diff --git a/.github/workflows/binance-lev-tier-update.yml b/.github/workflows/binance-lev-tier-update.yml
index 01040534e..cc0c11c68 100644
--- a/.github/workflows/binance-lev-tier-update.yml
+++ b/.github/workflows/binance-lev-tier-update.yml
@@ -2,7 +2,7 @@ name: Binance Leverage tiers update
 
 on:
   schedule:
-    - cron: "25 3 * * 4"
+    - cron: "25 2 * * 4"
   # on demand
   workflow_dispatch:
 
@@ -24,11 +24,7 @@ jobs:
       with:
         persist-credentials: false
 
-    - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
-      with:
-        python-version: "3.14"
-
-    - name: Install uv
+    - name: Install uv and Python 🐍
       uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
       with:
         activate-environment: true
diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 16846d1ac..529f3d2cb 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -32,12 +32,7 @@ jobs:
       with:
         persist-credentials: false
 
-    - name: Set up Python 🐍
-      uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
-      with:
-        python-version: ${{ matrix.python-version }}
-
-    - name: Install uv
+    - name: Install uv and Python 🐍
       uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
       with:
         activate-environment: true
@@ -177,12 +172,7 @@ jobs:
       with:
         persist-credentials: false
 
-    - name: Set up Python 🐍
-      uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 #v6.2.0
-      with:
-        python-version: "3.13"
-
-    - name: Install uv
+    - name: Install uv and Python 🐍
       uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
       with:
         activate-environment: true
@@ -201,7 +191,8 @@ jobs:
       with:
         persist-credentials: false
 
-    - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
+    - name: Set up Python 🐍
+      uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
       with:
         python-version: "3.13"
 
@@ -219,12 +210,7 @@ jobs:
       run: |
         ./tests/test_docs.sh
 
-    - name: Set up Python 🐍
-      uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
-      with:
-        python-version: "3.13"
-
-    - name: Install uv
+    - name: Install uv and Python 🐍
       uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
       with:
         activate-environment: true
@@ -256,12 +242,7 @@ jobs:
       with:
         persist-credentials: false
 
-    - name: Set up Python 🐍
-      uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
-      with:
-        python-version: "${{ matrix.python-version }}"
-
-    - name: Install uv
+    - name: Install uv and Python 🐍
       uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
       with:
         activate-environment: true
@@ -328,12 +309,7 @@ jobs:
       with:
         persist-credentials: false
 
-    - name: Set up Python 🐍
-      uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
-      with:
-        python-version: "${{ matrix.python-version }}"
-
-    - name: Install uv
+    - name: Install uv and Python 🐍
       uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
       with:
         activate-environment: true
diff --git a/.github/workflows/deploy-docs.yml b/.github/workflows/deploy-docs.yml
index bd3d0ee9b..e30a0edae 100644
--- a/.github/workflows/deploy-docs.yml
+++ b/.github/workflows/deploy-docs.yml
@@ -26,12 +26,7 @@ jobs:
         with:
           persist-credentials: true
 
-      - name: Set up Python
-        uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
-        with:
-          python-version: '3.13'
-
-      - name: Install uv
+      - name: Install uv and Python 🐍
         uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
         with:
           activate-environment: true
diff --git a/.github/workflows/devcontainer-build.yml b/.github/workflows/devcontainer-build.yml
index 3cbc8ba6c..d29831375 100644
--- a/.github/workflows/devcontainer-build.yml
+++ b/.github/workflows/devcontainer-build.yml
@@ -31,7 +31,7 @@ jobs:
       with:
         persist-credentials: false
     - name: Login to GitHub Container Registry
-      uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4.0.0
+      uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
       with:
         registry: ghcr.io
         username: ${{ github.actor }}
diff --git a/.github/workflows/docker-build.yml b/.github/workflows/docker-build.yml
index 7dce79b6a..90cc7c629 100644
--- a/.github/workflows/docker-build.yml
+++ b/.github/workflows/docker-build.yml
@@ -59,7 +59,7 @@ jobs:
       uses: ./.github/actions/docker-tags
 
     - name: Login to Docker Hub
-      uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4.0.0
+      uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
       with:
         username: ${{ secrets.DOCKERHUB_USERNAME }}
         password: ${{ secrets.DOCKERHUB_TOKEN }}
@@ -183,13 +183,13 @@ jobs:
       uses: ./.github/actions/docker-tags
 
     - name: Login to Docker Hub
-      uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4.0.0
+      uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
       with:
         username: ${{ secrets.DOCKERHUB_USERNAME }}
         password: ${{ secrets.DOCKERHUB_TOKEN }}
 
     - name: Login to github
-      uses: docker/login-action@b45d80f862d83dbcd57f89517bcf500b2ab88fb2 # v4.0.0
+      uses: docker/login-action@4907a6ddec9925e35a0a9e82d7399ccc52663121 # v4.1.0
       with:
         registry: ghcr.io
         username: ${{ github.actor }}
diff --git a/.github/workflows/pre-commit-update.yml b/.github/workflows/pre-commit-update.yml
index 3d74af2c1..61da3b15e 100644
--- a/.github/workflows/pre-commit-update.yml
+++ b/.github/workflows/pre-commit-update.yml
@@ -25,11 +25,7 @@ jobs:
       with:
         persist-credentials: false
 
-    - uses: actions/setup-python@a309ff8b426b58ec0e2a45f0f869d46889d02405 # v6.2.0
-      with:
-        python-version: "3.13"
-
-    - name: Install uv
+    - name: Install uv and Python 🐍
       uses: astral-sh/setup-uv@cec208311dfd045dd5311c1add060b2062131d57 # v8.0.0
       with:
         activate-environment: true
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index a32221ee3..aaf398201 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -15,7 +15,7 @@ repos:
 
 
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: "v1.20.0"
+    rev: "v1.20.1"
     hooks:
       - id: mypy
         exclude: build_helpers
@@ -26,12 +26,12 @@ repos:
           - types-tabulate==0.10.0.20260308
           - types-python-dateutil==2.9.0.20260402
           - scipy-stubs==1.17.1.3
-          - SQLAlchemy==2.0.48
+          - SQLAlchemy==2.0.49
         # stages: [push]
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
     # Ruff version.
-    rev: 'v0.15.9'
+    rev: 'v0.15.10'
     hooks:
       - id: ruff
       - id: ruff-format
@@ -70,6 +70,6 @@ repos:
 
   # Ensure github actions remain safe
   - repo: https://github.com/woodruffw/zizmor-pre-commit
-    rev: v1.23.1
+    rev: v1.24.1
     hooks:
     - id: zizmor
diff --git a/docs/backtesting.md b/docs/backtesting.md
index 12455b367..00776ed66 100644
--- a/docs/backtesting.md
+++ b/docs/backtesting.md
@@ -160,118 +160,131 @@ The most important in the backtesting is to understand the result.
 A backtesting result will look like that:
 
 ```
-                                                 BACKTESTING REPORT                                                  
-┏━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃          Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
-┡━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
-│ LTC/USDT:USDT │     16 │          1.0 │          56.176 │         5.62 │        16:16:00 │   16     0     0   100 │
-│ ETC/USDT:USDT │     12 │         0.72 │          30.936 │         3.09 │         9:55:00 │   11     0     1  91.7 │
-│ ETH/USDT:USDT │      8 │         0.66 │          17.864 │         1.79 │ 1 day, 13:55:00 │    7     0     1  87.5 │
-│ XLM/USDT:USDT │     10 │         0.31 │          11.054 │         1.11 │        12:08:00 │    9     0     1  90.0 │
-│ BTC/USDT:USDT │      8 │         0.21 │           7.289 │         0.73 │ 3 days, 1:24:00 │    6     0     2  75.0 │
-│ XRP/USDT:USDT │      9 │        -0.14 │          -7.261 │        -0.73 │        21:18:00 │    8     0     1  88.9 │
-│ DOT/USDT:USDT │      6 │         -0.4 │          -9.187 │        -0.92 │         5:35:00 │    4     0     2  66.7 │
-│ ADA/USDT:USDT │      8 │        -1.76 │         -52.098 │        -5.21 │        11:38:00 │    6     0     2  75.0 │
-│         TOTAL │     77 │         0.22 │          54.774 │         5.48 │        22:12:00 │   67     0    10  87.0 │
-└───────────────┴────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
-                                               LEFT OPEN TRADES REPORT                                                
-┏━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃          Pair ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃     Avg Duration ┃  Win  Draw  Loss  Win% ┃
-┡━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
-│ BTC/USDT:USDT │      1 │        -4.14 │          -9.930 │        -0.99 │ 17 days, 8:00:00 │    0     0     1     0 │
-│ ETC/USDT:USDT │      1 │        -4.24 │         -15.365 │        -1.54 │         10:40:00 │    0     0     1     0 │
-│ DOT/USDT:USDT │      1 │        -5.29 │         -19.125 │        -1.91 │         11:30:00 │    0     0     1     0 │
-│         TOTAL │      3 │        -4.56 │         -44.420 │        -4.44 │  6 days, 2:03:00 │    0     0     3     0 │
-└───────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────────┴────────────────────────┘
-                                                ENTER TAG STATS                                                
-┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃
-┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
-│     OTHER │      77 │         0.22 │          54.774 │         5.48 │     22:12:00 │   67     0    10  87.0 │
-│     TOTAL │      77 │         0.22 │          54.774 │         5.48 │     22:12:00 │   67     0    10  87.0 │
-└───────────┴─────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┘
-                                                EXIT REASON STATS                                                 
-┏━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
-┡━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
-│         roi │    67 │         1.05 │         242.179 │        24.22 │        15:49:00 │   67     0     0   100 │
-│ exit_signal │     4 │        -2.23 │         -31.217 │        -3.12 │  1 day, 8:38:00 │    0     0     4     0 │
-│  force_exit │     3 │        -4.56 │         -44.420 │        -4.44 │ 6 days, 2:03:00 │    0     0     3     0 │
-│   stop_loss │     3 │       -10.14 │        -111.768 │       -11.18 │  1 day, 3:05:00 │    0     0     3     0 │
-│       TOTAL │    77 │         0.22 │          54.774 │         5.48 │        22:12:00 │   67     0    10  87.0 │
-└─────────────┴───────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
-                                                        MIXED TAG STATS                                                        
-┏━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
-┡━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
-│           │         roi │     67 │         1.05 │         242.179 │        24.22 │        15:49:00 │   67     0     0   100 │
-│           │ exit_signal │      4 │        -2.23 │         -31.217 │        -3.12 │  1 day, 8:38:00 │    0     0     4     0 │
-│           │  force_exit │      3 │        -4.56 │         -44.420 │        -4.44 │ 6 days, 2:03:00 │    0     0     3     0 │
-│           │   stop_loss │      3 │       -10.14 │        -111.768 │       -11.18 │  1 day, 3:05:00 │    0     0     3     0 │
-│     TOTAL │             │     77 │         0.22 │          54.774 │         5.48 │        22:12:00 │   67     0    10  87.0 │
-└───────────┴─────────────┴────────┴──────────────┴─────────────────┴──────────────┴─────────────────┴────────────────────────┘
-                          SUMMARY METRICS                          
-┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃ Metric                        ┃ Value                                     ┃
-┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
-│ Backtesting from              │ 2025-07-01 00:00:00                       │
-│ Backtesting to                │ 2025-08-01 00:00:00                       │
-│ Trading Mode                  │ Isolated Futures                          │
-│ Max open trades               │ 3                                         │
-│                               │                                           │
-│ Total/Daily Avg Trades        │ 77 / 2.48                                 │
-│ Starting balance              │ 1000 USDT                                 │
-│ Final balance                 │ 1054.669 USDT                             │
-│ Absolute profit               │ 54.669 USDT                               │
-│ Total profit %                │ 5.47%                                     │
-│ CAGR %                        │ 87.14%                                    │
-│ Sortino                       │ 2.46                                      │
-│ Sharpe                        │ 3.73                                      │
-│ Calmar                        │ 40.81                                     │
-│ SQN                           │ 0.69                                      │
-│ Profit factor                 │ 1.29                                      │
-│ Expectancy (Ratio)            │ 0.71 (0.04)                               │
-│ Avg. daily profit             │ 1.764 USDT                                │
-│ Avg. stake amount             │ 345.251 USDT                              │
-│ Total trade volume            │ 53352.96 USDT                             │
-│                               │                                           │
-│ Long / Short trades           │ 67 / 10                                   │
-│ Long / Short profit %         │ 8.93% / -3.46%                            │
-│ Long / Short profit USDT      │ 89.262 / -34.593                          │
-│                               │                                           │
-│ Best Pair                     │ LTC/USDT:USDT 5.62%                       │
-│ Worst Pair                    │ ADA/USDT:USDT -5.21%                      │
-│ Best trade                    │ ETC/USDT:USDT 2.00%                       │
-│ Worst trade                   │ ADA/USDT:USDT -10.17%                     │
-│ Best day                      │ 26.931 USDT                               │
-│ Worst day                     │ -47.741 USDT                              │
-│ Days win/draw/lose            │ 20 / 6 / 5                                │
-│ Min/Max/Avg. Duration Winners │ 0d 00:35 / 5d 18:15 / 0d 15:49            │
-│ Min/Max/Avg. Duration Losers  │ 0d 10:40 / 17d 08:00 / 2d 17:00           │
-│ Max Consecutive Wins / Loss   │ 36 / 3                                    │
-│ Rejected Entry signals        │ 258                                       │
-│ Entry/Exit Timeouts           │ 0 / 0                                     │
-│                               │                                           │
-│ Min/Max balance realized      │ 1003.168 USDT / 1149.577 USDT             │
-│ Min/Max balance unrealized    │ 1000 USDT / 1149.577 USDT                 │
-│ Min/Max balance dates         │ 2025-07-01 00:05:00 / 2025-07-22 15:15:00 │
-│ Max % of account underwater   │ 8.26%                                     │
-│ Absolute drawdown             │ 94.908 USDT (8.26%)                       │
-│ Drawdown duration             │ 9 days 08:50:00                           │
-│ Profit at drawdown start      │ 149.577 USDT                              │
-│ Profit at drawdown end        │ 54.669 USDT                               │
-│ Drawdown start                │ 2025-07-22 15:10:00                       │
-│ Drawdown end                  │ 2025-08-01 00:00:00                       │
-│ Market change                 │ 30.51%                                    │
-└───────────────────────────────┴───────────────────────────────────────────┘
+                                               BACKTESTING REPORT                                                
+┏━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃          Pair ┃ Trades ┃ Avg Profit % ┃  Tot Profit ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ LTC/USDT:USDT │     16 │         1.01 │      56.882 │         5.69 │        16:16:00 │   16     0     0   100 │
+│ ETC/USDT:USDT │     12 │         0.73 │      31.513 │         3.15 │         9:55:00 │   11     0     1  91.7 │
+│ ETH/USDT:USDT │      8 │         0.69 │      18.659 │         1.87 │ 1 day, 13:55:00 │    7     0     1  87.5 │
+│ XLM/USDT:USDT │     10 │          0.3 │      10.694 │         1.07 │        12:08:00 │    9     0     1  90.0 │
+│ BTC/USDT:USDT │      8 │         0.22 │       7.502 │         0.75 │ 3 days, 1:24:00 │    6     0     2  75.0 │
+│ XRP/USDT:USDT │      9 │        -0.13 │      -6.837 │        -0.68 │        21:18:00 │    8     0     1  88.9 │
+│ DOT/USDT:USDT │      6 │        -0.39 │      -9.169 │        -0.92 │         5:35:00 │    4     0     2  66.7 │
+│ ADA/USDT:USDT │      8 │        -1.75 │     -52.089 │        -5.21 │        11:38:00 │    6     0     2  75.0 │
+│         TOTAL │     77 │         0.23 │      57.157 │         5.72 │        22:12:00 │   67     0    10  87.0 │
+└───────────────┴────────┴──────────────┴─────────────┴──────────────┴─────────────────┴────────────────────────┘
+                                             LEFT OPEN TRADES REPORT                                              
+┏━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃          Pair ┃ Trades ┃ Avg Profit % ┃  Tot Profit ┃ Tot Profit % ┃     Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ BTC/USDT:USDT │      1 │        -4.14 │      -9.930 │        -0.99 │ 17 days, 8:00:00 │    0     0     1     0 │
+│ ETC/USDT:USDT │      1 │        -4.24 │     -15.365 │        -1.54 │         10:40:00 │    0     0     1     0 │
+│ DOT/USDT:USDT │      1 │        -5.29 │     -19.166 │        -1.92 │         11:30:00 │    0     0     1     0 │
+│         TOTAL │      3 │        -4.56 │     -44.461 │        -4.45 │  6 days, 2:03:00 │    0     0     3     0 │
+└───────────────┴────────┴──────────────┴─────────────┴──────────────┴──────────────────┴────────────────────────┘
+                                              ENTER TAG STATS                                              
+┏━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Enter Tag ┃ Entries ┃ Avg Profit % ┃  Tot Profit ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│     OTHER │      77 │         0.23 │      57.157 │         5.72 │     22:12:00 │   67     0    10  87.0 │
+│     TOTAL │      77 │         0.23 │      57.157 │         5.72 │     22:12:00 │   67     0    10  87.0 │
+└───────────┴─────────┴──────────────┴─────────────┴──────────────┴──────────────┴────────────────────────┘
+                                              EXIT REASON STATS                                               
+┏━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Exit Reason ┃ Exits ┃ Avg Profit % ┃  Tot Profit ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│         roi │    67 │         1.06 │     245.117 │        24.51 │        15:49:00 │   67     0     0   100 │
+│ exit_signal │     4 │        -2.23 │     -31.226 │        -3.12 │  1 day, 8:38:00 │    0     0     4     0 │
+│  force_exit │     3 │        -4.56 │     -44.461 │        -4.45 │ 6 days, 2:03:00 │    0     0     3     0 │
+│   stop_loss │     3 │       -10.14 │    -112.273 │       -11.23 │  1 day, 3:05:00 │    0     0     3     0 │
+│       TOTAL │    77 │         0.23 │      57.157 │         5.72 │        22:12:00 │   67     0    10  87.0 │
+└─────────────┴───────┴──────────────┴─────────────┴──────────────┴─────────────────┴────────────────────────┘
+                                                      MIXED TAG STATS                                                      
+┏━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Enter Tag ┃ Exit Reason ┃ Trades ┃ Avg Profit % ┃  Tot Profit ┃ Tot Profit % ┃    Avg Duration ┃  Win  Draw  Loss  Win% ┃
+┡━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩
+│           │         roi │     67 │         1.06 │     245.117 │        24.51 │        15:49:00 │   67     0     0   100 │
+│           │ exit_signal │      4 │        -2.23 │     -31.226 │        -3.12 │  1 day, 8:38:00 │    0     0     4     0 │
+│           │  force_exit │      3 │        -4.56 │     -44.461 │        -4.45 │ 6 days, 2:03:00 │    0     0     3     0 │
+│           │   stop_loss │      3 │       -10.14 │    -112.273 │       -11.23 │  1 day, 3:05:00 │    0     0     3     0 │
+│     TOTAL │             │     77 │         0.23 │      57.157 │         5.72 │        22:12:00 │   67     0    10  87.0 │
+└───────────┴─────────────┴────────┴──────────────┴─────────────┴──────────────┴─────────────────┴────────────────────────┘
+                                   SUMMARY METRICS                                    
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Metric                                 ┃ Value                                     ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ Backtesting from                       │ 2025-07-01 00:00:00                       │
+│ Backtesting to                         │ 2025-08-01 00:00:00                       │
+│ Trading Mode                           │ Isolated Futures                          │
+│ Max open trades                        │ 3                                         │
+│                                        │                                           │
+│ Total/Daily Avg Trades                 │ 77 / 2.48                                 │
+│ Starting balance                       │ 1000 USDT                                 │
+│ Final balance                          │ 1057.157 USDT                             │
+│ Absolute profit                        │ 57.157 USDT                               │
+│ Total profit %                         │ 5.72%                                     │
+│ CAGR %                                 │ 92.41%                                    │
+│ Sharpe (closed trades)                 │ 3.89                                      │
+│ Sortino (closed trades)                │ 2.57                                      │
+│ Calmar (closed trades)                 │ 43.03                                     │
+│ SQN                                    │ 0.71                                      │
+│ Profit factor                          │ 1.30                                      │
+│ Expectancy (Ratio)                     │ 0.74 (0.04)                               │
+│ Avg. daily profit                      │ 1.844 USDT                                │
+│ Avg. stake amount                      │ 345.478 USDT                              │
+│ Market change                          │ 30.51%                                    │
+│ Total trade volume                     │ 53390.788 USDT                            │
+│                                        │                                           │
+│ Long / Short trades                    │ 67 / 10                                   │
+│ Long / Short profit %                  │ 9.19% / -3.48%                            │
+│ Long / Short profit USDT               │ 91.940 / -34.783                          │
+│                                        │                                           │
+│ Best Pair                              │ LTC/USDT:USDT 5.69%                       │
+│ Worst Pair                             │ ADA/USDT:USDT -5.21%                      │
+│ Best trade                             │ XRP/USDT:USDT 2.00%                       │
+│ Worst trade                            │ ADA/USDT:USDT -10.17%                     │
+│ Best day                               │ 27.031 USDT                               │
+│ Worst day                              │ -47.826 USDT                              │
+│ Days win/draw/lose                     │ 20 / 6 / 5                                │
+│ Min/Max/Avg. Duration Winners          │ 0d 00:35 / 5d 18:15 / 0d 15:49            │
+│ Min/Max/Avg. Duration Losers           │ 0d 10:40 / 17d 08:00 / 2d 17:00           │
+│ Max Consecutive Wins / Loss            │ 36 / 3                                    │
+│ Rejected Entry signals                 │ 258                                       │
+│ Entry/Exit Timeouts                    │ 0 / 0                                     │
+│                                        │                                           │
+│ Min/Max balance (closed trades)        │ 1003.205 USDT / 1151.425 USDT             │
+│ Max % of account underwater            │ 8.19%                                     │
+│ Absolute drawdown                      │ 94.268 USDT (8.19%)                       │
+│ Drawdown duration                      │ 9 days 08:50:00                           │
+│ Profit at drawdown start               │ 151.425 USDT                              │
+│ Profit at drawdown end                 │ 57.157 USDT                               │
+│ Drawdown start                         │ 2025-07-22 15:10:00                       │
+│ Drawdown end                           │ 2025-08-01 00:00:00                       │
+│                                        │                                           │
+│ Wallet based Metrics                   │                                           │
+│ Min/Max balance (wallet balance)       │ 1000 USDT / 1151.425 USDT                 │
+│ Min/Max balance dates (wallet balance) │ 2025-07-01 00:05:00 / 2025-07-22 15:15:00 │
+│ Max % of account underwater (balance)  │ 5.01%                                     │
+│ Absolute drawdown (wallet balance)     │ 54.76 USDT (4.76%)                        │
+│ Drawdown duration                      │ 7 days 20:35:00                           │
+│ Profit at drawdown start               │ 151.425 USDT                              │
+│ Profit at drawdown end                 │ 96.664 USDT                               │
+│ Drawdown start                         │ 2025-07-22 15:15:00                       │
+│ Drawdown end                           │ 2025-07-30 11:50:00                       │
+│ Sharpe (daily wallet balance)          │ 4.42                                      │
+│ Sortino (daily wallet balance)         │ 4.35                                      │
+│ Calmar (daily wallet balance)          │ 136.07                                    │
+└────────────────────────────────────────┴───────────────────────────────────────────┘
 
 Backtested 2025-07-01 00:00:00 -> 2025-08-01 00:00:00 | Max open trades : 3
-                                                            STRATEGY SUMMARY                                                            
-┏━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
-┃       Strategy ┃ Trades ┃ Avg Profit % ┃ Tot Profit USDT ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃           Drawdown ┃
-┡━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
-│ SampleStrategy │     77 │         0.22 │          54.774 │         5.48 │     22:12:00 │   67     0    10  87.0 │ 94.647 USDT  8.23% │
-└────────────────┴────────┴──────────────┴─────────────────┴──────────────┴──────────────┴────────────────────────┴────────────────────┘
+                                                        STRATEGY SUMMARY                                                        
+┏━━━━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
+┃       Strategy ┃ Trades ┃ Avg Profit % ┃  Tot Profit ┃ Tot Profit % ┃ Avg Duration ┃  Win  Draw  Loss  Win% ┃       Drawdown ┃
+┡━━━━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
+│ SampleStrategy │     77 │         0.23 │      57.157 │         5.72 │     22:12:00 │   67     0    10  87.0 │ 94.268   8.19% │
+└────────────────┴────────┴──────────────┴─────────────┴──────────────┴──────────────┴────────────────────────┴────────────────┘
+
 ```
 
