:00:00+01:00      84.30  17.7030
+2025-04-16 22:30:00+01:00      76.25  16.0125
+
+[31534 rows x 2 columns]
+2025-04-16 10:15:09 INFO: Getting latest Forecast
+    https://api.nationalgrideso.com/api/3/action/datastore_search?resource_id=93c3048e-1dab-4057-a2a9-417540583929&limit=1000
+    https://api.nationalgrideso.com/api/3/action/datastore_search?resource_id=b2f03146-f05d-4824-a663-3a4f36090c71&limit=1000
+    https://api.nationalgrideso.com/api/3/action/datastore_search?resource_id=db6c038f-98af-4570-ab60-24d71ebd0ae5&limit=1000
+    https://api.nationalgrideso.com/api/3/action/datastore_search?resource_id=7c0411cd-2714-4bb5-a408-adb065edf34d&limit=5000
+    https://api.open-meteo.com/v1/forecast
+    https://data.elexon.co.uk/bmrs/api/v1/datasets/NDF
+2025-04-16 10:15:11 INFO:                            bm_wind   solar  ...  time  day_of_week
+2025-04-16 10:30:00+01:00  13916.0  5248.0  ...  10.5            2
+2025-04-16 11:00:00+01:00  13841.0  6072.0  ...  11.0            2
+2025-04-16 11:30:00+01:00  13951.0  6932.0  ...  11.5            2
+2025-04-16 12:00:00+01:00  13956.0  7716.0  ...  12.0            2
+2025-04-16 12:30:00+01:00  13858.0  8310.0  ...  12.5            2
+...                            ...     ...  ...   ...          ...
+2025-04-28 22:00:00+01:00   6538.0     0.0  ...  22.0            0
+2025-04-28 22:30:00+01:00   6566.0     0.0  ...  22.5            0
+2025-04-28 23:00:00+01:00   6602.0     0.0  ...  23.0            0
+2025-04-28 23:30:00+01:00   6644.0     0.0  ...  23.5            0
+2025-04-29 00:00:00+01:00   6692.0     0.0  ...   0.0            1
+
+[604 rows x 9 columns]
+2025-04-16 10:15:11 INFO:     id              name                       created_at
+0   11  2025-04-01 21:46 2025-04-14 18:09:54.304603+00:00
+1   12  2025-04-13 15:43 2025-04-14 18:10:00.741061+00:00
+2   13  2025-04-13 15:49 2025-04-14 18:10:07.697382+00:00
+3   14  2025-04-13 15:52 2025-04-14 18:10:14.684936+00:00
+4   15  2025-04-13 15:57 2025-04-14 18:10:21.526148+00:00
+5   16  2025-04-13 16:04 2025-04-14 18:10:28.274760+00:00
+6   17  2025-04-13 19:37 2025-04-14 18:11:25.824051+00:00
+7   18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00
+8   19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00
+9   20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00
+10  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00
+11  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00
+12  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00
+13  24  2025-04-15 23:06 2025-04-15 22:06:52.758343+00:00
+14  25  2025-04-16 06:15 2025-04-16 05:15:14.881366+00:00
+2025-04-16 10:15:11 INFO: Forecasts Database:
+{ff.to_string()}
+2025-04-16 10:15:11 INFO: train_X:
+                           bm_wind  solar  ...  weekend  day_ahead
+date_time                                  ...                    
+2025-04-16 21:00:00+00:00  10874.0    0.0  ...        0      84.30
+2025-04-16 21:30:00+00:00  10782.0    0.0  ...        0      76.25
+2025-04-01 21:30:00+00:00  14901.0    0.0  ...        0      70.00
+2025-04-01 22:00:00+00:00  15148.0    0.0  ...        0      67.20
+2025-04-01 22:30:00+00:00  15377.0    0.0  ...        0      55.70
+...                            ...    ...  ...      ...        ...
+2025-04-16 18:30:00+00:00  11331.0  530.0  ...        0     105.05
+2025-04-16 19:00:00+00:00  11170.0   45.0  ...        0     101.85
+2025-04-16 19:30:00+00:00  11050.0    0.0  ...        0     105.00
+2025-04-16 20:00:00+00:00  10972.0    0.0  ...        0     107.50
+2025-04-16 20:30:00+00:00  10937.0   20.0  ...        0      90.30
+
+[193 rows x 8 columns]
+2025-04-16 10:15:11 INFO: len(ff)      : 15
+2025-04-16 10:15:11 INFO: len(ff_train): 5
+2025-04-16 10:15:11 INFO: len(train_X) : 193
+2025-04-16 10:15:11 INFO: len(test_X)  : 1016
+2025-04-16 10:15:11 INFO: Earliest ff   : 11
+2025-04-16 10:15:11 INFO: Latest ff     : 25
+2025-04-16 10:15:11 INFO: Earliest ff_t : 11
+2025-04-16 10:15:11 INFO: Latest ff_t   : 25
+2025-04-16 10:15:11 INFO: train_cols:
