Refactor backtesting logic and introduce new components
- Replaced TrendDetectorSimple with a new Backtest class for improved backtesting functionality. - Integrated argparse for configuration file input, allowing dynamic parameter setting. - Added MarketFees and Supertrends classes to handle fee calculations and trend detection, respectively. - Removed deprecated main_debug.py and trend_detector_simple.py files to streamline the codebase. - Enhanced process_timeframe_data to utilize the new Backtest class for executing trades and calculating results. - Updated Storage class to support writing backtest results with metadata.
This commit is contained in:
Simon Moisy
parent
14905017c8
commit
806697116d
222
cycles/backtest.py
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222
cycles/backtest.py
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import pandas as pd
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import numpy as np
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from cycles.supertrend import Supertrends
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from cycles.market_fees import MarketFees
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class Backtest:
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@staticmethod
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def run(min1_df, df, initial_usd, stop_loss_pct, debug=False):
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"""
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Backtest a simple strategy using the meta supertrend (all three supertrends agree).
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Buys when meta supertrend is positive, sells when negative, applies a percentage stop loss.
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Parameters:
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- min1_df: pandas DataFrame, 1-minute timeframe data for more accurate stop loss checking (optional)
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- initial_usd: float, starting USD amount
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- stop_loss_pct: float, stop loss as a fraction (e.g. 0.05 for 5%)
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- debug: bool, whether to print debug info
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"""
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_df = df.copy().reset_index(drop=True)
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_df['timestamp'] = pd.to_datetime(_df['timestamp'])
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supertrends = Supertrends(_df, verbose=False)
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supertrend_results_list = supertrends.calculate_supertrend_indicators()
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trends = [st['results']['trend'] for st in supertrend_results_list]
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trends_arr = np.stack(trends, axis=1)
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meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]),
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trends_arr[:,0], 0)
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position = 0 # 0 = no position, 1 = long
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entry_price = 0
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usd = initial_usd
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coin = 0
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trade_log = []
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max_balance = initial_usd
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drawdowns = []
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trades = []
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entry_time = None
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current_trade_min1_start_idx = None
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min1_df['timestamp'] = pd.to_datetime(min1_df.index)
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for i in range(1, len(_df)):
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price_open = _df['open'].iloc[i]
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price_close = _df['close'].iloc[i]
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date = _df['timestamp'].iloc[i]
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prev_mt = meta_trend[i-1]
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curr_mt = meta_trend[i]
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# Check stop loss if in position
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if position == 1:
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stop_loss_result = Backtest.check_stop_loss(
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min1_df,
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entry_time,
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date,
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entry_price,
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stop_loss_pct,
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coin,
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usd,
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debug,
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current_trade_min1_start_idx
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)
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if stop_loss_result is not None:
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trade_log_entry, current_trade_min1_start_idx, position, coin, entry_price = stop_loss_result
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trade_log.append(trade_log_entry)
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continue
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# Update the start index for next check
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current_trade_min1_start_idx = Backtest.get_current_min1_end_idx(min1_df, date)
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# Entry: only if not in position and signal changes to 1
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if position == 0 and prev_mt != 1 and curr_mt == 1:
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entry_result = Backtest.handle_entry(usd, price_open, date)
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coin, entry_price, entry_time, usd, position, trade_log_entry = entry_result
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trade_log.append(trade_log_entry)
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# Exit: only if in position and signal changes from 1 to -1
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elif position == 1 and prev_mt == 1 and curr_mt == -1:
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exit_result = Backtest.handle_exit(coin, price_open, entry_price, entry_time, date)
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usd, coin, position, entry_price, trade_log_entry = exit_result
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trade_log.append(trade_log_entry)
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# Track drawdown
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balance = usd if position == 0 else coin * price_close
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if balance > max_balance:
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max_balance = balance
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drawdown = (max_balance - balance) / max_balance
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drawdowns.append(drawdown)
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# If still in position at end, sell at last close
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if position == 1:
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exit_result = Backtest.handle_exit(coin, _df['close'].iloc[-1], entry_price, entry_time, _df['timestamp'].iloc[-1])
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usd, coin, position, entry_price, trade_log_entry = exit_result
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trade_log.append(trade_log_entry)
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# Calculate statistics
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final_balance = usd
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n_trades = len(trade_log)
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wins = [1 for t in trade_log if t['exit'] is not None and t['exit'] > t['entry']]
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win_rate = len(wins) / n_trades if n_trades > 0 else 0
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max_drawdown = max(drawdowns) if drawdowns else 0
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avg_trade = np.mean([t['exit']/t['entry']-1 for t in trade_log if t['exit'] is not None]) if trade_log else 0
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trades = []
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total_fees_usd = 0.0
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for trade in trade_log:
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if trade['exit'] is not None:
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profit_pct = (trade['exit'] - trade['entry']) / trade['entry']
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else:
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profit_pct = 0.0
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trades.append({
