diff --git a/ai-example.py b/ai-example.py
deleted file mode 100644
index 0d25930..0000000
--- a/ai-example.py
+++ /dev/null
@@ -1,126 +0,0 @@
-import asyncio
-import tulipy
-import gymnasium as gym
-from datetime import datetime, timedelta
-import numpy as np
-import time
-from tensorflow.keras.callbacks import TensorBoard
-import argparse
-
-import octobot_commons.symbols as symbols
-import octobot_script as obs
-
-async def basic_evaluation_function(ctx):
-    closes = await obs.Close(ctx, max_history=True)
-    open = await obs.Open(ctx, limit=30)
-    high = await obs.High(ctx, limit=30)
-    low = await obs.Low(ctx, limit=30)
-    vol = await obs.Volume(ctx, limit=30)
-    rsi_v = tulipy.rsi(closes, period=10)
-    ema_values = tulipy.ema(closes, period=21)
-
-    try:
-        if (len(rsi_v) > 15 and len(ema_values) > 15):
-            return np.array([
-                closes[-10:],
-                open[-10:],
-                high[-10:],
-                low[-10:],
-                vol[-10:],
-                rsi_v[-15:],
-                ema_values[-15:]
-            ], dtype=np.float32).flatten()
-        else:
-            return np.zeros(80, dtype=np.float32) 
-    except ValueError:
-        return np.zeros(80, dtype=np.float32) 
-
-async def run_strategy(data, env, agent, symbol, time_frame, is_training=False, plot=False):
-    async def strategy(ctx):
-        state = None
-        if not env.env.get_wrapper_attr('is_reset'):
-            state = await env.reset(options={
-                'ctx': ctx
-            })
-        else:
-            state = await env.get_obs(ctx)
-            
-        action = agent.act(state)
-        next_state, reward, done, info = await env.step({
-            'ctx': ctx,
-            'content': action
-        })
-
-        if is_training:
-            agent.remember(state, action, reward, next_state, done)  
-
-    # Run a backtest using the above data, strategy and configuration.
-    res = await obs.run(data, strategy, {}, enable_logs=False)
-
-    if plot:
-        print(res.describe())
-        await res.plot(show=True)
-
-def init_argparse() -> argparse.ArgumentParser:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("-ex", "--exchange", type=str, default="binance")
-    parser.add_argument("-s", "--symbol", type=str, default="BTC/USDT")
-    parser.add_argument("-tf", "--timeframe", type=str, default="1d")
-    parser.add_argument("-e", "--episode", type=int, default=1)
-    parser.add_argument('-b', '--batch_size', type=int, default=32,
-                        help='batch size for experience replay')
-    parser.add_argument("-t", "--train", action=argparse.BooleanOptionalAction)
-    parser.add_argument("-p", "--plot", action=argparse.BooleanOptionalAction)
-    parser.add_argument('-w', '--weights', type=str, help='a trained model weights')
-    parser.add_argument("-d", "--days", type=int, default=365)
-    parser.add_argument("-ev", "--evaluate", action=argparse.BooleanOptionalAction)
-    parser.add_argument("-ep", "--epochs", type=int, default=20)
-    return parser
-
-
-
-def main():
-    parser = init_argparse()
-    args = parser.parse_args()
-
-    timestamp = time.strftime('%Y%m%d%H%M')
-    symbol = symbols.parse_symbol(args.symbol)
-    time_frame = args.timeframe
-    data = asyncio.run(obs.get_data(symbol.merged_str_symbol(), time_frame, exchange=args.exchange, start_timestamp=int(float(str((datetime.now() - timedelta(days=args.days)).timestamp()))))) # start_timestamp=1505606400
-
-    action_size = 9
-    gym_env = gym.make(action_size=action_size, id='TradingEnv', name= "test", dynamic_feature_functions=[basic_evaluation_function], traded_symbols=[symbol])
-    agent = obs.DQNAgent(action_size)
-
-    logdir = "tensorboard_logs/scalars/" + datetime.now().strftime("%Y%m%d-%H%M%S")
-    tensorboard_callback = TensorBoard(log_dir=logdir, histogram_freq=1, write_images=False, batch_size=args.batch_size)
-
-    if args.weights:
-        print(f"Loading model {args.weights}...")
-
-        # load trained weights
-        agent.load(args.weights)
-    elif not args.train:
-        print("weights has not be provided when using model!")
-        return
-
-    for episode in range(args.episode):
-        print(f"Starting episode {episode}...") 
-        asyncio.run(run_strategy(data, gym_env, agent, symbol, time_frame, is_training=args.train, plot=args.plot))
-        
-        if args.train and len(agent.memory) > args.batch_size:
-            print("Starting replay...")
-            score = agent.replay(args.batch_size, args.epochs, args.evaluate, tensorboard_callback)
-            if args.evaluate:
-                print(f"Score = {score}")
-
-        if args.train and (episode + 1) % 10 == 0:  # checkpoint weights
-            print("Saving...")
-            agent.save(f"weights/{timestamp}-dqn.h5")
-
-    if args.train:
-        agent.save(f"weights/{timestamp}-final-dqn.h5")
-
-    asyncio.run(data.stop())
-
-main()
diff --git a/octobot_script/internal/backtester_trading_mode.py b/octobot_script/internal/backtester_trading_mode.py
index 3570887..1a4b220 100644
--- a/octobot_script/internal/backtester_trading_mode.py
+++ b/octobot_script/internal/backtester_trading_mode.py
@@ -38,4 +38,5 @@ def get_supported_exchange_types(cls) -> list:
             trading_enums.ExchangeTypes.SPOT,
             trading_enums.ExchangeTypes.FUTURE,
             trading_enums.ExchangeTypes.MARGIN,
+            trading_enums.ExchangeTypes.OPTION,
         ]
diff --git a/requirements-ai.txt b/requirements-ai.txt
deleted file mode 100644
index 5bfb801..0000000
--- a/requirements-ai.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-numpy
-tensorflow==2.11.0
-gymnasium
-tqdm
-tensorboard