 ### Backtesting report table
@@ -330,59 +343,72 @@ The last element of the backtest report is the summary metrics table.
 It contains key metrics about the performance of your strategy on backtesting data.
 
 ```
-┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
-┃ Metric                        ┃ Value                                     ┃
-┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
-│ Backtesting from              │ 2025-07-01 00:00:00                       │
-│ Backtesting to                │ 2025-08-01 00:00:00                       │
-│ Trading Mode                  │ Isolated Futures                          │
-│ Max open trades               │ 3                                         │
-│                               │                                           │
-│ Total/Daily Avg Trades        │ 77 / 2.48                                 │
-│ Starting balance              │ 1000 USDT                                 │
-│ Final balance                 │ 1054.669 USDT                             │
-│ Absolute profit               │ 54.669 USDT                               │
-│ Total profit %                │ 5.47%                                     │
-│ CAGR %                        │ 87.14%                                    │
-│ Sortino                       │ 2.46                                      │
-│ Sharpe                        │ 3.73                                      │
-│ Calmar                        │ 40.81                                     │
-│ SQN                           │ 0.69                                      │
-│ Profit factor                 │ 1.29                                      │
-│ Expectancy (Ratio)            │ 0.71 (0.04)                               │
-│ Avg. daily profit             │ 1.764 USDT                                │
-│ Avg. stake amount             │ 345.251 USDT                              │
-│ Total trade volume            │ 53352.96 USDT                             │
-│                               │                                           │
-│ Long / Short trades           │ 67 / 10                                   │
-│ Long / Short profit %         │ 8.93% / -3.46%                            │
-│ Long / Short profit USDT      │ 89.262 / -34.593                          │
-│                               │                                           │
-│ Best Pair                     │ LTC/USDT:USDT 5.62%                       │
-│ Worst Pair                    │ ADA/USDT:USDT -5.21%                      │
-│ Best trade                    │ ETC/USDT:USDT 2.00%                       │
-│ Worst trade                   │ ADA/USDT:USDT -10.17%                     │
-│ Best day                      │ 26.931 USDT                               │
-│ Worst day                     │ -47.741 USDT                              │
-│ Days win/draw/lose            │ 20 / 6 / 5                                │
-│ Min/Max/Avg. Duration Winners │ 0d 00:35 / 5d 18:15 / 0d 15:49            │
-│ Min/Max/Avg. Duration Losers  │ 0d 10:40 / 17d 08:00 / 2d 17:00           │
-│ Max Consecutive Wins / Loss   │ 36 / 3                                    │
-│ Rejected Entry signals        │ 258                                       │
-│ Entry/Exit Timeouts           │ 0 / 0                                     │
-│                               │                                           │
-│ Min/Max balance realized      │ 1003.168 USDT / 1149.577 USDT             │
-│ Min/Max balance unrealized    │ 1000 USDT / 1149.577 USDT                 │
-│ Min/Max balance dates         │ 2025-07-01 00:05:00 / 2025-07-22 15:15:00 │
-│ Max % of account underwater   │ 8.26%                                     │
-│ Absolute drawdown             │ 94.908 USDT (8.26%)                       │
-│ Drawdown duration             │ 9 days 08:50:00                           │
-│ Profit at drawdown start      │ 149.577 USDT                              │
-│ Profit at drawdown end        │ 54.669 USDT                               │
-│ Drawdown start                │ 2025-07-22 15:10:00                       │
-│ Drawdown end                  │ 2025-08-01 00:00:00                       │
-│ Market change                 │ 30.51%                                    │
-└───────────────────────────────┴───────────────────────────────────────────┘
+                                   SUMMARY METRICS                                    
+┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
+┃ Metric                                 ┃ Value                                     ┃
+┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
+│ Backtesting from                       │ 2025-07-01 00:00:00                       │
+│ Backtesting to                         │ 2025-08-01 00:00:00                       │
+│ Trading Mode                           │ Isolated Futures                          │
+│ Max open trades                        │ 3                                         │
+│                                        │                                           │
+│ Total/Daily Avg Trades                 │ 77 / 2.48                                 │
+│ Starting balance                       │ 1000 USDT                                 │
+│ Final balance                          │ 1057.157 USDT                             │
+│ Absolute profit                        │ 57.157 USDT                               │
+│ Total profit %                         │ 5.72%                                     │
+│ CAGR %                                 │ 92.41%                                    │
+│ Sharpe (closed trades)                 │ 3.89                                      │
+│ Sortino (closed trades)                │ 2.57                                      │
+│ Calmar (closed trades)                 │ 43.03                                     │
+│ SQN                                    │ 0.71                                      │
+│ Profit factor                          │ 1.30                                      │
+│ Expectancy (Ratio)                     │ 0.74 (0.04)                               │
+│ Avg. daily profit                      │ 1.844 USDT                                │
+│ Avg. stake amount                      │ 345.478 USDT                              │
+│ Market change                          │ 30.51%                                    │
+│ Total trade volume                     │ 53390.788 USDT                            │
+│                                        │                                           │
+│ Long / Short trades                    │ 67 / 10                                   │
+│ Long / Short profit %                  │ 9.19% / -3.48%                            │
+│ Long / Short profit USDT               │ 91.940 / -34.783                          │
+│                                        │                                           │
+│ Best Pair                              │ LTC/USDT:USDT 5.69%                       │
+│ Worst Pair                             │ ADA/USDT:USDT -5.21%                      │
+│ Best trade                             │ XRP/USDT:USDT 2.00%                       │
+│ Worst trade                            │ ADA/USDT:USDT -10.17%                     │
+│ Best day                               │ 27.031 USDT                               │
+│ Worst day                              │ -47.826 USDT                              │
+│ Days win/draw/lose                     │ 20 / 6 / 5                                │
+│ Min/Max/Avg. Duration Winners          │ 0d 00:35 / 5d 18:15 / 0d 15:49            │
+│ Min/Max/Avg. Duration Losers           │ 0d 10:40 / 17d 08:00 / 2d 17:00           │
+│ Max Consecutive Wins / Loss            │ 36 / 3                                    │
+│ Rejected Entry signals                 │ 258                                       │
+│ Entry/Exit Timeouts                    │ 0 / 0                                     │
+│                                        │                                           │
+│ Min/Max balance (closed trades)        │ 1003.205 USDT / 1151.425 USDT             │
+│ Max % of account underwater            │ 8.19%                                     │
+│ Absolute drawdown                      │ 94.268 USDT (8.19%)                       │
+│ Drawdown duration                      │ 9 days 08:50:00                           │
+│ Profit at drawdown start               │ 151.425 USDT                              │
+│ Profit at drawdown end                 │ 57.157 USDT                               │
+│ Drawdown start                         │ 2025-07-22 15:10:00                       │
+│ Drawdown end                           │ 2025-08-01 00:00:00                       │
+│                                        │                                           │
+│ Wallet based Metrics                   │                                           │
+│ Min/Max balance (wallet balance)       │ 1000 USDT / 1151.425 USDT                 │
+│ Min/Max balance dates (wallet balance) │ 2025-07-01 00:05:00 / 2025-07-22 15:15:00 │
+│ Max % of account underwater (balance)  │ 5.01%                                     │
+│ Absolute drawdown (wallet balance)     │ 54.76 USDT (4.76%)                        │
+│ Drawdown duration                      │ 7 days 20:35:00                           │
+│ Profit at drawdown start               │ 151.425 USDT                              │
+│ Profit at drawdown end                 │ 96.664 USDT                               │
+│ Drawdown start                         │ 2025-07-22 15:15:00                       │
+│ Drawdown end                           │ 2025-07-30 11:50:00                       │
+│ Sharpe (daily wallet balance)          │ 4.42                                      │
+│ Sortino (daily wallet balance)         │ 4.35                                      │
+│ Calmar (daily wallet balance)          │ 136.07                                    │
+└────────────────────────────────────────┴───────────────────────────────────────────┘
 ```
 