+2025-04-16 10:15:11 INFO:   bm_wind         :     3454.00    8777.20   15467.00
+2025-04-16 10:15:11 INFO:   solar           :        0.00    2386.76   10571.00
+2025-04-16 10:15:11 INFO:   demand          :    18060.00   25265.01   31810.00
+2025-04-16 10:15:11 INFO:   peak            :        0.00       0.12       1.00
+2025-04-16 10:15:11 INFO:   days_ago        :        0.17       4.81      14.52
+2025-04-16 10:15:11 INFO:   wind_10m        :        6.05      24.75      45.40
+2025-04-16 10:15:11 INFO:   weekend         :        0.00       0.03       1.00
+2025-04-16 10:15:11 INFO: test_X:
+                              id  forecast_id  ...  peak  day_ahead
+date_time                                      ...                 
+2025-04-15 22:30:00+00:00  14637           24  ...   0.0      82.00
+2025-04-15 23:00:00+00:00  14638           24  ...   0.0      78.75
+2025-04-15 23:30:00+00:00  14639           24  ...   0.0      78.85
+2025-04-16 00:00:00+00:00  14640           24  ...   0.0      74.15
+2025-04-16 00:30:00+00:00  14641           24  ...   0.0      72.25
+...                          ...          ...  ...   ...        ...
+2025-04-16 19:30:00+00:00  14053           23  ...   0.0     105.00
+2025-04-16 20:00:00+00:00  14054           23  ...   0.0     107.50
+2025-04-16 20:30:00+00:00  14055           23  ...   0.0      90.30
+2025-04-16 21:00:00+00:00  14056           23  ...   0.0      84.30
+2025-04-16 21:30:00+00:00  14057           23  ...   0.0      76.25
+
+[1016 rows x 22 columns]
+2025-04-16 10:15:14 INFO: Forecast from 2025-04-16 10:30:00+01:00 tp 2025-04-29 00:00:00+01:00
+2025-04-16 10:15:14 INFO: Agile to      2025-04-16 22:30:00+01:00
+2025-04-16 10:15:14 INFO: Forecast
+                           bm_wind   solar  ...  day_ahead_low  day_ahead_high
+2025-04-16 10:30:00+01:00  13916.0  5248.0  ...      70.945318       78.757462
+2025-04-16 11:00:00+01:00  13841.0  6072.0  ...      70.945318       78.757462
+2025-04-16 11:30:00+01:00  13951.0  6932.0  ...      59.063833       70.482971
+2025-04-16 12:00:00+01:00  13956.0  7716.0  ...      36.562767       60.419027
+2025-04-16 12:30:00+01:00  13858.0  8310.0  ...      43.279658       60.789566
+...                            ...     ...  ...            ...             ...
+2025-04-28 22:00:00+01:00   6538.0     0.0  ...      61.889986       81.053406
+2025-04-28 22:30:00+01:00   6566.0     0.0  ...      70.457985       78.812744
+2025-04-28 23:00:00+01:00   6602.0     0.0  ...      68.167249       77.041573
+2025-04-28 23:30:00+01:00   6644.0     0.0  ...      67.858875       77.293633
+2025-04-29 00:00:00+01:00   6692.0     0.0  ...      67.858875       76.749825
+
+[604 rows x 15 columns]
+2025-04-16 10:15:14 INFO: idx0: 2025-04-16 10:30:00+01:00:2025-04-16 22:30:00+01:00
+                           mult  shift
+2025-04-16 10:30:00+01:00     0      1
+2025-04-16 11:00:00+01:00     0      1
+2025-04-16 11:30:00+01:00     0      1
+2025-04-16 12:00:00+01:00     0      1
+2025-04-16 12:30:00+01:00     0      1
+2025-04-16 13:00:00+01:00     0      1
+2025-04-16 13:30:00+01:00     0      1
+2025-04-16 14:00:00+01:00     0      1
+2025-04-16 14:30:00+01:00     0      1
+2025-04-16 15:00:00+01:00     0      1
+2025-04-16 15:30:00+01:00     0      1
+2025-04-16 16:00:00+01:00     0      1
+2025-04-16 16:30:00+01:00     0      1
+2025-04-16 17:00:00+01:00     0      1
+2025-04-16 17:30:00+01:00     0      1
+2025-04-16 18:00:00+01:00     0      1
+2025-04-16 18:30:00+01:00     0      1
+2025-04-16 19:00:00+01:00     0      1
+2025-04-16 19:30:00+01:00     0      1
+2025-04-16 20:00:00+01:00     0      1
+2025-04-16 20:30:00+01:00     0      1
+2025-04-16 21:00:00+01:00     0      1
+2025-04-16 21:30:00+01:00     0      1
+2025-04-16 22:00:00+01:00     0      1
+2025-04-16 22:30:00+01:00     0      1
+2025-04-16 10:15:14 INFO: idx1: 2025-04-16 23:00:00+01:00:2025-04-29 00:00:00+01:00
+                           mult  shift
+2025-04-16 23:00:00+01:00     1      0
+2025-04-16 23:30:00+01:00     1      0
+2025-04-17 00:00:00+01:00     1      0