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'entry_time': trade['entry_time'],
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'exit_time': trade['exit_time'],
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'entry': trade['entry'],
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'exit': trade['exit'],
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'profit_pct': profit_pct,
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'type': trade.get('type', 'SELL'),
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'fee_usd': trade.get('fee_usd')
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})
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fee_usd = trade.get('fee_usd')
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total_fees_usd += fee_usd
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results = {
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"initial_usd": initial_usd,
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"final_usd": final_balance,
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"n_trades": n_trades,
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"win_rate": win_rate,
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"max_drawdown": max_drawdown,
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"avg_trade": avg_trade,
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"trade_log": trade_log,
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"trades": trades,
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"total_fees_usd": total_fees_usd,
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}
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if n_trades > 0:
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results["first_trade"] = {
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"entry_time": trade_log[0]['entry_time'],
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"entry": trade_log[0]['entry']
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}
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results["last_trade"] = {
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"exit_time": trade_log[-1]['exit_time'],
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"exit": trade_log[-1]['exit']
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}
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return results
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@staticmethod
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def check_stop_loss(min1_df, entry_time, date, entry_price, stop_loss_pct, coin, usd, debug, current_trade_min1_start_idx):
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stop_price = entry_price * (1 - stop_loss_pct)
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if current_trade_min1_start_idx is None:
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current_trade_min1_start_idx = min1_df.index[min1_df.index >= entry_time][0]
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current_min1_end_idx = min1_df.index[min1_df.index <= date][-1]
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# Check all 1-minute candles in between for stop loss
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min1_slice = min1_df.loc[current_trade_min1_start_idx:current_min1_end_idx]
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if (min1_slice['low'] <= stop_price).any():
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# Stop loss triggered, find the exact candle
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stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
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# More realistic fill: if open < stop, fill at open, else at stop
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if stop_candle['open'] < stop_price:
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sell_price = stop_candle['open']
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else:
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sell_price = stop_price
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if debug:
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print(f"STOP LOSS triggered: entry={entry_price}, stop={stop_price}, sell_price={sell_price}, entry_time={entry_time}, stop_time={stop_candle.name}")
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btc_to_sell = coin
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usd_gross = btc_to_sell * sell_price
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exit_fee = MarketFees.calculate_okx_taker_maker_fee(usd_gross, is_maker=False)
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trade_log_entry = {
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'type': 'STOP',
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'entry': entry_price,
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'exit': sell_price,
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'entry_time': entry_time,
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'exit_time': stop_candle.name,
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'fee_usd': exit_fee
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}
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# After stop loss, reset position and entry
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return trade_log_entry, None, 0, 0, 0
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return None
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@staticmethod
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def handle_entry(usd, price_open, date):
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entry_fee = MarketFees.calculate_okx_taker_maker_fee(usd, is_maker=False)
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usd_after_fee = usd - entry_fee
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coin = usd_after_fee / price_open
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entry_price = price_open
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entry_time = date
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usd = 0
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position = 1
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trade_log_entry = {
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'type': 'BUY',
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'entry': entry_price,
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'exit': None,
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'entry_time': entry_time,
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'exit_time': None,
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'fee_usd': entry_fee
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}
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return coin, entry_price, entry_time, usd, position, trade_log_entry
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@staticmethod
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def handle_exit(coin, price_open, entry_price, entry_time, date):
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btc_to_sell = coin
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usd_gross = btc_to_sell * price_open
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exit_fee = MarketFees.calculate_okx_taker_maker_fee(usd_gross, is_maker=False)
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usd = usd_gross - exit_fee
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trade_log_entry = {
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'type': 'SELL',
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'entry': entry_price,
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'exit': price_open,
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'entry_time': entry_time,
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'exit_time': date,
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'fee_usd': exit_fee
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}
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coin = 0
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position = 0
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entry_price = 0
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return usd, coin, position, entry_price, trade_log_entry
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@staticmethod
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def get_current_min1_end_idx(min1_df, date):
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# Implement the logic to find the end index of the current 1-minute candle
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# This is a placeholder and should be replaced with the actual implementation
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return min1_df.index[min1_df.index <= date][-1]
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import pandas as pd
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import numpy as np
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from trend_detector_simple import TrendDetectorSimple
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import os
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import datetime
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import csv
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def load_data(file_path, start_date, stop_date):
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"""Load and filter data by date range."""