 - `Backtesting from` / `Backtesting to`: Backtesting range (usually defined with the `--timerange` option).
@@ -394,14 +420,15 @@ It contains key metrics about the performance of your strategy on backtesting da
 - `Absolute profit`: Profit made in stake currency.
 - `Total profit %`: Total profit. Aligned to the `TOTAL` row's `Tot Profit %` from the first table. Calculated as `(End capital − Starting capital) / Starting capital`.
 - `CAGR %`: Compound annual growth rate.
-- `Sortino`: Annualized Sortino ratio.
-- `Sharpe`: Annualized Sharpe ratio.
-- `Calmar`: Annualized Calmar ratio.
+- `Sharpe (closed trades)`: Annualized Sharpe ratio including only closed trades (ignoring open trades with profits or losses).
+- `Sortino (closed trades)`: Annualized Sortino ratio including only closed trades (ignoring open trades with profits or losses).
+- `Calmar (closed trades)`: Annualized Calmar ratio including only closed trades (ignoring open trades with profits or losses).
 - `SQN`: System Quality Number (SQN) - by Van Tharp.
 - `Profit factor`: Sum of the profits of all winning trades divided by the sum of the losses of all losing trades.
 - `Expectancy (Ratio)`: Expectancy ratio, which is the average profit or loss per trade. A negative expectancy ratio means that your strategy is not profitable.
 - `Avg. daily profit`: Average profit per day, calculated as `(Total Profit / Backtest Days)`.
 - `Avg. stake amount`: Average stake amount, either `stake_amount` or the average when using dynamic stake amount.
+- `Market change`: Change of the market during the backtest period. Calculated as the average of all pairs' changes from the first to the last candle using the "close" column.
 - `Total trade volume`: Volume generated on the exchange to reach the above profit.
 - `Long / Short trades`: Split long/short trade counts (only shown when short trades were made).
 - `Long / Short profit %`: Profit percentage for long and short trades (only shown when short trades were made).
@@ -415,15 +442,21 @@ It contains key metrics about the performance of your strategy on backtesting da
 - `Max Consecutive Wins / Loss`: Maximum consecutive wins/losses in a row.
 - `Rejected Entry signals`: Trade entry signals that could not be acted upon due to `max_open_trades` being reached.
 - `Entry/Exit Timeouts`: Entry/exit orders which did not fill (only applicable if custom pricing is used).
-- `Min/Max balance realized`: Lowest and Highest Wallet balance during the backtest period based on closed trades trades.
-- `Min/Max balance unrealized`: Lowest and Highest Wallet balance during the backtest period - including capital tied in open trades.
-- `Min/Max balance dates`: Dates when the minimum and maximum unrealized balance occurred.
+- `Min/Max balance (closed trades)`: Lowest and Highest Wallet balance during the backtest period based on closed trades trades.
 - `Max % of account underwater`: Maximum percentage your account has decreased from the top since the simulation started. Calculated as the maximum of `(Max Balance - Current Balance) / (Max Balance)`.
 - `Absolute drawdown`: Maximum absolute drawdown experienced, including percentage relative to the account calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`..
+- `Absolute drawdown (wallet balance)`: Maximum absolute drawdown experienced based on the unrealized balance, including percentage relative to the account calculated as `(Absolute Drawdown) / (DrawdownHigh + startingBalance)`.
 - `Drawdown duration`: Duration of the largest drawdown period.
 - `Profit at drawdown start` / `Profit at drawdown end`: Profit at the beginning and end of the largest drawdown period.
 - `Drawdown start` / `Drawdown end`: Start and end datetime for the largest drawdown (can also be visualized via the `plot-dataframe` sub-command).
-- `Market change`: Change of the market during the backtest period. Calculated as the average of all pairs' changes from the first to the last candle using the "close" column.
+- `Min/Max balance (wallet balance)`: Lowest and Highest Wallet balance during the backtest period - including capital tied in open trades.
+- `Min/Max balance dates (wallet balance)`: Dates when the minimum and maximum unrealized balance occurred.
+- `Sharpe (wallet balance)` Annualized Sharpe ratio calculation including unrealized profits.
+- `Sortino (wallet balance)` Annualized Sortino ratio calculation including unrealized profits.
+- `Calmar (wallet balance)` Annualized Calmar ratio calculation including unrealized profits.
+
+!!! Tip "Wallet based Metrics"
+    The metrics under the "Wallet based Metrics" section are calculated based on the unrealized balance, which includes the capital tied in open trades. This provides a more comprehensive view of the strategy's performance, as it accounts for both realized and unrealized profits and losses.
 
 ### Daily / Weekly / Monthly / Yearly breakdown
 
diff --git a/docs/plotting.md b/docs/plotting.md
index ae480e78f..599a71dbc 100644
--- a/docs/plotting.md
+++ b/docs/plotting.md
@@ -111,10 +111,10 @@ It also allows multiple subplots to display both MACD and RSI at the same time.
 
 Plot type can be configured using `type` key. Possible types are:
 
-* `scatter` corresponding to `plotly.graph_objects.Scatter` class (default).
-* `bar` corresponding to `plotly.graph_objects.Bar` class.
+* `scatter` corresponding a scatter plot.
+* `bar` corresponding to a bar plot.
 
-Extra parameters to `plotly.graph_objects.*` constructor can be specified in `plotly` dict.
+Extra parameters to `plotly.graph_objects.*` constructor can be specified in `plotly` dict - these are only supported when using plotly as plotting library and will be ignored when using freq-ui.
 
 Sample configuration with inline comments explaining the process:
 