+2025-04-17 00:30:00+01:00     1      0
+2025-04-17 01:00:00+01:00     1      0
+...                         ...    ...
+2025-04-28 22:00:00+01:00     1      0
+2025-04-28 22:30:00+01:00     1      0
+2025-04-28 23:00:00+01:00     1      0
+2025-04-28 23:30:00+01:00     1      0
+2025-04-29 00:00:00+01:00     1      0
+
+[579 rows x 2 columns]
+2025-04-16 10:15:14 INFO: Scale factors
+                           mult  shift
+2025-04-16 10:30:00+01:00     0      1
+2025-04-16 11:00:00+01:00     0      1
+2025-04-16 11:30:00+01:00     0      1
+2025-04-16 12:00:00+01:00     0      1
+2025-04-16 12:30:00+01:00     0      1
+...                         ...    ...
+2025-04-28 22:00:00+01:00     1      0
+2025-04-28 22:30:00+01:00     1      0
+2025-04-28 23:00:00+01:00     1      0
+2025-04-28 23:30:00+01:00     1      0
+2025-04-29 00:00:00+01:00     1      0
+
+[604 rows x 2 columns]
+2025-04-16 10:15:14 INFO: Scale Factors:
+                           mult  shift  day_ahead    agile
+2025-04-16 10:30:00+01:00     0      1      63.45  13.3245
+2025-04-16 11:00:00+01:00     0      1      52.50  11.0250
+2025-04-16 11:30:00+01:00     0      1      44.10   9.2610
+2025-04-16 12:00:00+01:00     0      1      27.10   5.6910
+2025-04-16 12:30:00+01:00     0      1      23.60   4.9560
+...                         ...    ...        ...      ...
+2025-04-28 22:00:00+01:00     1      0       0.00   0.0000
+2025-04-28 22:30:00+01:00     1      0       0.00   0.0000
+2025-04-28 23:00:00+01:00     1      0       0.00   0.0000
+2025-04-28 23:30:00+01:00     1      0       0.00   0.0000
+2025-04-29 00:00:00+01:00     1      0       0.00   0.0000
+
+[604 rows x 4 columns]
+2025-04-16 10:15:14 INFO:                            mult  shift  ...  day_ahead_low  day_ahead_high
+2025-04-16 10:30:00+01:00     0      1  ...      62.450000       64.450000
+2025-04-16 11:00:00+01:00     0      1  ...      51.500000       53.500000
+2025-04-16 11:30:00+01:00     0      1  ...      43.100000       45.100000
+2025-04-16 12:00:00+01:00     0      1  ...      26.100000       28.100000
+2025-04-16 12:30:00+01:00     0      1  ...      22.600000       24.600000
+...                         ...    ...  ...            ...             ...
+2025-04-28 22:00:00+01:00     1      0  ...      61.889986       81.053406
+2025-04-28 22:30:00+01:00     1      0  ...      70.457985       78.812744
+2025-04-28 23:00:00+01:00     1      0  ...      68.167249       77.041573
+2025-04-28 23:30:00+01:00     1      0  ...      67.858875       77.293633
+2025-04-29 00:00:00+01:00     1      0  ...      67.858875       76.749825
+
+[604 rows x 7 columns]
+2025-04-16 10:15:14 INFO: Final forecast from 2025-04-16 10:30:00+01:00 to 2025-04-29 00:00:00+01:00
+2025-04-16 10:15:14 INFO: Forecast
+                           bm_wind   solar  emb_wind  ...    rad   demand  day_ahead
+2025-04-16 10:30:00+01:00  13916.0  5248.0    4123.0  ...  170.0  26888.0  63.450000
+2025-04-16 11:00:00+01:00  13841.0  6072.0    4195.0  ...  161.5  26097.0  52.500000
+2025-04-16 11:30:00+01:00  13951.0  6932.0    4242.0  ...  184.0  24528.0  44.100000
+2025-04-16 12:00:00+01:00  13956.0  7716.0    4289.0  ...  298.5  24500.0  27.100000
+2025-04-16 12:30:00+01:00  13858.0  8310.0    4392.0  ...  171.5  24025.0  23.600000
+...                            ...     ...       ...  ...    ...      ...        ...
+2025-04-28 22:00:00+01:00   6538.0     0.0    1500.0  ...    0.0  28295.0  81.053406
+2025-04-28 22:30:00+01:00   6566.0     0.0    1516.0  ...    0.0  26885.0  78.812744
+2025-04-28 23:00:00+01:00   6602.0     0.0    1532.0  ...    0.0  25506.0  77.041573
+2025-04-28 23:30:00+01:00   6644.0     0.0    1552.0  ...    0.0  23916.0  77.293633
+2025-04-29 00:00:00+01:00   6692.0     0.0    1572.0  ...    0.0  22841.0  76.749825
+
+[604 rows x 8 columns]
+2025-04-16 10:15:22 INFO:   26: 2025-04-16 10:15
+2025-04-16 10:15:22 INFO:   25: 2025-04-16 06:15
+2025-04-16 10:15:22 INFO:   24: 2025-04-15 23:06
+2025-04-16 10:15:22 INFO:   23: 2025-04-15 17:14