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data = pd.read_csv(file_path)
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data['Timestamp'] = pd.to_datetime(data['Timestamp'], unit='s')
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data = data[(data['Timestamp'] >= start_date) & (data['Timestamp'] <= stop_date)]
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data.columns = data.columns.str.lower()
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return data.set_index('timestamp')
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def process_month_timeframe(min1_df, month_df, stop_loss_pcts, rule_name, initial_usd):
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"""Process a single month for a given timeframe with all stop loss values."""
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month_df = month_df.copy().reset_index(drop=True)
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trend_detector = TrendDetectorSimple(month_df, verbose=False)
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analysis_results = trend_detector.detect_trends()
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signal_df = analysis_results.get('signal_df')
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results_rows = []
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trade_rows = []
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for stop_loss_pct in stop_loss_pcts:
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results = trend_detector.backtest_meta_supertrend(
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min1_df,
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initial_usd=initial_usd,
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stop_loss_pct=stop_loss_pct
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)
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trades = results.get('trades', [])
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n_trades = results["n_trades"]
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n_winning_trades = sum(1 for trade in trades if trade['profit_pct'] > 0)
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total_profit = sum(trade['profit_pct'] for trade in trades)
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total_loss = sum(-trade['profit_pct'] for trade in trades if trade['profit_pct'] < 0)
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win_rate = n_winning_trades / n_trades if n_trades > 0 else 0
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avg_trade = total_profit / n_trades if n_trades > 0 else 0
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profit_ratio = total_profit / total_loss if total_loss > 0 else float('inf')
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# Max drawdown
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cumulative_profit = 0
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max_drawdown = 0
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peak = 0
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for trade in trades:
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cumulative_profit += trade['profit_pct']
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if cumulative_profit > peak:
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peak = cumulative_profit
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drawdown = peak - cumulative_profit
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if drawdown > max_drawdown:
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max_drawdown = drawdown
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# Final USD
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final_usd = initial_usd
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for trade in trades:
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final_usd *= (1 + trade['profit_pct'])
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row = {
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"timeframe": rule_name,
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"month": str(month_df['timestamp'].iloc[0].to_period('M')),
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"stop_loss_pct": stop_loss_pct,
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"n_trades": n_trades,
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"n_stop_loss": sum(1 for trade in trades if 'type' in trade and trade['type'] == 'STOP'),
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"win_rate": win_rate,
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"max_drawdown": max_drawdown,
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"avg_trade": avg_trade,
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"profit_ratio": profit_ratio,
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"initial_usd": initial_usd,
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"final_usd": final_usd,
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}
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results_rows.append(row)
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for trade in trades:
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trade_rows.append({
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"timeframe": rule_name,
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"month": str(month_df['timestamp'].iloc[0].to_period('M')),
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"stop_loss_pct": stop_loss_pct,
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"entry_time": trade.get("entry_time"),
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"exit_time": trade.get("exit_time"),
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"entry_price": trade.get("entry_price"),
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"exit_price": trade.get("exit_price"),
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"profit_pct": trade.get("profit_pct"),
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"type": trade.get("type", ""),
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})
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return results_rows, trade_rows
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def process_timeframe(rule, data_1min, stop_loss_pcts, initial_usd):
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"""Process an entire timeframe sequentially."""
