From 267f2e352ea298f4bda88053c89974a203dff344 Mon Sep 17 00:00:00 2001
From: Matthias <xmatthias@outlook.com>
Date: Tue, 23 Jan 2024 19:22:38 +0100
Subject: [PATCH] Add trades-generator

---
 tests/conftest.py | 39 ++++++++++++++++++++++++++++++++++++---
 1 file changed, 36 insertions(+), 3 deletions(-)

diff --git a/tests/conftest.py b/tests/conftest.py
index c717f84ba..8534cc34d 100644
--- a/tests/conftest.py
+++ b/tests/conftest.py
@@ -3,7 +3,7 @@ import json
 import logging
 import re
 from copy import deepcopy
-from datetime import timedelta
+from datetime import datetime, timedelta, timezone
 from pathlib import Path
 from typing import Optional
 from unittest.mock import MagicMock, Mock, PropertyMock
@@ -22,8 +22,7 @@ from freqtrade.exchange import Exchange, timeframe_to_minutes, timeframe_to_seco
 from freqtrade.freqtradebot import FreqtradeBot
 from freqtrade.persistence import LocalTrade, Order, Trade, init_db
 from freqtrade.resolvers import ExchangeResolver
-from freqtrade.util import dt_ts
-from freqtrade.util.datetime_helpers import dt_now
+from freqtrade.util import dt_now, dt_ts
 from freqtrade.worker import Worker
 from tests.conftest_trades import (leverage_trade, mock_trade_1, mock_trade_2, mock_trade_3,
                                    mock_trade_4, mock_trade_5, mock_trade_6, short_trade)
@@ -106,6 +105,40 @@ def get_args(args):
     return Arguments(args).get_parsed_arg()
 
 
+def generate_trades_history(n_rows, start_date: Optional[datetime] = None, days=5):
+    np.random.seed(42)
+    if not start_date:
+        start_date = datetime(2020, 1, 1, tzinfo=timezone.utc)
+
+        # Generate random data
+    end_date = start_date + timedelta(days=days)
+    _start_timestamp = start_date.timestamp()
+    _end_timestamp = pd.to_datetime(end_date).timestamp()
+
+    random_timestamps_in_seconds = np.random.uniform(_start_timestamp, _end_timestamp, n_rows)
+    timestamp = pd.to_datetime(random_timestamps_in_seconds, unit='s')
+
+    id = [f'a{np.random.randint(1e6, 1e7-1)}cd{np.random.randint(100, 999)}' for _ in range(n_rows)]
+
+    side = np.random.choice(['buy', 'sell'], n_rows)
+
+    # Initial price and subsequent changes
+    initial_price = 0.019626
+    price_changes = np.random.normal(0, initial_price * 0.05, n_rows)
+    price = np.cumsum(np.concatenate(([initial_price], price_changes)))[:n_rows]
+
+    amount = np.random.uniform(0.011, 20, n_rows)
+    cost = price * amount
+
+    # Create DataFrame
+    df = pd.DataFrame({'timestamp': timestamp, 'id': id, 'type': None, 'side': side,
+                       'price': price, 'amount': amount, 'cost': cost})
+    df['date'] = pd.to_datetime(df['timestamp'], unit='ms', utc=True)
+    df = df.sort_values('timestamp').reset_index(drop=True)
+    assert list(df.columns) == constants.DEFAULT_TRADES_COLUMNS + ['date']
+    return df
+
+
 def generate_test_data(timeframe: str, size: int, start: str = '2020-07-05'):
     np.random.seed(42)