+2025-04-16 10:15:22 INFO:   22: 2025-04-15 16:15
+2025-04-16 10:15:22 INFO:   21: 2025-04-15 10:15
+2025-04-16 10:15:22 INFO:   20: 2025-04-15 06:15
+2025-04-16 10:15:22 INFO:   19: 2025-04-14 22:15
+2025-04-16 10:15:22 INFO:   18: 2025-04-14 19:21
+2025-04-16 10:15:22 INFO:   17: 2025-04-13 19:37
+2025-04-16 10:15:22 INFO:   16: 2025-04-13 16:04
+2025-04-16 10:15:22 INFO:   15: 2025-04-13 15:57
+2025-04-16 10:15:22 INFO:   14: 2025-04-13 15:52
+2025-04-16 10:15:22 INFO:   13: 2025-04-13 15:49
+2025-04-16 10:15:22 INFO:   12: 2025-04-13 15:43
+2025-04-16 10:15:22 INFO:   11: 2025-04-01 21:46
+Restarting machine 17814469b9d798
+No health checks found
+Machine 17814469b9d798 restarted successfully!
+2025-04-16 14:06:31 INFO:    id              name                       created_at
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00
+2025-04-16 14:08:31 INFO:    id              name                       created_at
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00
+2025-04-16 14:08:47 INFO: 
+
+Added Forecast:   25: 2025-04-16 14:08
+2025-04-16 15:01:44 INFO:    id              name                       created_at
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00
+7  25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00
+2025-04-16 15:02:02 INFO: 
+
+Added Forecast:   26: 2025-04-16 15:01
+2025-04-16 15:02:41 INFO:    id              name                       created_at
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00
+7  25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00
+8  26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00
+2025-04-16 15:07:17 INFO:    id              name                       created_at
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00
+7  25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00
+8  26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00
+2025-04-16 15:09:36 INFO:    id              name                       created_at
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00
+7  25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00
+8  26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00
+2025-04-16 15:09:41 INFO: Cross-val scrore: [-14.6491809   -6.45852132 -12.3239967  -15.04279345 -29.85792956]
+2025-04-16 15:09:57 INFO: 
+
+Added Forecast:   27: 2025-04-16 15:09
+2025-04-16 15:19:48 INFO:    id              name                       created_at  mean stdev
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00  None  None
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00  None  None
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00  None  None
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00  None  None
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00  None  None
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00  None  None
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00  None  None
+7  25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00  None  None
+8  26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00  None  None
+9  27  2025-04-16 15:09 2025-04-16 14:09:45.215338+00:00  None  None
+2025-04-16 15:19:53 INFO: Cross-val scrore: [-14.6491809   -6.45852132 -12.3239967  -15.04279345 -29.85792956]
+2025-04-16 15:20:36 INFO:    id              name                       created_at  mean stdev
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00  None  None
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00  None  None
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00  None  None
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00  None  None
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00  None  None
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00  None  None
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00  None  None
+7  25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00  None  None
+8  26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00  None  None
+9  27  2025-04-16 15:09 2025-04-16 14:09:45.215338+00:00  None  None
+2025-04-16 15:20:42 INFO: Cross-val scrore: [-14.6491809   -6.45852132 -12.3239967  -15.04279345 -29.85792956]