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if rule == "1T":
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df = data_1min.copy()
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else:
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df = data_1min.resample(rule).agg({
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'open': 'first',
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'high': 'max',
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'low': 'min',
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'close': 'last',
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'volume': 'sum'
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}).dropna()
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df = df.reset_index()
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df['month'] = df['timestamp'].dt.to_period('M')
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results_rows = []
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all_trade_rows = []
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for month, month_df in df.groupby('month'):
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if len(month_df) < 10:
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continue
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month_results, month_trades = process_month_timeframe(data_1min, month_df, stop_loss_pcts, rule, initial_usd)
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results_rows.extend(month_results)
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all_trade_rows.extend(month_trades)
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return results_rows, all_trade_rows
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def aggregate_results(all_rows, initial_usd):
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"""Aggregate results per stop_loss_pct and per rule (timeframe)."""
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from collections import defaultdict
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grouped = defaultdict(list)
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for row in all_rows:
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key = (row['timeframe'], row['stop_loss_pct'])
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grouped[key].append(row)
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summary_rows = []
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|
||||||
for (rule, stop_loss_pct), rows in grouped.items():
|
|
||||||
n_months = len(rows)
|
|
||||||
total_trades = sum(r['n_trades'] for r in rows)
|
|
||||||
total_stop_loss = sum(r['n_stop_loss'] for r in rows)
|
|
||||||
avg_win_rate = np.mean([r['win_rate'] for r in rows])
|
|
||||||
avg_max_drawdown = np.mean([r['max_drawdown'] for r in rows])
|
|
||||||
avg_avg_trade = np.mean([r['avg_trade'] for r in rows])
|
|
||||||
avg_profit_ratio = np.mean([r['profit_ratio'] for r in rows])
|
|
||||||
final_usd = np.mean([r.get('final_usd', initial_usd) for r in rows])
|
|
||||||
|
|
||||||
summary_rows.append({
|
|
||||||
"timeframe": rule,
|
|
||||||
"stop_loss_pct": stop_loss_pct,
|
|
||||||
"n_trades": total_trades,
|
|
||||||
"n_stop_loss": total_stop_loss,
|
|
||||||
"win_rate": avg_win_rate,
|
|
||||||
"max_drawdown": avg_max_drawdown,
|
|
||||||
"avg_trade": avg_avg_trade,
|
|
||||||
"profit_ratio": avg_profit_ratio,
|
|
||||||
"initial_usd": initial_usd,
|
|
||||||
"final_usd": final_usd,
|
|
||||||
})
|
|
||||||
return summary_rows
|
|
||||||
|
|
||||||
def write_results(filename, fieldnames, rows):
|
|
||||||
"""Write results to a CSV file."""
