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always start from best model

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Dobromir Popov 2025-02-04 21:16:35 +02:00
parent a25e1eb686
commit 070d58f2bf
1 changed files with 13 additions and 1 deletions
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14
crypto/brian/index-deep-new.py
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@ -355,6 +355,7 @@ def parse_args():
parser.add_argument('--epochs', type=int, default=100) parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--lr', type=float, default=3e-4) parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--threshold', type=float, default=0.005) parser.add_argument('--threshold', type=float, default=0.005)
parser.add_argument('--start_fresh', action='store_true', help='Start training from scratch ignoring saved checkpoints.')
return parser.parse_args() return parser.parse_args()
def random_action(): def random_action():
@ -379,7 +380,18 @@ async def main():
return return
base_tf = "1m" base_tf = "1m"
env = BacktestEnvironment(candles_dict, base_tf, timeframes) env = BacktestEnvironment(candles_dict, base_tf, timeframes)
train_on_historical_data(env, model, device, args) start_epoch = 0
if not args.start_fresh:
checkpoint = load_best_checkpoint(model)
if checkpoint is not None:
start_epoch = checkpoint.get("epoch", 0) + 1
print(f"Resuming training from epoch {start_epoch}.")
else:
print("No checkpoint found. Starting training from scratch.")
else:
print("Starting training from scratch as requested.")
train_on_historical_data(env, model, device, args, start_epoch=start_epoch)
elif args.mode == 'live': elif args.mode == 'live':
load_best_checkpoint(model) load_best_checkpoint(model)
candles_dict = load_candles_cache(CACHE_FILE) candles_dict = load_candles_cache(CACHE_FILE)