+2025-04-16 15:21:00 INFO: 
+
+Added Forecast:   28: 2025-04-16 15:20
+2025-04-16 15:21:17 INFO:     id              name                       created_at       mean     stdev
+0   18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00        NaN       NaN
+1   19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00        NaN       NaN
+2   20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00        NaN       NaN
+3   21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00        NaN       NaN
+4   22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00        NaN       NaN
+5   23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00        NaN       NaN
+6   24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00        NaN       NaN
+7   25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00        NaN       NaN
+8   26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00        NaN       NaN
+9   27  2025-04-16 15:09 2025-04-16 14:09:45.215338+00:00        NaN       NaN
+10  28  2025-04-16 15:20 2025-04-16 14:20:46.422498+00:00 -15.666484  7.730198
+2025-04-16 15:21:23 INFO: Cross-val scrore: [-14.80727976  -6.61297107 -12.27491292 -14.55917733 -29.94994601]
+2025-04-16 15:21:40 INFO: 
+
+Added Forecast:   29: 2025-04-16 15:21
+2025-04-16 15:39:27 INFO:    id              name                       created_at  mean stdev
+0  18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00  None  None
+1  19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00  None  None
+2  20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00  None  None
+3  21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00  None  None
+4  22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00  None  None
+5  23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00  None  None
+6  24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00  None  None
+7  25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00  None  None
+8  26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00  None  None
+9  27  2025-04-16 15:09 2025-04-16 14:09:45.215338+00:00  None  None
+2025-04-16 15:39:32 INFO: Cross-val scrore: [-14.6491809   -6.45852132 -12.3239967  -15.04279345 -29.85792956]
+2025-04-16 15:39:48 INFO: 
+
+Added Forecast:   30: 2025-04-16 15:39
+2025-04-16 15:40:14 INFO:     id              name                       created_at       mean     stdev
+0   18  2025-04-14 19:21 2025-04-14 18:21:21.668951+00:00        NaN       NaN
+1   19  2025-04-14 22:15 2025-04-14 21:15:13.688685+00:00        NaN       NaN
+2   20  2025-04-15 06:15 2025-04-15 05:15:20.771691+00:00        NaN       NaN
+3   21  2025-04-15 10:15 2025-04-15 09:15:13.805469+00:00        NaN       NaN
+4   22  2025-04-15 16:15 2025-04-15 15:15:54.357581+00:00        NaN       NaN
+5   23  2025-04-15 17:14 2025-04-15 16:14:14.069566+00:00        NaN       NaN
+6   24  2025-04-15 22:54 2025-04-15 21:54:36.185845+00:00        NaN       NaN
+7   25  2025-04-16 14:08 2025-04-16 13:08:35.352505+00:00        NaN       NaN
+8   26  2025-04-16 15:01 2025-04-16 14:01:48.755874+00:00        NaN       NaN
+9   27  2025-04-16 15:09 2025-04-16 14:09:45.215338+00:00        NaN       NaN
+10  30  2025-04-16 15:39 2025-04-16 14:39:36.016846+00:00  15.666484  7.730198
+2025-04-16 15:40:19 INFO: Cross-val scrore: [-14.81128377  -6.61297107 -12.27491292 -14.55917733 -29.94994601]
+2025-04-16 15:40:36 INFO: 
+
+Added Forecast:   31: 2025-04-16 15:40
diff --git a/prices/management/commands/update3.py b/prices/management/commands/update.py
similarity index 90%
rename from prices/management/commands/update3.py
rename to prices/management/commands/update.py
index ba214ca..3f64ea1 100644
--- a/prices/management/commands/update3.py
+++ b/prices/management/commands/update.py
@@ -1,5 +1,6 @@
 import xgboost as xg
 from sklearn.metrics import mean_squared_error as MSE
+from sklearn.model_selection import cross_val_score
 from sklearn.neighbors import KernelDensity
 from sklearn.model_selection import train_test_split
 