|
|
||||||
with open(filename, 'w', newline="") as csvfile:
|
|
||||||
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
|
|
||||||
writer.writeheader()
|
|
||||||
for row in rows:
|
|
||||||
writer.writerow(row)
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
# Config
|
|
||||||
start_date = '2020-01-01'
|
|
||||||
stop_date = '2025-05-15'
|
|
||||||
initial_usd = 10000
|
|
||||||
|
|
||||||
results_dir = "results"
|
|
||||||
os.makedirs(results_dir, exist_ok=True)
|
|
||||||
timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
|
|
||||||
|
|
||||||
timeframes = ["6h", "1D"]
|
|
||||||
stop_loss_pcts = [0.01, 0.02, 0.03, 0.05, 0.07, 0.10]
|
|
||||||
|
|
||||||
data_1min = load_data('./data/btcusd_1-min_data.csv', start_date, stop_date)
|
|
||||||
print(f"1min rows: {len(data_1min)}")
|
|
||||||
|
|
||||||
filename = os.path.join(
|
|
||||||
results_dir,
|
|
||||||
f"{timestamp}_backtest_results_{start_date}_{stop_date}_multi_timeframe_stoploss.csv"
|
|
||||||
)
|
|
||||||
fieldnames = ["timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate", "max_drawdown", "avg_trade", "profit_ratio", "initial_usd", "final_usd"]
|
|
||||||
|
|
||||||
all_results = []
|
|
||||||
all_trades = []
|
|
||||||
|
|
||||||
for name in timeframes:
|
|
||||||
print(f"Processing timeframe: {name}")
|
|
||||||
results, trades = process_timeframe(name, data_1min, stop_loss_pcts, initial_usd)
|
|
||||||
all_results.extend(results)
|
|
||||||
all_trades.extend(trades)
|
|
||||||
|
|
||||||
summary_rows = aggregate_results(all_results, initial_usd)
|
|
||||||
# write_results(filename, fieldnames, summary_rows)
|
|
||||||
|
|
||||||
trades_filename = os.path.join(
|
|
||||||
results_dir,
|
|
||||||
f"{timestamp}_backtest_trades.csv"
|
|
||||||
)
|
|
||||||
trades_fieldnames = [
|
|
||||||
"timeframe", "month", "stop_loss_pct", "entry_time", "exit_time",
|
|
||||||
"entry_price", "exit_price", "profit_pct", "type"
|
|
||||||
]
|
|
||||||
# write_results(trades_filename, trades_fieldnames, all_trades)
|
|
||||||
7
cycles/market_fees.py
Normal file
7
cycles/market_fees.py
Normal file
@ -0,0 +1,7 @@
|
|||||||
|
import pandas as pd
|
||||||
|
|
||||||
|
class MarketFees:
|
||||||
|
@staticmethod
|
||||||
|
def calculate_okx_taker_maker_fee(amount, is_maker=True):
|
||||||
|
fee_rate = 0.0008 if is_maker else 0.0010
|
||||||
|
return amount * fee_rate
|
||||||
336
cycles/supertrend.py
Normal file
336
cycles/supertrend.py
Normal file
@ -0,0 +1,336 @@
|
|||||||
|
import pandas as pd
|
||||||
|
import numpy as np
|
||||||
|
import logging
|
||||||
|
from scipy.signal import find_peaks
|
||||||
|
from matplotlib.patches import Rectangle
|
||||||
|
from scipy import stats
|
||||||
|
import concurrent.futures
|
||||||
|
from functools import partial
|
||||||
|
from functools import lru_cache
|
||||||
|
import matplotlib.pyplot as plt
|
||||||
|
|
||||||
|
# Color configuration
|
||||||
|
# Plot colors
|
||||||
|
DARK_BG_COLOR = '#181C27'
|
||||||
|
LEGEND_BG_COLOR = '#333333'
|
||||||
|
TITLE_COLOR = 'white'
|
||||||
|
AXIS_LABEL_COLOR = 'white'
|
||||||
|
|
||||||
|
# Candlestick colors
|
||||||
|
CANDLE_UP_COLOR = '#089981' # Green
|
||||||
|
CANDLE_DOWN_COLOR = '#F23645' # Red
|
||||||
|
|
||||||
|
# Marker colors
|
||||||
|
MIN_COLOR = 'red'
|
||||||
|
MAX_COLOR = 'green'
|
||||||
|
|
||||||
|
# Line style colors
|
||||||
|
MIN_LINE_STYLE = 'g--' # Green dashed
|
||||||
|
MAX_LINE_STYLE = 'r--' # Red dashed
|
||||||
|