@@ -163,7 +163,7 @@ def plot_config(self):
 ```
 
 ??? Note "As attribute (former method)"
-    Assigning plot_config is also possible as Attribute (this used to be the default way).
+    Assigning `plot_config` is also possible as Attribute (this used to be the default way).
     This has the disadvantage that strategy parameters are not available, preventing certain configurations from working.
 
     ``` python
diff --git a/freqtrade/data/btanalysis/bt_fileutils.py b/freqtrade/data/btanalysis/bt_fileutils.py
index a97d5bef3..9328ba428 100644
--- a/freqtrade/data/btanalysis/bt_fileutils.py
+++ b/freqtrade/data/btanalysis/bt_fileutils.py
@@ -10,7 +10,6 @@ from io import BytesIO, StringIO
 from pathlib import Path
 from typing import Any, Literal
 
-import numpy as np
 import pandas as pd
 
 from freqtrade.constants import LAST_BT_RESULT_FN
@@ -308,7 +307,7 @@ def get_backtest_market_change(filename: Path, include_ts: bool = True) -> pd.Da
     else:
         df = pd.read_feather(filename)
     if include_ts:
-        df.loc[:, "__date_ts"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+        df.loc[:, "__date_ts"] = df.loc[:, "date"].dt.as_unit("ms").astype("int64")
     return df
 
 
@@ -326,7 +325,7 @@ def get_backtest_wallet_change(filename: Path, strategy_name: str) -> pd.DataFra
         data = load_file_from_zip(filename, f"{filename.stem}_{strategy_name}_wallet.feather")
         df = pd.read_feather(BytesIO(data))
 
-        df.loc[:, "__date_ts"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+        df.loc[:, "__date_ts"] = df.loc[:, "date"].dt.as_unit("ms").astype("int64")
         return df
     except ValueError:
         pass
diff --git a/freqtrade/data/history/datahandlers/jsondatahandler.py b/freqtrade/data/history/datahandlers/jsondatahandler.py
index 1a33b3e2f..332b687b4 100644
--- a/freqtrade/data/history/datahandlers/jsondatahandler.py
+++ b/freqtrade/data/history/datahandlers/jsondatahandler.py
@@ -1,6 +1,5 @@
 import logging
 
-import numpy as np
 from pandas import DataFrame, read_json, to_datetime
 
 from freqtrade import misc
@@ -35,8 +34,8 @@ class JsonDataHandler(IDataHandler):
         filename = self._pair_data_filename(self._datadir, pair, timeframe, candle_type)
         self.create_dir_if_needed(filename)
         _data = data.copy()
-        # Convert date to int
-        _data["date"] = _data["date"].astype(np.int64) // 1000 // 1000
+        # Convert date to int (milliseconds)
+        _data["date"] = _data["date"].dt.as_unit("ms").astype("int64")
 
         # Reset index, select only appropriate columns and save as json
         _data.reset_index(drop=True).loc[:, self._columns].to_json(
diff --git a/freqtrade/data/metrics.py b/freqtrade/data/metrics.py
index a9d3963d2..4d66104ea 100644
--- a/freqtrade/data/metrics.py
+++ b/freqtrade/data/metrics.py
@@ -333,6 +333,72 @@ def calculate_expectancy(trades: pd.DataFrame) -> tuple[float, float]:
     return expectancy, expectancy_ratio
 
 
+def _calculate_annualized_ratio(
+    expected_returns_mean: float,
+    denominator: float,
+    annualization_factor: int = 365,
+) -> float:
+    """
+    Helper function to calculate annualized ratios like Sharpe and Sortino.
+    :param expected_returns_mean: Mean of the returns (expected returns)
+    :param denominator: Denominator of the ratio (e.g. standard deviation for Sharpe)
+    :param annualization_factor: Factor to annualize the ratio (default is 365 for daily returns)
+    :return: Annualized ratio, or -100.0 if denominator is zero or NaN to indicate this is
+             not optimal.
+    """
+    if denominator != 0 and not np.isnan(denominator):
+        return float(expected_returns_mean / denominator * np.sqrt(annualization_factor))
+
+    # Define high (negative) ratio to be clear that this is NOT optimal.
+    return -100.0
+
+
+def _calculate_daily_returns_from_balance(
+    balance_history: pd.DataFrame,
+    date_col: str,
+    balance_col: str,
+) -> pd.Series:
+    wallet = _prepare_balance_history(balance_history, date_col, balance_col)
+    if len(wallet) == 0:
+        return pd.DataFrame(columns=[date_col, balance_col])
+
+    # Sample balance to daily end-of-day values to normalize variable snapshot frequency.
+    daily_balance = (
+        wallet.set_index(date_col)[balance_col].resample("1D").last().dropna().rename(balance_col)
+    )
+    daily_balance = daily_balance.reset_index()
+
+    if len(daily_balance) < 2:
+        return pd.Series(dtype=float)
+
+    return daily_balance[balance_col].pct_change().dropna()
+
+
+def _prepare_balance_history(
+    balance_history: pd.DataFrame,
+    date_col: str,
+    balance_col: str,
+) -> pd.DataFrame:
+    """
+    Prepare balance history for calculations by filtering out rows with
+    missing date or balance values.
+    """
+    if (
+        len(balance_history) == 0
+        or date_col not in balance_history
+        or balance_col not in balance_history
+    ):
+        return pd.DataFrame(columns=[date_col, balance_col])
+
+    wallet = balance_history.loc[:, [date_col, balance_col]].copy()
+    wallet = wallet.dropna(subset=[date_col, balance_col]).sort_values(date_col)
+
+    if len(wallet) == 0:
+        return pd.DataFrame(columns=[date_col, balance_col])
+
+    return wallet
+
+
 def calculate_sortino(
     trades: pd.DataFrame,
     min_date: datetime | None,
@@ -354,14 +420,31 @@ def calculate_sortino(
 
     down_stdev = np.std(trades.loc[trades["profit_abs"] < 0, "profit_abs"] / starting_balance)
 
-    if down_stdev != 0 and not np.isnan(down_stdev):
-        sortino_ratio = expected_returns_mean / down_stdev * np.sqrt(365)
-    else:
-        # Define high (negative) sortino ratio to be clear that this is NOT optimal.
-        sortino_ratio = -100
+    return _calculate_annualized_ratio(expected_returns_mean, down_stdev)
 
-    # print(expected_returns_mean, down_stdev, sortino_ratio)
-    return sortino_ratio
+
+def calculate_sortino_from_balance(
+    balance_history: pd.DataFrame,
+    date_col: str = "date",
+    balance_col: str = "total_quote",
+) -> float:
+    """
+    Calculate sortino ratio from historical balance snapshots.
+
+    :param balance_history: DataFrame containing at least date and balance columns
+    :param date_col: Column containing timestamps
+    :param balance_col: Column containing historical balance values
+    :return: sortino
+    """
+    daily_returns = _calculate_daily_returns_from_balance(balance_history, date_col, balance_col)
+
+    if len(daily_returns) == 0:
+        return 0.0
+
+    expected_returns_mean = daily_returns.mean()
+    downside_returns = daily_returns[daily_returns < 0]
+    down_stdev = downside_returns.std(ddof=0)
+    return _calculate_annualized_ratio(expected_returns_mean, down_stdev)
 
 
 def calculate_sharpe(
@@ -384,14 +467,67 @@ def calculate_sharpe(
     expected_returns_mean = total_profit.sum() / days_period
     up_stdev = np.std(total_profit)
 
-    if up_stdev != 0:
-        sharp_ratio = expected_returns_mean / up_stdev * np.sqrt(365)
-    else:
-        # Define high (negative) sharpe ratio to be clear that this is NOT optimal.
-        sharp_ratio = -100
+    return _calculate_annualized_ratio(expected_returns_mean, up_stdev)
 
-    # print(expected_returns_mean, up_stdev, sharp_ratio)
-    return sharp_ratio
+
+def calculate_sharpe_from_balance(
+    balance_history: pd.DataFrame,
+    date_col: str = "date",
+    balance_col: str = "total_quote",
+) -> float:
+    """
+    Calculate sharpe ratio from historical balance snapshots.
+
+    :param balance_history: DataFrame containing at least date and balance columns
+    :param date_col: Column containing timestamps
+    :param balance_col: Column containing historical balance values
+    :return: sharpe
+    """
+    daily_returns = _calculate_daily_returns_from_balance(balance_history, date_col, balance_col)
+
+    if len(daily_returns) == 0:
+        return 0.0
+
+    expected_returns_mean = daily_returns.mean()
+    up_stdev = daily_returns.std(ddof=0)
+    return _calculate_annualized_ratio(expected_returns_mean, up_stdev)
+
+
+def calculate_max_drawdown_from_balance(
+    balance_history: pd.DataFrame,
+    date_col: str = "date",
+    balance_col: str = "total_quote",
+    relative: bool = False,
+) -> DrawDownResult:
+    """
+    Calculate max drawdown from historical balance snapshots.
+
+    :param balance_history: DataFrame containing at least date and balance columns
+    :param date_col: Column containing timestamps
+    :param balance_col: Column containing historical balance values
+    :param relative: If True, use relative drawdown for max calculation instead of absolute
+    :return: DrawDownResult object
+    :raise: ValueError if balance-history dataframe was found empty.
+    """
+    wallet = _prepare_balance_history(
+        balance_history=balance_history,
+        date_col=date_col,
+        balance_col=balance_col,
+    )
+
+    if len(wallet) < 2:
+        raise ValueError("Balance-history dataframe empty.")
+
+    starting_balance = float(wallet[balance_col].iloc[0])
+    wallet.loc[:, "total_balance"] = wallet[balance_col].diff().fillna(0.0)
+
+    return calculate_max_drawdown(
+        wallet,
+        date_col=date_col,
+        value_col="total_balance",
+        starting_balance=starting_balance,
+        relative=relative,
+    )
 
 
 def calculate_calmar(
@@ -401,12 +537,12 @@ def calculate_calmar(
     starting_balance: float,
 ) -> float:
     """
-    Calculate calmar
+    Calculate calmar from trades data.
     :param trades: DataFrame containing trades (requires columns close_date and profit_abs)
     :return: calmar
     """
     if (len(trades) == 0) or (min_date is None) or (max_date is None) or (min_date == max_date):
-        return 0
+        return 0.0
 
     total_profit = trades["profit_abs"].sum() / starting_balance
     days_period = max(1, (max_date - min_date).days)
@@ -422,16 +558,51 @@ def calculate_calmar(
         )
         max_drawdown = drawdown.relative_account_drawdown
     except ValueError:
-        max_drawdown = 0
+        return 0.0
 
-    if max_drawdown != 0:
-        calmar_ratio = expected_returns_mean / max_drawdown * math.sqrt(365)
-    else:
-        # Define high (negative) calmar ratio to be clear that this is NOT optimal.
-        calmar_ratio = -100
+    return _calculate_annualized_ratio(expected_returns_mean, max_drawdown)
 
-    # print(expected_returns_mean, max_drawdown, calmar_ratio)
-    return calmar_ratio
+
+def calculate_calmar_from_balance(
+    balance_history: pd.DataFrame,
+    date_col: str = "date",
+    balance_col: str = "total_quote",
+) -> float:
+    """
+    Calculate calmar ratio from historical balance snapshots.
+
+    :param balance_history: DataFrame containing at least date and balance columns
+    :param date_col: Column containing timestamps
+    :param balance_col: Column containing historical balance values
+    :return: calmar
+    """
+    wallet = _prepare_balance_history(
+        balance_history=balance_history,
+        date_col=date_col,
+        balance_col=balance_col,
+    )
+
+    if len(wallet) < 2:
+        return 0.0
+
+    starting_balance = float(wallet[balance_col].iloc[0])
+    final_balance = float(wallet[balance_col].iloc[-1])
+    days_period = max(1, (wallet[date_col].iloc[-1] - wallet[date_col].iloc[0]).days)
+
+    total_profit = (final_balance - starting_balance) / starting_balance
+    expected_returns_mean = total_profit / days_period * 100
+
+    try:
+        drawdown = calculate_max_drawdown_from_balance(
+            wallet,
+            date_col=date_col,
+            balance_col=balance_col,
+        )
+        max_drawdown = drawdown.relative_account_drawdown
+    except ValueError:
+        return 0.0
+
+    return _calculate_annualized_ratio(expected_returns_mean, max_drawdown)
 
 
 def calculate_sqn(trades: pd.DataFrame, starting_balance: float) -> float:
diff --git a/freqtrade/exchange/binance_leverage_tiers.json b/freqtrade/exchange/binance_leverage_tiers.json
index 58f8b3286..09b8e5791 100644
--- a/freqtrade/exchange/binance_leverage_tiers.json
+++ b/freqtrade/exchange/binance_leverage_tiers.json
@@ -5,13 +5,13 @@
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
       "minNotional": 0.0,
-      "maxNotional": 20000.0,
+      "maxNotional": 5000.0,
       "maintenanceMarginRate": 0.01,
       "maxLeverage": 75.0,
       "info": {
         "bracket": 1,
         "initialLeverage": 75,
-        "notionalCap": 20000,
+        "notionalCap": 5000,
         "notionalFloor": 0,
         "maintMarginRatio": 0.01,
         "cum": 0.0
@@ -21,136 +21,170 @@
       "tier": 2.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 20000.0,
-      "maxNotional": 200000.0,
+      "minNotional": 5000.0,
+      "maxNotional": 10000.0,
       "maintenanceMarginRate": 0.015,
       "maxLeverage": 50.0,
       "info": {
         "bracket": 2,
         "initialLeverage": 50,
-        "notionalCap": 200000,
-        "notionalFloor": 20000,
+        "notionalCap": 10000,
+        "notionalFloor": 5000,
         "maintMarginRatio": 0.015,
-        "cum": 100.0
+        "cum": 25.0
       }
     },
     {
       "tier": 3.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 200000.0,
-      "maxNotional": 1000000.0,
+      "minNotional": 10000.0,
+      "maxNotional": 50000.0,
       "maintenanceMarginRate": 0.02,
       "maxLeverage": 25.0,
       "info": {
         "bracket": 3,
         "initialLeverage": 25,
-        "notionalCap": 1000000,
-        "notionalFloor": 200000,
+        "notionalCap": 50000,
+        "notionalFloor": 10000,
         "maintMarginRatio": 0.02,
-        "cum": 1100.0
+        "cum": 75.0
       }
     },
     {
       "tier": 4.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 1000000.0,
-      "maxNotional": 2000000.0,
+      "minNotional": 50000.0,
+      "maxNotional": 100000.0,
       "maintenanceMarginRate": 0.025,
       "maxLeverage": 20.0,
       "info": {
         "bracket": 4,
         "initialLeverage": 20,
-        "notionalCap": 2000000,
-        "notionalFloor": 1000000,
+        "notionalCap": 100000,
+        "notionalFloor": 50000,
         "maintMarginRatio": 0.025,
-        "cum": 6100.0
+        "cum": 325.0
       }
     },
     {
       "tier": 5.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 2000000.0,
-      "maxNotional": 5000000.0,
-      "maintenanceMarginRate": 0.05,
-      "maxLeverage": 10.0,
+      "minNotional": 100000.0,
+      "maxNotional": 175000.0,
+      "maintenanceMarginRate": 0.0333,
+      "maxLeverage": 15.0,
       "info": {
         "bracket": 5,
-        "initialLeverage": 10,
-        "notionalCap": 5000000,
-        "notionalFloor": 2000000,
-        "maintMarginRatio": 0.05,
-        "cum": 56100.0
+        "initialLeverage": 15,
+        "notionalCap": 175000,
+        "notionalFloor": 100000,
+        "maintMarginRatio": 0.0333,
+        "cum": 1155.0
       }
     },
     {
       "tier": 6.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 5000000.0,
-      "maxNotional": 7500000.0,
-      "maintenanceMarginRate": 0.1,
-      "maxLeverage": 5.0,
+      "minNotional": 175000.0,
+      "maxNotional": 250000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
       "info": {
         "bracket": 6,
-        "initialLeverage": 5,
-        "notionalCap": 7500000,
-        "notionalFloor": 5000000,
-        "maintMarginRatio": 0.1,
-        "cum": 306100.0
+        "initialLeverage": 10,
+        "notionalCap": 250000,
+        "notionalFloor": 175000,
+        "maintMarginRatio": 0.05,
+        "cum": 4077.5
       }
     },
     {
       "tier": 7.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 7500000.0,
-      "maxNotional": 10000000.0,
-      "maintenanceMarginRate": 0.125,
-      "maxLeverage": 4.0,
+      "minNotional": 250000.0,
+      "maxNotional": 750000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
       "info": {
         "bracket": 7,
-        "initialLeverage": 4,
-        "notionalCap": 10000000,
-        "notionalFloor": 7500000,
-        "maintMarginRatio": 0.125,
-        "cum": 493600.0
+        "initialLeverage": 5,
+        "notionalCap": 750000,
+        "notionalFloor": 250000,
+        "maintMarginRatio": 0.1,
+        "cum": 16577.5
       }
     },
     {
       "tier": 8.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 10000000.0,
-      "maxNotional": 12500000.0,
-      "maintenanceMarginRate": 0.25,
-      "maxLeverage": 2.0,
+      "minNotional": 750000.0,
+      "maxNotional": 1500000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
       "info": {
         "bracket": 8,
-        "initialLeverage": 2,
-        "notionalCap": 12500000,
-        "notionalFloor": 10000000,
-        "maintMarginRatio": 0.25,