@@ -61,12 +62,11 @@ def add_arguments(self, parser):
         )
 
         parser.add_argument(
-            "--no_day_of_week",
-            action="store_true",
+            "--max_days",
         )
 
         parser.add_argument(
-            "--nordpool",
+            "--no_day_of_week",
             action="store_true",
         )
 
@@ -90,9 +90,9 @@ def add_arguments(self, parser):
 
     def handle(self, *args, **options):
         # Setup logging
-        local_dir = os.path.join(os.getcwd(), ".local")
-        os.makedirs(local_dir, exist_ok=True)
-        log_file = os.path.join(local_dir, "train_forecast.log")
+        log_dir = os.path.join(os.getcwd(), "logs")
+        os.makedirs(log_dir, exist_ok=True)
+        log_file = os.path.join(log_dir, "update.log")
 
         logger = logging.getLogger(__name__)
         logger.setLevel(logging.INFO)
@@ -111,12 +111,11 @@ def handle(self, *args, **options):
         debug = options.get("debug", False)
 
         min_fd = int(options.get("min_fd", 600) or 600)
-        min_ad = int(options.get("min_ad", 0) or 0)
+        min_ad = int(options.get("min_ad", 1500) or 1500)
+        max_days = int(options.get("max_days", 60) or 60)
 
         no_ranges = options.get("no_ranges", False)
 
-        use_gb60 = options.get("gb60", False)
-
         drop_cols = ["emb_wind"]
         if options.get("no_day_of_week", False):
             drop_cols += ["day_of_week"]
@@ -129,14 +128,32 @@ def handle(self, *args, **options):
             ignore_forecast = [int(x) for x in options.get("ignore_forecast", [])]
 
         # Clean any invalid forecasts
-        for f in Forecasts.objects.all():
-            q = ForecastData.objects.filter(forecast=f)
-            a = AgileData.objects.filter(forecast=f)
-
+        if debug:
+            logger.info(f"Max days: {max_days}")
+
+            logger.info(f"  ID  |       Name       |  #FD  |   #AD   | Days |")
+            logger.info(f"------+------------------+-------+---------+------+")
+        keep = []
+        for f in Forecasts.objects.all().order_by("-created_at"):
+            fd = ForecastData.objects.filter(forecast=f)
+            ad = AgileData.objects.filter(forecast=f)
+            dt = pd.to_datetime(f.name).tz_localize("GB")
+            days = (pd.Timestamp.now(tz="GB") - dt).days
+            if fd.count() < min_fd or ad.count() < min_ad:
+                fail = " <- Fail"
+            else:
+                if days < max_days * 2:
+                    keep.append(f.id)
+                    fail = ""
+                else:
+                    fail = "<- Old"
             if debug:
-                logger.info(f"{f.id} {f.name} {q.count()} {a.count()}")
-            if q.count() < min_fd or a.count() < min_ad:
-                f.delete()
+                logger.info(f"{f.id:5d} | {f.name} | {fd.count():5d} | {ad.count():7d} | {days:4d} | {fail}")
+
+        forecasts_to_delete = Forecasts.objects.exclude(id__in=keep)
+        if debug:
+            logger.info(f"\nDeleting ({forecasts_to_delete})\n")
+        forecasts_to_delete.delete()
 
         prices, start = model_to_df(PriceHistory)
 
@@ -245,8 +262,6 @@ def handle(self, *args, **options):
                         df["dt"] = (df.index - df["created_at"]).dt.total_seconds() / 3600 / 24
                         df["peak"] = ((df["time"] >= 16) & (df["time"] < 19)).astype(float)
 