SMA7_LINE_STYLE = 'y-' # Yellow solid
|
||||||
|
SMA15_LINE_STYLE = 'm-' # Magenta solid
|
||||||
|
|
||||||
|
# SuperTrend colors
|
||||||
|
ST_COLOR_UP = 'g-'
|
||||||
|
ST_COLOR_DOWN = 'r-'
|
||||||
|
|
||||||
|
# Cache the calculation results by function parameters
|
||||||
|
@lru_cache(maxsize=32)
|
||||||
|
def cached_supertrend_calculation(period, multiplier, data_tuple):
|
||||||
|
# Convert tuple back to numpy arrays
|
||||||
|
high = np.array(data_tuple[0])
|
||||||
|
low = np.array(data_tuple[1])
|
||||||
|
close = np.array(data_tuple[2])
|
||||||
|
|
||||||
|
# Calculate TR and ATR using vectorized operations
|
||||||
|
tr = np.zeros_like(close)
|
||||||
|
tr[0] = high[0] - low[0]
|
||||||
|
hc_range = np.abs(high[1:] - close[:-1])
|
||||||
|
lc_range = np.abs(low[1:] - close[:-1])
|
||||||
|
hl_range = high[1:] - low[1:]
|
||||||
|
tr[1:] = np.maximum.reduce([hl_range, hc_range, lc_range])
|
||||||
|
|
||||||
|
# Use numpy's exponential moving average
|
||||||
|
atr = np.zeros_like(tr)
|
||||||
|
atr[0] = tr[0]
|
||||||
|
multiplier_ema = 2.0 / (period + 1)
|
||||||
|
for i in range(1, len(tr)):
|
||||||
|
atr[i] = (tr[i] * multiplier_ema) + (atr[i-1] * (1 - multiplier_ema))
|
||||||
|
|
||||||
|
# Calculate bands
|
||||||
|
upper_band = np.zeros_like(close)
|
||||||
|
lower_band = np.zeros_like(close)
|
||||||
|
for i in range(len(close)):
|
||||||
|
hl_avg = (high[i] + low[i]) / 2
|
||||||
|
upper_band[i] = hl_avg + (multiplier * atr[i])
|
||||||
|
lower_band[i] = hl_avg - (multiplier * atr[i])
|
||||||
|
|
||||||
|
final_upper = np.zeros_like(close)
|
||||||
|
final_lower = np.zeros_like(close)
|
||||||
|
supertrend = np.zeros_like(close)
|
||||||
|
trend = np.zeros_like(close)
|
||||||
|
final_upper[0] = upper_band[0]
|
||||||
|
final_lower[0] = lower_band[0]
|
||||||
|
if close[0] <= upper_band[0]:
|
||||||
|
supertrend[0] = upper_band[0]
|
||||||
|
trend[0] = -1
|
||||||
|
else:
|
||||||
|
supertrend[0] = lower_band[0]
|
||||||
|
trend[0] = 1
|
||||||
|
for i in range(1, len(close)):
|
||||||
|
if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
|
||||||
|
final_upper[i] = upper_band[i]
|
||||||
|
else:
|
||||||
|
final_upper[i] = final_upper[i-1]
|
||||||
|
if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
|
||||||
|
final_lower[i] = lower_band[i]
|
||||||
|
else:
|
||||||
|
final_lower[i] = final_lower[i-1]
|
||||||
|
if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
|
||||||
|
supertrend[i] = final_upper[i]
|
||||||
|
trend[i] = -1
|
||||||
|
elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
|
||||||
|
supertrend[i] = final_lower[i]
|
||||||
|
trend[i] = 1
|
||||||
|
elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
|
||||||
|
supertrend[i] = final_lower[i]
|
||||||
|
trend[i] = 1
|
||||||
|
elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
|
||||||
|
supertrend[i] = final_upper[i]
|
||||||
|
trend[i] = -1
|
||||||
|
return {
|
||||||
|
'supertrend': supertrend,
|
||||||
|
'trend': trend,
|
||||||
|
'upper_band': final_upper,
|
||||||
|
'lower_band': final_lower
|
||||||
|
}
|
||||||
|
|
||||||
|
def calculate_supertrend_external(data, period, multiplier):
|
||||||
|
# Convert DataFrame columns to hashable tuples
|
||||||
|
high_tuple = tuple(data['high'])
|
||||||
|
low_tuple = tuple(data['low'])
|
||||||
|
close_tuple = tuple(data['close'])
|
||||||
|
|
||||||
|
# Call the cached function
|
||||||
|
return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))