-        "cum": 1743600.0
+        "initialLeverage": 4,
+        "notionalCap": 1500000,
+        "notionalFloor": 750000,
+        "maintMarginRatio": 0.125,
+        "cum": 35327.5
       }
     },
     {
       "tier": 9.0,
       "symbol": "0G/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 12500000.0,
-      "maxNotional": 15000000.0,
+      "minNotional": 1500000.0,
+      "maxNotional": 4500000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": 9,
+        "initialLeverage": 3,
+        "notionalCap": 4500000,
+        "notionalFloor": 1500000,
+        "maintMarginRatio": 0.1667,
+        "cum": 97877.5
+      }
+    },
+    {
+      "tier": 10.0,
+      "symbol": "0G/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 4500000.0,
+      "maxNotional": 7500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
+      "info": {
+        "bracket": 10,
+        "initialLeverage": 2,
+        "notionalCap": 7500000,
+        "notionalFloor": 4500000,
+        "maintMarginRatio": 0.25,
+        "cum": 472727.5
+      }
+    },
+    {
+      "tier": 11.0,
+      "symbol": "0G/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 7500000.0,
+      "maxNotional": 12500000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
-        "bracket": 9,
+        "bracket": 11,
         "initialLeverage": 1,
-        "notionalCap": 15000000,
-        "notionalFloor": 12500000,
+        "notionalCap": 12500000,
+        "notionalFloor": 7500000,
         "maintMarginRatio": 0.5,
-        "cum": 4868600.0
+        "cum": 2347727.5
       }
     }
   ],
@@ -10126,15 +10160,15 @@
       "symbol": "ARIA/USDT:USDT",
       "currency": "USDT",
       "minNotional": 0.0,
-      "maxNotional": 5000.0,
-      "maintenanceMarginRate": 0.015,
-      "maxLeverage": 50.0,
+      "maxNotional": 6000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
       "info": {
         "bracket": 1,
-        "initialLeverage": 50,
-        "notionalCap": 5000,
+        "initialLeverage": 10,
+        "notionalCap": 6000,
         "notionalFloor": 0,
-        "maintMarginRatio": 0.015,
+        "maintMarginRatio": 0.05,
         "cum": 0.0
       }
     },
@@ -10142,119 +10176,85 @@
       "tier": 2.0,
       "symbol": "ARIA/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 5000.0,
-      "maxNotional": 10000.0,
-      "maintenanceMarginRate": 0.025,
-      "maxLeverage": 20.0,
+      "minNotional": 6000.0,
+      "maxNotional": 50000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
       "info": {
         "bracket": 2,
-        "initialLeverage": 20,
-        "notionalCap": 10000,
-        "notionalFloor": 5000,
-        "maintMarginRatio": 0.025,
-        "cum": 50.0
+        "initialLeverage": 5,
+        "notionalCap": 50000,
+        "notionalFloor": 6000,
+        "maintMarginRatio": 0.1,
+        "cum": 300.0
       }
     },
     {
       "tier": 3.0,
       "symbol": "ARIA/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 10000.0,
-      "maxNotional": 20000.0,
-      "maintenanceMarginRate": 0.05,
-      "maxLeverage": 10.0,
+      "minNotional": 50000.0,
+      "maxNotional": 100000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
       "info": {
         "bracket": 3,
-        "initialLeverage": 10,
-        "notionalCap": 20000,
-        "notionalFloor": 10000,
-        "maintMarginRatio": 0.05,
-        "cum": 300.0
+        "initialLeverage": 4,
+        "notionalCap": 100000,
+        "notionalFloor": 50000,
+        "maintMarginRatio": 0.125,
+        "cum": 1550.0
       }
     },
     {
       "tier": 4.0,
       "symbol": "ARIA/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 20000.0,
-      "maxNotional": 50000.0,
-      "maintenanceMarginRate": 0.1,
-      "maxLeverage": 5.0,
+      "minNotional": 100000.0,
+      "maxNotional": 300000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
       "info": {
         "bracket": 4,
-        "initialLeverage": 5,
-        "notionalCap": 50000,
-        "notionalFloor": 20000,
-        "maintMarginRatio": 0.1,
-        "cum": 1300.0
+        "initialLeverage": 3,
+        "notionalCap": 300000,
+        "notionalFloor": 100000,
+        "maintMarginRatio": 0.1667,
+        "cum": 5720.0
       }
     },
     {
       "tier": 5.0,
       "symbol": "ARIA/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 50000.0,
-      "maxNotional": 250000.0,
-      "maintenanceMarginRate": 0.125,
-      "maxLeverage": 4.0,
+      "minNotional": 300000.0,
+      "maxNotional": 1000000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
       "info": {
         "bracket": 5,
-        "initialLeverage": 4,
-        "notionalCap": 250000,
-        "notionalFloor": 50000,
-        "maintMarginRatio": 0.125,
-        "cum": 2550.0
+        "initialLeverage": 2,
+        "notionalCap": 1000000,
+        "notionalFloor": 300000,
+        "maintMarginRatio": 0.25,
+        "cum": 30710.0
       }
     },
     {
       "tier": 6.0,
       "symbol": "ARIA/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 250000.0,
-      "maxNotional": 500000.0,
-      "maintenanceMarginRate": 0.1667,
-      "maxLeverage": 3.0,
-      "info": {
-        "bracket": 6,
-        "initialLeverage": 3,
-        "notionalCap": 500000,
-        "notionalFloor": 250000,
-        "maintMarginRatio": 0.1667,
-        "cum": 12975.0
-      }
-    },
-    {
-      "tier": 7.0,
-      "symbol": "ARIA/USDT:USDT",
-      "currency": "USDT",
-      "minNotional": 500000.0,
-      "maxNotional": 7500000.0,
-      "maintenanceMarginRate": 0.25,
-      "maxLeverage": 2.0,
-      "info": {
-        "bracket": 7,
-        "initialLeverage": 2,
-        "notionalCap": 7500000,
-        "notionalFloor": 500000,
-        "maintMarginRatio": 0.25,
-        "cum": 54625.0
-      }
-    },
-    {
-      "tier": 8.0,
-      "symbol": "ARIA/USDT:USDT",
-      "currency": "USDT",
-      "minNotional": 7500000.0,
-      "maxNotional": 12500000.0,
+      "minNotional": 1000000.0,
+      "maxNotional": 2000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
-        "bracket": 8,
+        "bracket": 6,
         "initialLeverage": 1,
-        "notionalCap": 12500000,
-        "notionalFloor": 7500000,
+        "notionalCap": 2000000,
+        "notionalFloor": 1000000,
         "maintMarginRatio": 0.5,
-        "cum": 1929625.0
+        "cum": 280710.0
       }
     }
   ],
@@ -11402,15 +11402,15 @@
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
       "minNotional": 0.0,
-      "maxNotional": 5000.0,
-      "maintenanceMarginRate": 0.01,
-      "maxLeverage": 75.0,
+      "maxNotional": 10000.0,
+      "maintenanceMarginRate": 0.015,
+      "maxLeverage": 50.0,
       "info": {
         "bracket": 1,
-        "initialLeverage": 75,
-        "notionalCap": 5000,
+        "initialLeverage": 50,
+        "notionalCap": 10000,
         "notionalFloor": 0,
-        "maintMarginRatio": 0.01,
+        "maintMarginRatio": 0.015,
         "cum": 0.0
       }
     },
@@ -11418,38 +11418,21 @@
       "tier": 2.0,
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 5000.0,
-      "maxNotional": 10000.0,
-      "maintenanceMarginRate": 0.015,
-      "maxLeverage": 50.0,
-      "info": {
-        "bracket": 2,
-        "initialLeverage": 50,
-        "notionalCap": 10000,
-        "notionalFloor": 5000,
-        "maintMarginRatio": 0.015,
-        "cum": 25.0
-      }
-    },
-    {
-      "tier": 3.0,
-      "symbol": "ATH/USDT:USDT",
-      "currency": "USDT",
       "minNotional": 10000.0,
       "maxNotional": 50000.0,
       "maintenanceMarginRate": 0.02,
       "maxLeverage": 25.0,
       "info": {
-        "bracket": 3,
+        "bracket": 2,
         "initialLeverage": 25,
         "notionalCap": 50000,
         "notionalFloor": 10000,
         "maintMarginRatio": 0.02,
-        "cum": 75.0
+        "cum": 50.0
       }
     },
     {
-      "tier": 4.0,
+      "tier": 3.0,
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
       "minNotional": 50000.0,
@@ -11457,50 +11440,50 @@
       "maintenanceMarginRate": 0.025,
       "maxLeverage": 20.0,
       "info": {
-        "bracket": 4,
+        "bracket": 3,
         "initialLeverage": 20,
         "notionalCap": 100000,
         "notionalFloor": 50000,
         "maintMarginRatio": 0.025,
-        "cum": 325.0
+        "cum": 300.0
+      }
+    },
+    {
+      "tier": 4.0,
+      "symbol": "ATH/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 100000.0,
+      "maxNotional": 125000.0,
+      "maintenanceMarginRate": 0.0333,
+      "maxLeverage": 15.0,
+      "info": {
+        "bracket": 4,
+        "initialLeverage": 15,
+        "notionalCap": 125000,
+        "notionalFloor": 100000,
+        "maintMarginRatio": 0.0333,
+        "cum": 1130.0
       }
     },
     {
       "tier": 5.0,
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 100000.0,
-      "maxNotional": 175000.0,
-      "maintenanceMarginRate": 0.0333,
-      "maxLeverage": 15.0,
-      "info": {
-        "bracket": 5,
-        "initialLeverage": 15,
-        "notionalCap": 175000,
-        "notionalFloor": 100000,
-        "maintMarginRatio": 0.0333,
-        "cum": 1155.0
-      }
-    },
-    {
-      "tier": 6.0,
-      "symbol": "ATH/USDT:USDT",
-      "currency": "USDT",
-      "minNotional": 175000.0,
+      "minNotional": 125000.0,
       "maxNotional": 250000.0,
       "maintenanceMarginRate": 0.05,
       "maxLeverage": 10.0,
       "info": {
-        "bracket": 6,
+        "bracket": 5,
         "initialLeverage": 10,
         "notionalCap": 250000,
-        "notionalFloor": 175000,
+        "notionalFloor": 125000,
         "maintMarginRatio": 0.05,
-        "cum": 4077.5
+        "cum": 3217.5
       }
     },
     {
-      "tier": 7.0,
+      "tier": 6.0,
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
       "minNotional": 250000.0,
@@ -11508,16 +11491,16 @@
       "maintenanceMarginRate": 0.1,
       "maxLeverage": 5.0,
       "info": {
-        "bracket": 7,
+        "bracket": 6,
         "initialLeverage": 5,
         "notionalCap": 750000,
         "notionalFloor": 250000,
         "maintMarginRatio": 0.1,
-        "cum": 16577.5
+        "cum": 15717.5
       }
     },
     {
-      "tier": 8.0,
+      "tier": 7.0,
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
       "minNotional": 750000.0,
@@ -11525,63 +11508,63 @@
       "maintenanceMarginRate": 0.125,
       "maxLeverage": 4.0,
       "info": {
-        "bracket": 8,
+        "bracket": 7,
         "initialLeverage": 4,
         "notionalCap": 1500000,
         "notionalFloor": 750000,
         "maintMarginRatio": 0.125,
-        "cum": 35327.5
+        "cum": 34467.5
+      }
+    },
+    {
+      "tier": 8.0,
+      "symbol": "ATH/USDT:USDT",
+      "currency": "USDT",
+      "minNotional": 1500000.0,
+      "maxNotional": 2500000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
+      "info": {
+        "bracket": 8,
+        "initialLeverage": 3,
+        "notionalCap": 2500000,
+        "notionalFloor": 1500000,
+        "maintMarginRatio": 0.1667,
+        "cum": 97017.5
       }
     },
     {
       "tier": 9.0,
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 1500000.0,
-      "maxNotional": 4500000.0,
-      "maintenanceMarginRate": 0.1667,
-      "maxLeverage": 3.0,
+      "minNotional": 2500000.0,
+      "maxNotional": 5000000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
       "info": {
         "bracket": 9,
-        "initialLeverage": 3,
-        "notionalCap": 4500000,
-        "notionalFloor": 1500000,
-        "maintMarginRatio": 0.1667,
-        "cum": 97877.5
+        "initialLeverage": 2,
+        "notionalCap": 5000000,
+        "notionalFloor": 2500000,
+        "maintMarginRatio": 0.25,
+        "cum": 305267.5
       }
     },
     {
       "tier": 10.0,
       "symbol": "ATH/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 4500000.0,
+      "minNotional": 5000000.0,
       "maxNotional": 7500000.0,
-      "maintenanceMarginRate": 0.25,
-      "maxLeverage": 2.0,
-      "info": {
-        "bracket": 10,
-        "initialLeverage": 2,
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@@ -16605,51 +16588,51 @@
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@@ -16729,12 +16695,12 @@
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@@ -17381,15 +17347,15 @@
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@@ -17397,51 +17363,51 @@
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@@ -17521,12 +17453,12 @@
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@@ -33434,15 +33366,15 @@
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@@ -33450,51 +33382,51 @@
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@@ -33574,12 +33472,12 @@
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@@ -36500,13 +36398,13 @@
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@@ -36516,136 +36414,170 @@
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   ],
@@ -40065,6 +39997,127 @@
       }
     }
   ],
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+  ],
   "GHST/USDT:USDT": [
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@@ -49810,13 +49863,13 @@
       "symbol": "KAITO/USDT:USDT",
       "currency": "USDT",
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@@ -49826,51 +49879,51 @@
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@@ -49878,71 +49931,54 @@
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@@ -49950,12 +49986,12 @@
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@@ -52742,15 +52778,15 @@
       "symbol": "LINEA/USDT:USDT",
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@@ -52758,170 +52794,136 @@
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@@ -59482,14 +59484,14 @@
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@@ -59638,12 +59606,12 @@
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@@ -62740,13 +62708,13 @@
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@@ -62756,51 +62724,51 @@
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@@ -62880,12 +62831,12 @@
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@@ -71101,13 +71052,13 @@
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@@ -71117,51 +71068,51 @@
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@@ -71241,12 +71175,12 @@
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@@ -80105,12 +80022,12 @@
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@@ -81448,14 +81365,14 @@
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@@ -81465,14 +81382,14 @@
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@@ -81481,15 +81398,15 @@
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@@ -81621,12 +81504,12 @@
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@@ -84600,13 +84483,13 @@
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@@ -84616,51 +84499,51 @@
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@@ -84740,12 +84606,12 @@
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@@ -88141,63 +87990,63 @@
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@@ -88381,12 +88196,12 @@
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@@ -94121,136 +93936,136 @@
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@@ -95172,136 +94970,170 @@
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@@ -96775,14 +96607,14 @@
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@@ -97911,136 +97777,170 @@
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@@ -100270,170 +100170,170 @@
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+      "minNotional": 750000.0,
+      "maxNotional": 1500000.0,
       "maintenanceMarginRate": 0.125,
       "maxLeverage": 4.0,
       "info": {
         "bracket": 8,
         "initialLeverage": 4,
-        "notionalCap": 4500000,
-        "notionalFloor": 3000000,
+        "notionalCap": 1500000,
+        "notionalFloor": 750000,
         "maintMarginRatio": 0.125,
-        "cum": 120492.5
+        "cum": 35327.5
       }
     },
     {
       "tier": 9.0,
       "symbol": "ZEN/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 4500000.0,
-      "maxNotional": 7500000.0,
+      "minNotional": 1500000.0,
+      "maxNotional": 4500000.0,
       "maintenanceMarginRate": 0.1667,
       "maxLeverage": 3.0,
       "info": {
         "bracket": 9,
         "initialLeverage": 3,
-        "notionalCap": 7500000,
-        "notionalFloor": 4500000,
+        "notionalCap": 4500000,
+        "notionalFloor": 1500000,
         "maintMarginRatio": 0.1667,
-        "cum": 308142.5
+        "cum": 97877.5
       }
     },
     {
       "tier": 10.0,
       "symbol": "ZEN/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 7500000.0,
-      "maxNotional": 12000000.0,
+      "minNotional": 4500000.0,
+      "maxNotional": 7500000.0,
       "maintenanceMarginRate": 0.25,