-                        max_days = 60
-
                         features = [
                             "bm_wind",
                             "solar",
@@ -287,6 +302,12 @@ def handle(self, *args, **options):
                             colsample_bytree=1,
                         )
 
+                        scores = cross_val_score(
+                            xg_model, train_X, train_y, cv=5, scoring="neg_root_mean_squared_error"
+                        )
+
+                        logger.info(f"Cross-val scrore: {scores}")
+
                         xg_model.fit(train_X, train_y, sample_weight=sample_weights, verbose=True)
 
                         # Drop the training data set
@@ -400,8 +421,10 @@ def handle(self, *args, **options):
                             )
                         )
                         sfs.append(
-                            pd.DataFrame(index=fc.index.difference(sfs[0].index.union(sfs[1].index))),
-                            data={"mult": 1, "shift": 0},
+                            pd.DataFrame(
+                                index=fc.index.difference(sfs[0].index.union(sfs[1].index)),
+                                data={"mult": 1, "shift": 0},
+                            )
                         )
                     else:
                         sfs.append(pd.DataFrame(index=fc.index.difference(sfs[0].index), data={"mult": 1, "shift": 0}))
@@ -478,7 +501,7 @@ def handle(self, *args, **options):
                         logger.info(f"Final forecast from {fc.index[0]} to {fc.index[-1]}")
                         logger.info(f"Forecast\n{fc}")
 
-                    this_forecast = Forecasts(name=new_name)
+                    this_forecast = Forecasts(name=new_name, mean=-np.mean(scores), stdev=np.std(scores))
                     this_forecast.save()
                     fc["forecast"] = this_forecast
                     ag["forecast"] = this_forecast
diff --git a/prices/migrations/0028_forecasts_mean_forecasts_stdev_and_more.py b/prices/migrations/0028_forecasts_mean_forecasts_stdev_and_more.py
new file mode 100644
index 0000000..06e5953
--- /dev/null
+++ b/prices/migrations/0028_forecasts_mean_forecasts_stdev_and_more.py
@@ -0,0 +1,28 @@
+# Generated by Django 4.2.20 on 2025-04-16 14:19
+
+from django.db import migrations, models
+
+
+class Migration(migrations.Migration):
+
+    dependencies = [
+        ("prices", "0027_delete_updateerrors"),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name="forecasts",
+            name="mean",
+            field=models.FloatField(null=True),
+        ),
+        migrations.AddField(
+            model_name="forecasts",
+            name="stdev",
+            field=models.FloatField(null=True),
+        ),
+        migrations.AlterField(
+            model_name="forecastdata",
+            name="day_ahead",
+            field=models.FloatField(null=True),
+        ),
+    ]
diff --git a/prices/models.py b/prices/models.py
index 1da52bd..2a650bc 100644
--- a/prices/models.py
+++ b/prices/models.py
@@ -5,6 +5,8 @@
 class Forecasts(models.Model):
     name = models.CharField(unique=True, max_length=64)
     created_at = models.DateTimeField(auto_now_add=True)
+    mean = models.FloatField(null=True)
+    stdev = models.FloatField(null=True)
 
     def __str__(self):
         return self.name
diff --git a/prices/views.py b/prices/views.py
index c397369..f3c6940 100644
--- a/prices/views.py
+++ b/prices/views.py
@@ -225,7 +225,7 @@ def update_chart(self, context, **kwargs):
                         marker={"symbol": 104, "size": 10},
                         mode="lines",
                         line=dict(width=width),
-                        name="Prediction",
+                        name=f"Prediction ({pd.to_datetime(f.name).tz_localize('GB').strftime('%d-%b %H:%M')})",
                         hovertemplate=hover_template_price,
                     )
                 ]
@@ -240,7 +240,7 @@ def update_chart(self, context, **kwargs):
                             line=dict(width=1, color="red"),
                             name="Low",
                             showlegend=False,
-                            hovertemplate=hover_template_price
+                            hovertemplate=hover_template_price,
                         ),
                         go.Scatter(
                             x=df.index,
@@ -367,7 +367,7 @@ def update_chart(self, context, **kwargs):
             legend=legend,
             height=height,
             template="plotly_dark",
-            hovermode='x',
+            hovermode="x",
         )
 
         figure.update_layout(**layout)