       "maxLeverage": 2.0,
       "info": {
         "bracket": 10,
         "initialLeverage": 2,
-        "notionalCap": 12000000,
-        "notionalFloor": 7500000,
+        "notionalCap": 7500000,
+        "notionalFloor": 4500000,
         "maintMarginRatio": 0.25,
-        "cum": 932892.5
+        "cum": 472727.5
       }
     },
     {
       "tier": 11.0,
       "symbol": "ZEN/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 12000000.0,
-      "maxNotional": 18000000.0,
+      "minNotional": 7500000.0,
+      "maxNotional": 12500000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
         "bracket": 11,
         "initialLeverage": 1,
-        "notionalCap": 18000000,
-        "notionalFloor": 12000000,
+        "notionalCap": 12500000,
+        "notionalFloor": 7500000,
         "maintMarginRatio": 0.5,
-        "cum": 3932892.5
+        "cum": 2347727.5
       }
     }
   ],
@@ -102081,14 +101981,14 @@
       "currency": "USDT",
       "minNotional": 0.0,
       "maxNotional": 5000.0,
-      "maintenanceMarginRate": 0.015,
-      "maxLeverage": 50.0,
+      "maintenanceMarginRate": 0.04,
+      "maxLeverage": 20.0,
       "info": {
         "bracket": 1,
-        "initialLeverage": 50,
+        "initialLeverage": 20,
         "notionalCap": 5000,
         "notionalFloor": 0,
-        "maintMarginRatio": 0.015,
+        "maintMarginRatio": 0.04,
         "cum": 0.0
       }
     },
@@ -102097,135 +101997,101 @@
       "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
       "currency": "USDT",
       "minNotional": 5000.0,
-      "maxNotional": 10000.0,
-      "maintenanceMarginRate": 0.02,
-      "maxLeverage": 25.0,
+      "maxNotional": 15000.0,
+      "maintenanceMarginRate": 0.05,
+      "maxLeverage": 10.0,
       "info": {
         "bracket": 2,
-        "initialLeverage": 25,
-        "notionalCap": 10000,
+        "initialLeverage": 10,
+        "notionalCap": 15000,
         "notionalFloor": 5000,
-        "maintMarginRatio": 0.02,
-        "cum": 25.0
+        "maintMarginRatio": 0.05,
+        "cum": 50.0
       }
     },
     {
       "tier": 3.0,
       "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 10000.0,
-      "maxNotional": 25000.0,
-      "maintenanceMarginRate": 0.025,
-      "maxLeverage": 20.0,
+      "minNotional": 15000.0,
+      "maxNotional": 60000.0,
+      "maintenanceMarginRate": 0.1,
+      "maxLeverage": 5.0,
       "info": {
         "bracket": 3,
-        "initialLeverage": 20,
-        "notionalCap": 25000,
-        "notionalFloor": 10000,
-        "maintMarginRatio": 0.025,
-        "cum": 75.0
+        "initialLeverage": 5,
+        "notionalCap": 60000,
+        "notionalFloor": 15000,
+        "maintMarginRatio": 0.1,
+        "cum": 800.0
       }
     },
     {
       "tier": 4.0,
       "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 25000.0,
-      "maxNotional": 62500.0,
-      "maintenanceMarginRate": 0.05,
-      "maxLeverage": 10.0,
+      "minNotional": 60000.0,
+      "maxNotional": 200000.0,
+      "maintenanceMarginRate": 0.125,
+      "maxLeverage": 4.0,
       "info": {
         "bracket": 4,
-        "initialLeverage": 10,
-        "notionalCap": 62500,
-        "notionalFloor": 25000,
-        "maintMarginRatio": 0.05,
-        "cum": 700.0
+        "initialLeverage": 4,
+        "notionalCap": 200000,
+        "notionalFloor": 60000,
+        "maintMarginRatio": 0.125,
+        "cum": 2300.0
       }
     },
     {
       "tier": 5.0,
       "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 62500.0,
-      "maxNotional": 125000.0,
-      "maintenanceMarginRate": 0.1,
-      "maxLeverage": 5.0,
+      "minNotional": 200000.0,
+      "maxNotional": 500000.0,
+      "maintenanceMarginRate": 0.1667,
+      "maxLeverage": 3.0,
       "info": {
         "bracket": 5,
-        "initialLeverage": 5,
-        "notionalCap": 125000,
-        "notionalFloor": 62500,
-        "maintMarginRatio": 0.1,
-        "cum": 3825.0
+        "initialLeverage": 3,
+        "notionalCap": 500000,
+        "notionalFloor": 200000,
+        "maintMarginRatio": 0.1667,
+        "cum": 10640.0
       }
     },
     {
       "tier": 6.0,
       "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 125000.0,
-      "maxNotional": 250000.0,
-      "maintenanceMarginRate": 0.125,
-      "maxLeverage": 4.0,
+      "minNotional": 500000.0,
+      "maxNotional": 2500000.0,
+      "maintenanceMarginRate": 0.25,
+      "maxLeverage": 2.0,
       "info": {
         "bracket": 6,
-        "initialLeverage": 4,
-        "notionalCap": 250000,
-        "notionalFloor": 125000,
-        "maintMarginRatio": 0.125,
-        "cum": 6950.0
+        "initialLeverage": 2,
+        "notionalCap": 2500000,
+        "notionalFloor": 500000,
+        "maintMarginRatio": 0.25,
+        "cum": 52290.0
       }
     },
     {
       "tier": 7.0,
       "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
       "currency": "USDT",
-      "minNotional": 250000.0,
-      "maxNotional": 500000.0,
-      "maintenanceMarginRate": 0.1667,
-      "maxLeverage": 3.0,
-      "info": {
-        "bracket": 7,
-        "initialLeverage": 3,
-        "notionalCap": 500000,
-        "notionalFloor": 250000,
-        "maintMarginRatio": 0.1667,
-        "cum": 17375.0
-      }
-    },
-    {
-      "tier": 8.0,
-      "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
-      "currency": "USDT",
-      "minNotional": 500000.0,
-      "maxNotional": 7500000.0,
-      "maintenanceMarginRate": 0.25,
-      "maxLeverage": 2.0,
-      "info": {
-        "bracket": 8,
-        "initialLeverage": 2,
-        "notionalCap": 7500000,
-        "notionalFloor": 500000,
-        "maintMarginRatio": 0.25,
-        "cum": 59025.0
-      }
-    },
-    {
-      "tier": 9.0,
-      "symbol": "\u5e01\u5b89\u4eba\u751f/USDT:USDT",
-      "currency": "USDT",
-      "minNotional": 7500000.0,
-      "maxNotional": 12500000.0,
+      "minNotional": 2500000.0,
+      "maxNotional": 5000000.0,
       "maintenanceMarginRate": 0.5,
       "maxLeverage": 1.0,
       "info": {
-        "bracket": 9,
+        "bracket": 7,
         "initialLeverage": 1,
-        "notionalCap": 12500000,
-        "notionalFloor": 7500000,
+        "notionalCap": 5000000,
+        "notionalFloor": 2500000,
         "maintMarginRatio": 0.5,
-        "cum": 1934025.0
+        "cum": 677290.0
       }
     }
   ],
diff --git a/freqtrade/exchange/bitget.py b/freqtrade/exchange/bitget.py
index c769fc0c8..3be721cff 100644
--- a/freqtrade/exchange/bitget.py
+++ b/freqtrade/exchange/bitget.py
@@ -101,30 +101,36 @@ class Bitget(Exchange):
         return order
 
     def _fetch_stop_order_fallback(self, order_id: str, pair: str) -> CcxtOrder:
-        params2 = {
-            "stop": True,
-        }
-        for method in (
-            self._api.fetch_open_orders,
-            self._api.fetch_canceled_and_closed_orders,
-        ):
-            try:
-                orders = method(pair, params=params2)
-                orders_f = [order for order in orders if order["id"] == order_id]
-                if orders_f:
-                    order = orders_f[0]
-                    self._log_exchange_response("get_stop_order_fallback", order)
-                    return self._convert_stop_order(pair, order_id, order)
-            except (ccxt.OrderNotFound, ccxt.InvalidOrder):
-                pass
-            except ccxt.DDoSProtection as e:
-                raise DDosProtection(e) from e
-            except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
-                raise TemporaryError(
-                    f"Could not get order due to {e.__class__.__name__}. Message: {e}"
-                ) from e
-            except ccxt.BaseError as e:
-                raise OperationalException(e) from e
+        # old stoploss orders
+        paramsold = {"stop": True}
+        # new stoploss orders with stopLossPrice (used in futures starting 2026.4)
+        paramsnew = {"planType": "profit_loss"}
+        params_to_try = (
+            (paramsnew, paramsold) if self.trading_mode == TradingMode.FUTURES else (paramsold,)
+        )
+
+        for params2 in params_to_try:
+            for method in (
+                self._api.fetch_open_orders,
+                self._api.fetch_canceled_and_closed_orders,
+            ):
+                try:
+                    orders = method(pair, params=params2)
+                    orders_f = [order for order in orders if order["id"] == order_id]
+                    if orders_f:
+                        order = orders_f[0]
+                        self._log_exchange_response("get_stop_order_fallback", order)
+                        return self._convert_stop_order(pair, order_id, order)
+                except (ccxt.OrderNotFound, ccxt.InvalidOrder):
+                    pass
+                except ccxt.DDoSProtection as e:
+                    raise DDosProtection(e) from e
+                except (ccxt.OperationFailed, ccxt.ExchangeError) as e:
+                    raise TemporaryError(
+                        f"Could not get order due to {e.__class__.__name__}. Message: {e}"
+                    ) from e
+                except ccxt.BaseError as e:
+                    raise OperationalException(e) from e
         raise RetryableOrderError(f"StoplossOrder not found (pair: {pair} id: {order_id}).")
 
     @retrier(retries=API_RETRY_COUNT)
diff --git a/freqtrade/freqai/data_drawer.py b/freqtrade/freqai/data_drawer.py
index 960c822b5..9e9381937 100644
--- a/freqtrade/freqai/data_drawer.py
+++ b/freqtrade/freqai/data_drawer.py
@@ -361,7 +361,7 @@ class FreqaiDataDrawer:
             label_loc = df.columns.get_loc(label)
             pred_label_loc = predictions.columns.get_loc(label)
             df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc]
-            if df[label].dtype == object:
+            if pd.api.types.is_string_dtype(df[label].dtype):
                 continue
             label_mean_loc = df.columns.get_loc(f"{label}_mean")
             label_std_loc = df.columns.get_loc(f"{label}_std")
diff --git a/freqtrade/freqai/data_kitchen.py b/freqtrade/freqai/data_kitchen.py
index df7c827f9..9f04e0ca4 100644
--- a/freqtrade/freqai/data_kitchen.py
+++ b/freqtrade/freqai/data_kitchen.py
@@ -435,7 +435,7 @@ class FreqaiDataKitchen:
 
         for label in predictions.columns:
             append_dict[label] = predictions[label]
-            if predictions[label].dtype == object:
+            if pd.api.types.is_string_dtype(predictions[label].dtype):
                 continue
             if "labels_mean" in self.data and label in self.data["labels_mean"]:
                 append_dict[f"{label}_mean"] = self.data["labels_mean"][label]
@@ -879,7 +879,7 @@ class FreqaiDataKitchen:
 
         self.data["labels_mean"], self.data["labels_std"] = {}, {}
         for label in self.data_dictionary["train_labels"].columns:
-            if self.data_dictionary["train_labels"][label].dtype == object:
+            if pd.api.types.is_string_dtype(self.data_dictionary["train_labels"][label].dtype):
                 continue
             f = spy.stats.norm.fit(self.data_dictionary["train_labels"][label])
             self.data["labels_mean"][label], self.data["labels_std"][label] = f[0], f[1]
@@ -905,7 +905,7 @@ class FreqaiDataKitchen:
         self.find_labels(dataframe)
 
         for key in self.label_list:
-            if dataframe[key].dtype == object:
+            if pd.api.types.is_string_dtype(dataframe[key].dtype):
                 self.unique_classes[key] = dataframe[key].dropna().unique()
 
         if self.unique_classes:
diff --git a/freqtrade/freqai/freqai_interface.py b/freqtrade/freqai/freqai_interface.py
index 1ba58d3e8..2e3e74400 100644
--- a/freqtrade/freqai/freqai_interface.py
+++ b/freqtrade/freqai/freqai_interface.py
@@ -676,7 +676,7 @@ class IFreqaiModel(ABC):
         self.set_start_dry_live_date(strat_df)
 
         for label in hist_preds_df.columns:
-            if hist_preds_df[label].dtype == object:
+            if pd.api.types.is_string_dtype(hist_preds_df[label].dtype):
                 continue
             hist_preds_df[f"{label}_mean"] = 0
             hist_preds_df[f"{label}_std"] = 0
@@ -706,7 +706,7 @@ class IFreqaiModel(ABC):
         num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
         dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
         for label in full_labels:
-            if self.dd.historic_predictions[dk.pair][label].dtype == object:
+            if pd.api.types.is_string_dtype(self.dd.historic_predictions[dk.pair][label].dtype):
                 continue
             f = spy.stats.norm.fit(self.dd.historic_predictions[dk.pair][label].tail(num_candles))
             dk.data["labels_mean"][label], dk.data["labels_std"][label] = f[0], f[1]
@@ -896,7 +896,7 @@ class IFreqaiModel(ABC):
                     ]
                     self.fit_live_predictions(self.dk, self.dk.pair)
                     for label in label_columns:
-                        if dk.full_df[label].dtype == object:
+                        if pd.api.types.is_string_dtype(dk.full_df[label].dtype):
                             continue
                         if "labels_mean" in self.dk.data:
                             dk.full_df.at[index, f"{label}_mean"] = self.dk.data["labels_mean"][
diff --git a/freqtrade/optimize/analysis/lookahead_helpers.py b/freqtrade/optimize/analysis/lookahead_helpers.py
index c9434c3d8..affa0c652 100644
--- a/freqtrade/optimize/analysis/lookahead_helpers.py
+++ b/freqtrade/optimize/analysis/lookahead_helpers.py
@@ -126,14 +126,14 @@ class LookaheadAnalysisSubFunctions:
                 csv_df = add_or_update_row(csv_df, new_row_data)
 
         # Fill NaN values with a default value (e.g., 0)
-        csv_df["total_signals"] = csv_df["total_signals"].astype(int).fillna(0)
-        csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int).fillna(0)
-        csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int).fillna(0)
+        csv_df["total_signals"] = csv_df["total_signals"].astype("int64").fillna(0)
+        csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype("int64").fillna(0)
+        csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype("int64").fillna(0)
 
         # Convert columns to integers
-        csv_df["total_signals"] = csv_df["total_signals"].astype(int)
-        csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype(int)
-        csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype(int)
+        csv_df["total_signals"] = csv_df["total_signals"].astype("int64")
+        csv_df["biased_entry_signals"] = csv_df["biased_entry_signals"].astype("int64")
+        csv_df["biased_exit_signals"] = csv_df["biased_exit_signals"].astype("int64")
 
         logger.info(f"saving {config['lookahead_analysis_exportfilename']}")
         csv_df.to_csv(config["lookahead_analysis_exportfilename"], index=False)
diff --git a/freqtrade/optimize/optimize_reports/bt_output.py b/freqtrade/optimize/optimize_reports/bt_output.py
index 23963038c..f42054c65 100644
--- a/freqtrade/optimize/optimize_reports/bt_output.py
+++ b/freqtrade/optimize/optimize_reports/bt_output.py
@@ -1,6 +1,8 @@
 import logging
 from typing import Any, Literal
 
+from rich.text import Text
+
 from freqtrade.constants import UNLIMITED_STAKE_AMOUNT, Config
 from freqtrade.ft_types import BacktestResultType
 from freqtrade.optimize.optimize_reports.optimize_reports import generate_periodic_breakdown_stats
@@ -9,6 +11,8 @@ from freqtrade.util import decimals_per_coin, fmt_coin, print_rich_table
 
 logger = logging.getLogger(__name__)
 
+__EMPTY_LINE = ("", "")
+
 
 def _get_line_floatfmt(stake_currency: str) -> list[str]:
     """
@@ -201,7 +205,7 @@ def text_table_add_metrics(strat_results: dict) -> None:
 
         short_metrics = (
             [
-                ("", ""),  # Empty line to improve readability
+                __EMPTY_LINE,  # Empty line to improve readability
                 (
                     "Long / Short trades",
                     f"{strat_results.get('trade_count_long', 'total_trades')} / "
@@ -222,7 +226,7 @@ def text_table_add_metrics(strat_results: dict) -> None:
             else []
         )
 
-        drawdown_metrics = []
+        drawdown_metrics: list[tuple[str | Text, str | Text]] = []
         if "max_relative_drawdown" in strat_results:
             # Compatibility to show old hyperopt results
             drawdown_metrics.append(
@@ -289,25 +293,77 @@ def text_table_add_metrics(strat_results: dict) -> None:
         )
         wallet_metrics: list[tuple[str, str]] = [
             (
-                "Min/Max balance realized",
+                "Min/Max balance (closed trades)",
                 f"{fmt_coin(strat_results['csum_min'], stake)} / "
                 f"{fmt_coin(strat_results['csum_max'], stake)}",
             ),
         ]
-        if wallet_stats := strat_results.get("wallet_stats"):
-            wallet_metrics.extend(
+        wallet_stats = strat_results.get("wallet_stats", {})
+        if wallet_stats:
+            drawdown_metrics.extend(
                 [
+                    __EMPTY_LINE,  # Empty line to improve readability
+                    (Text("Wallet based Metrics", style="bold"), ""),
                     (
-                        "Min/Max balance unrealized",
+                        "Min/Max balance (wallet balance)",
                         f"{fmt_coin(wallet_stats['low_balance'], stake)} / "
                         f"{fmt_coin(wallet_stats['high_balance'], stake)}",
                     ),
                     (
-                        "Min/Max balance dates",
+                        "Min/Max balance dates (wallet balance)",
                         f"{wallet_stats['low_date']} / {wallet_stats['high_date']}",
                     ),
                 ]
             )
+            if "max_drawdown_abs" in wallet_stats:
+                # Assume that if sharpe is there, all others are there as well.
+                drawdown_metrics.extend(
+                    [
+                        (
+                            "Max % of account underwater (balance)",
+                            f"{wallet_stats['max_relative_drawdown']:.2%}",
+                        ),
+                        (
+                            "Absolute drawdown (wallet balance)",
+                            f"{fmt_coin(wallet_stats['max_drawdown_abs'], stake)} "
+                            f"({wallet_stats['max_drawdown_account']:.2%})",
+                        ),
+                        (
+                            "Drawdown duration",
+                            wallet_stats["drawdown_duration"]
+                            if "drawdown_duration" in wallet_stats
+                            else "N/A",
+                        ),
+                        (
+                            "Profit at drawdown start",
+                            fmt_coin(wallet_stats["max_drawdown_high"], stake),
+                        ),
+                        (
+                            "Profit at drawdown end",
+                            fmt_coin(wallet_stats["max_drawdown_low"], stake),
+                        ),
+                        ("Drawdown start", wallet_stats["drawdown_start"]),
+                        ("Drawdown end", wallet_stats["drawdown_end"]),
+                        (
+                            "Sharpe (daily wallet balance)",
+                            f"{wallet_stats['sharpe']:.2f}"
+                            if wallet_stats and "sharpe" in wallet_stats
+                            else "N/A",
+                        ),
+                        (
+                            "Sortino (daily wallet balance)",
+                            f"{wallet_stats['sortino']:.2f}"
+                            if wallet_stats and "sortino" in wallet_stats
+                            else "N/A",
+                        ),
+                        (
+                            "Calmar (daily wallet balance)",
+                            f"{wallet_stats['calmar']:.2f}"
+                            if wallet_stats and "calmar" in wallet_stats
+                            else "N/A",
+                        ),
+                    ]
+                )
 
         # Newly added fields should be ignored if they are missing in strat_results. hyperopt-show
         # command stores these results and newer version of freqtrade must be able to handle old
@@ -317,7 +373,7 @@ def text_table_add_metrics(strat_results: dict) -> None:
             ("Backtesting to", strat_results["backtest_end"]),
             *trading_mode,
             ("Max open trades", strat_results["max_open_trades"]),
-            ("", ""),  # Empty line to improve readability
+            __EMPTY_LINE,  # Empty line to improve readability
             (
                 "Total/Daily Avg Trades",
                 f"{strat_results['total_trades']} / {strat_results['trades_per_day']}",
@@ -336,9 +392,18 @@ def text_table_add_metrics(strat_results: dict) -> None:
             ),
             ("Total profit %", f"{strat_results['profit_total']:.2%}"),
             ("CAGR %", f"{strat_results['cagr']:.2%}" if "cagr" in strat_results else "N/A"),
-            ("Sortino", f"{strat_results['sortino']:.2f}" if "sortino" in strat_results else "N/A"),
-            ("Sharpe", f"{strat_results['sharpe']:.2f}" if "sharpe" in strat_results else "N/A"),
-            ("Calmar", f"{strat_results['calmar']:.2f}" if "calmar" in strat_results else "N/A"),
+            (
+                "Sharpe (closed trades)",
+                f"{strat_results['sharpe']:.2f}" if "sharpe" in strat_results else "N/A",
+            ),
+            (
+                "Sortino (closed trades)",
+                f"{strat_results['sortino']:.2f}" if "sortino" in strat_results else "N/A",
+            ),
+            (
+                "Calmar (closed trades)",
+                f"{strat_results['calmar']:.2f}" if "calmar" in strat_results else "N/A",
+            ),
             ("SQN", f"{strat_results['sqn']:.2f}" if "sqn" in strat_results else "N/A"),
             (
                 "Profit factor",
@@ -367,12 +432,13 @@ def text_table_add_metrics(strat_results: dict) -> None:
                 "Avg. stake amount",
                 fmt_coin(strat_results["avg_stake_amount"], stake),
             ),
+            ("Market change", f"{strat_results['market_change']:.2%}"),
             (
                 "Total trade volume",
                 fmt_coin(strat_results["total_volume"], stake),
             ),
             *short_metrics,
-            ("", ""),  # Empty line to improve readability
+            __EMPTY_LINE,  # Empty line to improve readability
             (
                 "Best Pair",
                 f"{strat_results['best_pair']['key']} "
@@ -428,10 +494,9 @@ def text_table_add_metrics(strat_results: dict) -> None:
                 f"{strat_results.get('timedout_exit_orders', 'N/A')}",
             ),
             *entry_adjustment_metrics,
-            ("", ""),  # Empty line to improve readability
+            __EMPTY_LINE,  # Empty line to improve readability
             *wallet_metrics,
             *drawdown_metrics,
-            ("Market change", f"{strat_results['market_change']:.2%}"),
         ]
         print_rich_table(metrics, ["Metric", "Value"], summary="SUMMARY METRICS", justify="left")
 
diff --git a/freqtrade/optimize/optimize_reports/optimize_reports.py b/freqtrade/optimize/optimize_reports/optimize_reports.py
index ba84d8834..15d25a19c 100644
--- a/freqtrade/optimize/optimize_reports/optimize_reports.py
+++ b/freqtrade/optimize/optimize_reports/optimize_reports.py
@@ -10,12 +10,16 @@ from freqtrade.constants import BACKTEST_BREAKDOWNS, DATETIME_PRINT_FORMAT
 from freqtrade.data.metrics import (
     calculate_cagr,
     calculate_calmar,
+    calculate_calmar_from_balance,
     calculate_csum,
     calculate_expectancy,
     calculate_market_change,
     calculate_max_drawdown,
+    calculate_max_drawdown_from_balance,
     calculate_sharpe,
+    calculate_sharpe_from_balance,
     calculate_sortino,
+    calculate_sortino_from_balance,
     calculate_sqn,
 )
 from freqtrade.ft_types import (
@@ -59,15 +63,61 @@ def generate_wallet_stats(wallet_df: DataFrame, stake_currency: str) -> dict[str
     low_balance = total_quote.loc[low_idx]
     low_date = wallet.loc[low_idx, "date"]
     high_date = wallet.loc[high_idx, "date"]
+    sharpe = calculate_sharpe_from_balance(wallet)
+    sortino = calculate_sortino_from_balance(wallet)
+    calmar = calculate_calmar_from_balance(wallet)
+    try:
+        drawdown = calculate_max_drawdown_from_balance(wallet)
+        # max_relative_drawdown = Underwater
+        drawdown_duration = drawdown.low_date - drawdown.high_date
+
+    except ValueError:
+        drawdown = None
+        drawdown_duration = timedelta()
+    try:
+        underwater = calculate_max_drawdown_from_balance(wallet, relative=True)
+    except ValueError:
+        underwater = None
     return {
         "start_balance": start_balance,
         "end_balance": end_balance,
         "high_balance": high_balance,
         "low_balance": low_balance,
+        "sharpe": sharpe,
+        "sortino": sortino,
+        "calmar": calmar,
         "low_date": low_date.strftime(DATETIME_PRINT_FORMAT),
         "low_ts": int(low_date.timestamp() * 1000),
         "high_date": high_date.strftime(DATETIME_PRINT_FORMAT),
         "high_ts": int(high_date.timestamp() * 1000),
+        # Drawdown metrics
+        "max_drawdown_account": drawdown.relative_account_drawdown if drawdown else 0.0,
+        "max_relative_drawdown": underwater.relative_account_drawdown if underwater else 0.0,
+        "max_drawdown_abs": drawdown.drawdown_abs if drawdown else 0.0,
+        "drawdown_start": (
+            drawdown.high_date.strftime(DATETIME_PRINT_FORMAT)
+            if drawdown and drawdown.high_date is not None
+            else None
+        ),
+        "drawdown_start_ts": (
+            int(drawdown.high_date.timestamp() * 1000)
+            if drawdown and drawdown.high_date is not None
+            else None
+        ),
+        "drawdown_end": (
+            drawdown.low_date.strftime(DATETIME_PRINT_FORMAT)
+            if drawdown and drawdown.low_date is not None
+            else None
+        ),
+        "drawdown_end_ts": (
+            int(drawdown.low_date.timestamp() * 1000)
+            if drawdown and drawdown.low_date is not None
+            else None
+        ),
+        "drawdown_duration": drawdown_duration,
+        "drawdown_duration_s": drawdown_duration.total_seconds(),
+        "max_drawdown_low": drawdown.low_value if drawdown else 0.0,
+        "max_drawdown_high": drawdown.high_value if drawdown else 0.0,
     }
 
 
@@ -290,7 +340,7 @@ def generate_strategy_comparison(bt_stats: dict) -> list[dict]:
 
 def _get_resample_from_period(period: str) -> str:
     if period == "day":
-        return "1d"
+        return "1D"
     if period == "week":
         # Weekly defaulting to Monday.
         return "1W-MON"
@@ -480,8 +530,8 @@ def generate_daily_stats(results: DataFrame) -> dict[str, Any]:
             "losing_days": 0,
             "daily_profit_list": [],
         }
-    daily_profit_rel = results.resample("1d", on="close_date")["profit_ratio"].sum()
-    daily_profit = results.resample("1d", on="close_date")["profit_abs"].sum().round(10)
+    daily_profit_rel = results.resample("1D", on="close_date")["profit_ratio"].sum()
+    daily_profit = results.resample("1D", on="close_date")["profit_abs"].sum().round(10)
     worst_rel = min(daily_profit_rel)
     best_rel = max(daily_profit_rel)
     worst = min(daily_profit)
diff --git a/freqtrade/plot/plotting.py b/freqtrade/plot/plotting.py
index 085a198ca..ed15c8e79 100644
--- a/freqtrade/plot/plotting.py
+++ b/freqtrade/plot/plotting.py
@@ -263,7 +263,7 @@ def plot_trades(fig, trades: pd.DataFrame) -> make_subplots:
         trades["desc"] = trades.apply(
             lambda row: (
                 f"{row['profit_ratio']:.2%}, "
-                + (f"{row['enter_tag']}, " if row["enter_tag"] is not None else "")
+                + (f"{row['enter_tag']}, " if pd.notna(row["enter_tag"]) else "")
                 + f"{row['exit_reason']}, "
                 + f"{row['trade_duration']} min"
             ),
diff --git a/freqtrade/rpc/rpc.py b/freqtrade/rpc/rpc.py
index 63f00a22d..0b47ecf7c 100644
--- a/freqtrade/rpc/rpc.py
+++ b/freqtrade/rpc/rpc.py
@@ -11,7 +11,7 @@ from typing import TYPE_CHECKING, Any
 import psutil
 from dateutil.relativedelta import relativedelta
 from dateutil.tz import tzlocal
-from numpy import inf, int64, isnan, mean, nan
+from numpy import inf, isnan, mean, nan
 from pandas import DataFrame, NaT, read_sql
 from sqlalchemy import func, select
 
@@ -794,7 +794,7 @@ class RPC:
         results = read_sql("wallet_history", con=Trade.session.bind, parse_dates=["timestamp"])
 
         results = results.rename({"timestamp": "date"}, axis=1)
-        results.loc[:, "__date_ts"] = results.loc[:, "date"].astype("int64") // 1000 // 1000
+        results.loc[:, "__date_ts"] = results.loc[:, "date"].dt.as_unit("ms").astype("int64")
         # Exclude non-bot managed for now
         results_filtered = results.loc[results["bot_managed"]]
 
@@ -1536,7 +1536,9 @@ class RPC:
                 df_cols = [col for col in dataframe_columns if col in cols_set]
                 dataframe = dataframe.loc[:, df_cols]
 
-            dataframe.loc[:, "__date_ts"] = dataframe.loc[:, "date"].astype(int64) // 1000 // 1000
+            dataframe.loc[:, "__date_ts"] = (
+                dataframe.loc[:, "date"].dt.as_unit("ms").astype("int64")
+            )
             # Move signal close to separate column when signal for easy plotting
             for sig_type in signals.keys():
                 if sig_type in dataframe.columns:
@@ -1546,7 +1548,7 @@ class RPC:
 
             # band-aid until this is fixed:
             # https://github.com/pandas-dev/pandas/issues/45836
-            datetime_types = ["datetime", "datetime64", "datetime64[ns, UTC]"]
+            datetime_types = ["datetime", "datetime64", "datetimetz"]
             date_columns = dataframe.select_dtypes(include=datetime_types)
             for date_column in date_columns:
                 # replace NaT with `None`
diff --git a/freqtrade/util/__init__.py b/freqtrade/util/__init__.py
index 06deb3ce0..0e9c1ac12 100644
--- a/freqtrade/util/__init__.py
+++ b/freqtrade/util/__init__.py
@@ -3,6 +3,7 @@ from freqtrade.util.datetime_helpers import (
     dt_from_ts,
     dt_humanize_delta,
     dt_now,
+    dt_now_no_micro,
     dt_ts,
     dt_ts_def,
     dt_ts_none,
@@ -39,6 +40,7 @@ __all__ = [
     "dt_from_ts",
     "dt_humanize_delta",
     "dt_now",
+    "dt_now_no_micro",
     "dt_ts",
     "dt_ts_def",
     "dt_ts_none",
diff --git a/freqtrade/util/datetime_helpers.py b/freqtrade/util/datetime_helpers.py
index b6535db5d..55bf29419 100644
--- a/freqtrade/util/datetime_helpers.py
+++ b/freqtrade/util/datetime_helpers.py
@@ -12,6 +12,13 @@ def dt_now() -> datetime:
     return datetime.now(UTC)
 
 
+def dt_now_no_micro() -> datetime:
+    """Return the current datetime in UTC without microseconds.
+    Should not be used outside of tests.
+    """
+    return dt_now().replace(microsecond=0)
+
+
 def dt_utc(
     year: int,
     month: int,
diff --git a/pyproject.toml b/pyproject.toml
index b62878fad..fd23460bb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -222,7 +222,6 @@ exclude-newer = "1 week"
 
 [tool.uv.exclude-newer-package]
 ccxt = false
-cryptography = "1 days"
 
 [tool.ruff]
 line-length = 100
diff --git a/requirements-dev.txt b/requirements-dev.txt
index 8018da419..d8f8c9719 100644
--- a/requirements-dev.txt
+++ b/requirements-dev.txt
@@ -6,10 +6,10 @@
 -r requirements-freqai-rl.txt
 -r docs/requirements-docs.txt
 
-ruff==0.15.8
+ruff==0.15.9
 mypy==1.20.0
 pre-commit==4.5.1
-pytest==9.0.2
+pytest==9.0.3
 pytest-asyncio==1.3.0
 pytest-cov==7.1.0
 pytest-mock==3.15.1
diff --git a/requirements.txt b/requirements.txt
index f33fd73c2..2af13fdb7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -5,12 +5,12 @@ numexpr==2.14.1
 # Indicator libraries
 ft-pandas-ta==0.3.16
 ta-lib==0.6.8
-technical==1.5.4
+technical==1.6.0
 
-ccxt==4.5.47
+ccxt==4.5.48
 cryptography==46.0.7
 aiohttp==3.13.5
-SQLAlchemy==2.0.48
+SQLAlchemy==2.0.49
 python-telegram-bot==22.7
 # can't be hard-pinned due to telegram-bot pinning httpx with ~
 httpx>=0.24.1
@@ -39,7 +39,7 @@ sdnotify==0.3.2
 # API Server
 fastapi==0.135.3
 pydantic==2.12.5
-uvicorn==0.42.0
+uvicorn==0.43.0
 pyjwt==2.12.1
 aiofiles==25.1.0
 psutil==7.2.2
diff --git a/tests/conftest.py b/tests/conftest.py
index 93d34fe18..46601ddfb 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -207,7 +207,7 @@ def generate_test_data(
 def generate_test_data_raw(timeframe: str, size: int, start: str = "2020-07-05", random_seed=42):
     """Generates data in the ohlcv format used by ccxt"""
     df = generate_test_data(timeframe, size, start, random_seed)
-    df["date"] = df.loc[:, "date"].astype(np.int64) // 1000 // 1000
+    df["date"] = df.loc[:, "date"].dt.as_unit("ms").astype("int64")
     return list(list(x) for x in zip(*(df[x].values.tolist() for x in df.columns), strict=False))
 
 
diff --git a/tests/data/test_metrics.py b/tests/data/test_metrics.py
index d19d9e328..242700eef 100644
--- a/tests/data/test_metrics.py
+++ b/tests/data/test_metrics.py
@@ -1,7 +1,8 @@
 from datetime import UTC, datetime, timedelta
 
+import numpy as np
 import pytest
-from pandas import DataFrame, DateOffset, Timestamp
+from pandas import DataFrame, DateOffset, Timestamp, to_datetime
 
 from freqtrade.configuration import TimeRange
 from freqtrade.data.btanalysis import (
@@ -11,12 +12,16 @@ from freqtrade.data.history import load_data, load_pair_history
 from freqtrade.data.metrics import (
     calculate_cagr,
     calculate_calmar,
+    calculate_calmar_from_balance,
     calculate_csum,
     calculate_expectancy,
     calculate_market_change,
     calculate_max_drawdown,
+    calculate_max_drawdown_from_balance,
     calculate_sharpe,
+    calculate_sharpe_from_balance,
     calculate_sortino,
+    calculate_sortino_from_balance,
     calculate_sqn,
     calculate_underwater,
     combine_dataframes_with_mean,
@@ -142,6 +147,48 @@ def test_calculate_max_drawdown(testdatadir):
         calculate_underwater(DataFrame())
 
 
+def test_calculate_max_drawdown_from_balance():
+    balance_history = DataFrame(
+        {
+            "date": to_datetime(
+                [
+                    "2025-01-01 00:00:00+00:00",
+                    "2025-01-01 12:00:00+00:00",
+                    "2025-01-01 18:00:00+00:00",
+                    "2025-01-04 00:00:00+00:00",
+                ],
+                utc=True,
+            ),
+            "total_quote": [100.0, 120.0, 80.0, 110.0],
+        }
+    )
+
+    drawdown = calculate_max_drawdown_from_balance(balance_history)
+    assert isinstance(drawdown.relative_account_drawdown, float)
+    assert pytest.approx(drawdown.relative_account_drawdown) == 1 / 3
+    assert pytest.approx(drawdown.drawdown_abs) == 40
+    assert pytest.approx(drawdown.current_high_value) == 20
+    assert pytest.approx(drawdown.low_value) == -20
+    assert pytest.approx(drawdown.high_value) == 20
+
+    assert drawdown.high_date == Timestamp("2025-01-01 12:00:00", tz="UTC")
+    assert drawdown.low_date == Timestamp("2025-01-01 18:00:00", tz="UTC")
+
+
+def test_calculate_max_drawdown_from_balance_empty_or_short():
+    with pytest.raises(ValueError, match=r"Balance-history dataframe empty\."):
+        calculate_max_drawdown_from_balance(DataFrame())
+
+    one_point = DataFrame(
+        {
+            "date": to_datetime(["2025-01-01 00:00:00+00:00"], utc=True),
+            "total_quote": [100.0],
+        }
+    )
+    with pytest.raises(ValueError, match=r"Balance-history dataframe empty\."):
+        calculate_max_drawdown_from_balance(one_point)
+
+
 def test_calculate_csum(testdatadir):
     filename = testdatadir / "backtest_results/backtest-result.json"
     bt_data = load_backtest_data(filename)
@@ -200,6 +247,53 @@ def test_calculate_sortino(testdatadir):
     assert pytest.approx(sortino) == 35.17722
 
 
+def test_calculate_sortino_from_balance():
+    balance_history = DataFrame(
+        {
+            "date": to_datetime(
+                [
+                    "2025-01-01 00:00:00+00:00",
+                    "2025-01-02 00:00:00+00:00",
+                    "2025-01-03 00:00:00+00:00",
+                    "2025-01-04 00:00:00+00:00",
+                    "2025-01-05 00:00:00+00:00",
+                ],
+                utc=True,
+            ),
+            "total_quote": [100.0, 110.0, 104.5, 125.4, 112.86],
+        }
+    )
+
+    sortino = calculate_sortino_from_balance(balance_history)
+    expected_returns = np.array([0.1, -0.05, 0.2, -0.1])
+    expected_sortino = expected_returns.mean() / np.std(expected_returns[expected_returns < 0])
+    expected_sortino *= np.sqrt(365)
+
+    assert isinstance(sortino, float)
+    assert pytest.approx(sortino) == expected_sortino
+    # Explicit assert
+    assert pytest.approx(sortino) == 28.6574597
+
+
+def test_calculate_sortino_from_balance_empty_or_no_downside():
+    assert calculate_sortino_from_balance(DataFrame()) == 0.0
+
+    positive_balance_history = DataFrame(
+        {
+            "date": to_datetime(
+                [
+                    "2025-01-01 00:00:00+00:00",
+                    "2025-01-02 00:00:00+00:00",
+                    "2025-01-03 00:00:00+00:00",
+                ],
+                utc=True,
+            ),
+            "total_quote": [100.0, 110.0, 121.0],
+        }
+    )
+    assert calculate_sortino_from_balance(positive_balance_history) == -100
+
+
 def test_calculate_sharpe(testdatadir):
     filename = testdatadir / "backtest_results/backtest-result.json"
     bt_data = load_backtest_data(filename)
@@ -217,6 +311,45 @@ def test_calculate_sharpe(testdatadir):
     assert pytest.approx(sharpe) == 44.5078669
 
 
+def test_calculate_sharpe_from_balance():
+    balance_history = DataFrame(
+        {
+            "date": to_datetime(
+                [
+                    "2025-01-01 00:00:00+00:00",
+                    "2025-01-02 00:00:00+00:00",
+                    "2025-01-03 00:00:00+00:00",
+                    "2025-01-04 00:00:00+00:00",
+                ],
+                utc=True,
+            ),
+            "total_quote": [100.0, 110.0, 104.5, 125.4],
+        }
+    )
+
+    sharpe = calculate_sharpe_from_balance(balance_history)
+    expected_returns = np.array([0.1, -0.05, 0.2])
+    expected_sharpe = expected_returns.mean() / expected_returns.std() * np.sqrt(365)
+
+    assert isinstance(sharpe, float)
+    assert pytest.approx(sharpe) == expected_sharpe
+
+
+def test_calculate_sharpe_from_balance_empty_or_flat():
+    assert calculate_sharpe_from_balance(DataFrame()) == 0.0
+
+    flat_balance_history = DataFrame(
+        {
+            "date": to_datetime(
+                ["2025-01-01 00:00:00+00:00", "2025-01-02 00:00:00+00:00"],
+                utc=True,
+            ),
+            "total_quote": [100.0, 100.0],
+        }
+    )
+    assert calculate_sharpe_from_balance(flat_balance_history) == -100
+
+
 def test_calculate_calmar(testdatadir):
     filename = testdatadir / "backtest_results/backtest-result.json"
     bt_data = load_backtest_data(filename)
@@ -234,6 +367,45 @@ def test_calculate_calmar(testdatadir):
     assert pytest.approx(calmar) == 559.040508
 
 
+def test_calculate_calmar_from_balance():
+    balance_history = DataFrame(
+        {
+            "date": to_datetime(
+                [
+                    "2025-01-01 00:00:00+00:00",
+                    "2025-01-01 12:00:00+00:00",
+                    "2025-01-01 18:00:00+00:00",
+                    "2025-01-04 00:00:00+00:00",
+                ],
+                utc=True,
+            ),
+            "total_quote": [100.0, 120.0, 80.0, 110.0],
+        }
+    )
+
+    calmar = calculate_calmar_from_balance(balance_history)
+    expected_returns_mean = ((110.0 - 100.0) / 100.0) / 3 * 100
+    expected_calmar = expected_returns_mean / (1 / 3) * np.sqrt(365)
+
+    assert isinstance(calmar, float)
+    assert pytest.approx(calmar) == expected_calmar
+
+
+def test_calculate_calmar_from_balance_empty_or_flat():
+    assert calculate_calmar_from_balance(DataFrame()) == 0.0
+
+    flat_balance_history = DataFrame(
+        {
+            "date": to_datetime(
+                ["2025-01-01 00:00:00+00:00", "2025-01-02 00:00:00+00:00"],
+                utc=True,
+            ),
+            "total_quote": [100.0, 100.0],
+        }
+    )
+    assert calculate_calmar_from_balance(flat_balance_history) == -100
+
+
 def test_calculate_sqn(testdatadir):
     filename = testdatadir / "backtest_results/backtest-result.json"
     bt_data = load_backtest_data(filename)
diff --git a/tests/exchange/test_binance_public_data.py b/tests/exchange/test_binance_public_data.py
index ab299321b..98d3864d3 100644
--- a/tests/exchange/test_binance_public_data.py
+++ b/tests/exchange/test_binance_public_data.py
@@ -69,7 +69,7 @@ def make_response_from_url(start_date, end_date):
             "taker_buy_quote_volume,ignore"
         )
         df = pd.DataFrame(columns=cols.split(","), dtype=float)
-        df["open_time"] = date_col.astype("int64") // 10**6
+        df["open_time"] = date_col.as_unit("ms").astype("int64")
         df["open"] = df["high"] = df["low"] = df["close"] = df["volume"] = 1.0
         return df
 
diff --git a/tests/exchange_online/test_ccxt_compat.py b/tests/exchange_online/test_ccxt_compat.py
index c44bb216a..556d5edf6 100644
--- a/tests/exchange_online/test_ccxt_compat.py
+++ b/tests/exchange_online/test_ccxt_compat.py
@@ -287,7 +287,7 @@ class TestCCXTExchange:
         # Check if last-timeframe is within the last 2 intervals
         now = datetime.now(UTC) - timedelta(minutes=(timeframe_to_minutes(timeframe) * 2))
         assert exch.klines(pair_tf).iloc[-1]["date"] >= timeframe_to_prev_date(timeframe, now)
-        assert exch.klines(pair_tf)["date"].astype(int).iloc[0] // 1e6 == since_ms
+        assert exch.klines(pair_tf)["date"].dt.as_unit("ms").astype("int64").iloc[0] == since_ms
 
     def _ccxt__async_get_candle_history(
         self, exchange, pair: str, timeframe: str, candle_type: CandleType, factor: float = 0.9
diff --git a/tests/optimize/test_optimize_reports.py b/tests/optimize/test_optimize_reports.py
index b37080f43..fcf5abffd 100644
--- a/tests/optimize/test_optimize_reports.py
+++ b/tests/optimize/test_optimize_reports.py
@@ -28,6 +28,7 @@ from freqtrade.optimize.optimize_reports import (
     generate_trading_stats,
     show_sorted_pairlist,
     store_backtest_results,
+    text_table_add_metrics,
     text_table_bt_results,
     text_table_strategy,
 )
@@ -36,6 +37,7 @@ from freqtrade.optimize.optimize_reports.optimize_reports import (
     _get_resample_from_period,
     calc_streak,
     generate_tag_metrics,
+    generate_wallet_stats,
 )
 from freqtrade.resolvers.strategy_resolver import StrategyResolver
 from freqtrade.util import dt_ts, format_duration
@@ -616,6 +618,63 @@ def test_text_table_strategy(testdatadir, capsys):
     )
 
 
+def test_generate_wallet_stats_extended_metrics():
+    wallet_df = pd.DataFrame(
+        {
+            "date": [
+                dt_utc(2025, 1, 1, 0, 0, 0),
+                dt_utc(2025, 1, 1, 12, 0, 0),
+                dt_utc(2025, 1, 1, 18, 0, 0),
+                dt_utc(2025, 1, 3, 0, 0, 0),
+            ],
+            "currency": ["BTC", "BTC", "BTC", "BTC"],
+            "rate": [1.0, 1.0, 1.0, 1.0],
+            "balance": [100.0, 120.0, 80.0, 110.0],
+        }
+    )
+
+    stats = generate_wallet_stats(wallet_df, "BTC")
+
+    assert "sharpe" in stats
+    assert "sortino" in stats
+    assert "calmar" in stats
+    assert "max_drawdown_account" in stats
+    assert "max_drawdown_abs" in stats
+    assert pytest.approx(stats["max_drawdown_account"]) == 1 / 3
+    assert stats["drawdown_start"] == "2025-01-01 12:00:00"
+    assert stats["drawdown_end"] == "2025-01-01 18:00:00"
+
+
+def test_text_table_add_metrics_shows_wallet_ratios(testdatadir, capsys):
+    filename = testdatadir / "backtest_results/backtest-result.json"
+    bt_data = load_backtest_stats(filename)
+    strat_results = next(iter(bt_data["strategy"].values()))
+    strat_results["wallet_stats"] = {
+        "low_balance": 0.95,
+        "high_balance": 1.12,
+        "low_date": "2025-01-01 18:00:00",
+        "high_date": "2025-01-01 12:00:00",
+        "sharpe": 1.23,
+        "sortino": 2.34,
+        "calmar": 3.45,
+        "max_drawdown_account": 0.12,
+        "max_relative_drawdown": 0.15,
+        "max_drawdown_abs": 0.05,
+        "drawdown_start": "2025-01-01 12:00:00",
+        "drawdown_end": "2025-01-01 18:00:00",
+        "max_drawdown_high": 1.12,
+        "max_drawdown_low": 0.95,
+    }
+
+    text_table_add_metrics(strat_results)
+    text = capsys.readouterr().out
+
+    assert "Sharpe (daily wallet balance)" in text
+    assert "Sortino (daily wallet balance)" in text
+    assert "Calmar (daily wallet balance)" in text
+    assert "Max % of account underwater (balance)" in text
+
+
 def test_generate_periodic_breakdown_stats(testdatadir):
     filename = testdatadir / "backtest_results/backtest-result.json"
     bt_data = load_backtest_data(filename).to_dict(orient="records")
@@ -654,7 +713,7 @@ def test_generate_periodic_breakdown_stats(testdatadir):
 
 
 def test__get_resample_from_period():
-    assert _get_resample_from_period("day") == "1d"
+    assert _get_resample_from_period("day") == "1D"
     assert _get_resample_from_period("week") == "1W-MON"
     assert _get_resample_from_period("month") == "1ME"
     assert _get_resample_from_period("weekday") == "weekday"
diff --git a/tests/strategy/test_interface.py b/tests/strategy/test_interface.py
index f64c2c3cb..4ddc35e88 100644
--- a/tests/strategy/test_interface.py
+++ b/tests/strategy/test_interface.py
@@ -22,6 +22,7 @@ from freqtrade.strategy.parameters import (
 )
 from freqtrade.strategy.strategy_validation import StrategyResultValidator
 from freqtrade.util import dt_now
+from freqtrade.util.datetime_helpers import dt_now_no_micro
 from tests.conftest import CURRENT_TEST_STRATEGY, TRADE_SIDES, log_has, log_has_re
 
 from .strats.strategy_test_v3 import StrategyTestV3
@@ -33,7 +34,7 @@ _STRATEGY.dp = DataProvider({}, None, None)
 
 
 def test_returns_latest_signal(ohlcv_history):
-    ohlcv_history.loc[1, "date"] = dt_now()
+    ohlcv_history.loc[1, "date"] = dt_now_no_micro()
     # Take a copy to correctly modify the call
     mocked_history = ohlcv_history.copy()
     mocked_history["enter_long"] = 0
@@ -160,7 +161,7 @@ def test_get_signal_exception_valueerror(mocker, caplog, ohlcv_history):
 def test_get_signal_old_dataframe(default_conf, mocker, caplog, ohlcv_history):
     # default_conf defines a 5m interval. we check interval * 2 + 5m
     # this is necessary as the last candle is removed (partial candles) by default
-    ohlcv_history.loc[1, "date"] = dt_now() - timedelta(minutes=16)
+    ohlcv_history.loc[1, "date"] = dt_now_no_micro() - timedelta(minutes=16)
     # Take a copy to correctly modify the call
     mocked_history = ohlcv_history.copy()
     mocked_history["exit_long"] = 0
@@ -179,7 +180,7 @@ def test_get_signal_old_dataframe(default_conf, mocker, caplog, ohlcv_history):
 def test_get_signal_no_sell_column(default_conf, mocker, caplog, ohlcv_history):
     # default_conf defines a 5m interval. we check interval * 2 + 5m
     # this is necessary as the last candle is removed (partial candles) by default
-    ohlcv_history.loc[1, "date"] = dt_now()
+    ohlcv_history.loc[1, "date"] = dt_now_no_micro()
     # Take a copy to correctly modify the call
     mocked_history = ohlcv_history.copy()
     # Intentionally don't set sell column
@@ -223,7 +224,7 @@ def test_ignore_expired_candle(default_conf):
 
 
 def test_assert_df_raise(mocker, caplog, ohlcv_history):
-    ohlcv_history.loc[1, "date"] = dt_now() - timedelta(minutes=16)
+    ohlcv_history.loc[1, "date"] = dt_now_no_micro() - timedelta(minutes=16)
     # Take a copy to correctly modify the call
     mocked_history = ohlcv_history.copy()
     mocked_history["sell"] = 0
@@ -1040,6 +1041,7 @@ def test_auto_hyperopt_interface_loadparams(default_conf, mocker, caplog):
     ],
 )
 def test_pandas_warning_direct(ohlcv_history, function, raises, recwarn):
+    recwarn.clear()
     df = _STRATEGY.populate_indicators(ohlcv_history, {"pair": "ETH/BTC"})
     if raises:
         assert len(recwarn) == 1
@@ -1053,6 +1055,7 @@ def test_pandas_warning_direct(ohlcv_history, function, raises, recwarn):
 
 
 def test_pandas_warning_through_analyze_pair(ohlcv_history, mocker, recwarn):
+    recwarn.clear()
     mocker.patch.object(_STRATEGY.dp, "ohlcv", return_value=ohlcv_history)
     _STRATEGY.analyze_pair("ETH/BTC")
     assert len(recwarn) == 0, f"warnings: {', '.join(str(w) for w in recwarn.list)}"
diff --git a/tests/util/test_datetime_helpers.py b/tests/util/test_datetime_helpers.py
index 9069b60c5..babe5b7b9 100644
--- a/tests/util/test_datetime_helpers.py
+++ b/tests/util/test_datetime_helpers.py
@@ -6,7 +6,9 @@ import time_machine
 from freqtrade.util import (
     dt_floor_day,
     dt_from_ts,
+    dt_humanize_delta,
     dt_now,
+    dt_now_no_micro,
     dt_ts,
     dt_ts_def,
     dt_ts_none,
@@ -16,15 +18,17 @@ from freqtrade.util import (
     format_ms_time_det,
     shorten_date,
 )
-from freqtrade.util.datetime_helpers import dt_humanize_delta
 
 
 def test_dt_now():
-    with time_machine.travel("2021-09-01 05:01:00 +00:00", tick=False) as t:
+    with time_machine.travel("2021-09-01 05:01:00.123 +00:00", tick=False) as t:
         now = datetime.now(UTC)
         assert dt_now() == now
         assert dt_ts() == int(now.timestamp() * 1000)
         assert dt_ts(now) == int(now.timestamp() * 1000)
+        assert dt_now().microsecond != 0.0
+        assert dt_now_no_micro().microsecond == 0.0
+        assert dt_now_no_micro() == now.replace(microsecond=0)
 
         t.shift(timedelta(hours=5))
         assert dt_now() >= now