|
||||||
|
|
||||||
|
|
||||||
|
class Supertrends:
|
||||||
|
def __init__(self, data, verbose=False, display=False):
|
||||||
|
"""
|
||||||
|
Initialize the TrendDetectorSimple class.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
- data: pandas DataFrame containing price data
|
||||||
|
- verbose: boolean, whether to display detailed logging information
|
||||||
|
- display: boolean, whether to enable display/plotting features
|
||||||
|
"""
|
||||||
|
|
||||||
|
self.data = data
|
||||||
|
self.verbose = verbose
|
||||||
|
self.display = display
|
||||||
|
|
||||||
|
# Only define display-related variables if display is True
|
||||||
|
if self.display:
|
||||||
|
# Plot style configuration
|
||||||
|
self.plot_style = 'dark_background'
|
||||||
|
self.bg_color = DARK_BG_COLOR
|
||||||
|
self.plot_size = (12, 8)
|
||||||
|
|
||||||
|
# Candlestick configuration
|
||||||
|
self.candle_width = 0.6
|
||||||
|
self.candle_up_color = CANDLE_UP_COLOR
|
||||||
|
self.candle_down_color = CANDLE_DOWN_COLOR
|
||||||
|
self.candle_alpha = 0.8
|
||||||
|
self.wick_width = 1
|
||||||
|
|
||||||
|
# Marker configuration
|
||||||
|
self.min_marker = '^'
|
||||||
|
self.min_color = MIN_COLOR
|
||||||
|
self.min_size = 100
|
||||||
|
self.max_marker = 'v'
|
||||||
|
self.max_color = MAX_COLOR
|
||||||
|
self.max_size = 100
|
||||||
|
self.marker_zorder = 100
|
||||||
|
|
||||||
|
# Line configuration
|
||||||
|
self.line_width = 1
|
||||||
|
self.min_line_style = MIN_LINE_STYLE
|
||||||
|
self.max_line_style = MAX_LINE_STYLE
|
||||||
|
self.sma7_line_style = SMA7_LINE_STYLE
|
||||||
|
self.sma15_line_style = SMA15_LINE_STYLE
|
||||||
|
|
||||||
|
# Text configuration
|
||||||
|
self.title_size = 14
|
||||||
|
self.title_color = TITLE_COLOR
|
||||||
|
self.axis_label_size = 12
|
||||||
|
self.axis_label_color = AXIS_LABEL_COLOR
|
||||||
|
|
||||||
|
# Legend configuration
|
||||||
|
self.legend_loc = 'best'
|
||||||
|
self.legend_bg_color = LEGEND_BG_COLOR
|
||||||
|
|
||||||
|
# Configure logging
|
||||||
|
logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
|
||||||
|
format='%(asctime)s - %(levelname)s - %(message)s')
|
||||||
|
self.logger = logging.getLogger('TrendDetectorSimple')
|
||||||
|
|
||||||
|
# Convert data to pandas DataFrame if it's not already
|
||||||
|
if not isinstance(self.data, pd.DataFrame):
|
||||||
|
if isinstance(self.data, list):
|
||||||
|
self.data = pd.DataFrame({'close': self.data})
|
||||||
|
else:
|
||||||
|
raise ValueError("Data must be a pandas DataFrame or a list")
|
||||||
|
|
||||||
|
def calculate_tr(self):
|
||||||
|
"""
|
||||||
|
Calculate True Range (TR) for the price data.
|
||||||
|
|
||||||
|
True Range is the greatest of:
|
||||||
|
1. Current high - current low
|
||||||
|
2. |Current high - previous close|
|
||||||
|
3. |Current low - previous close|
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
- Numpy array of TR values
|
||||||
|
"""
|
||||||
|
df = self.data.copy()
|
||||||
|
high = df['high'].values
|
||||||
|
low = df['low'].values
|
||||||
|
close = df['close'].values
|
||||||
|
|
||||||
|
tr = np.zeros_like(close)
|
||||||
|
tr[0] = high[0] - low[0] # First TR is just the first day's range
|
||||||
|
|
||||||
|
for i in range(1, len(close)):
|
||||||
|
# Current high - current low
|
||||||
|
hl_range = high[i] - low[i]
|
||||||
|
# |Current high - previous close|
|
||||||
|
hc_range = abs(high[i] - close[i-1])
|
||||||
|
# |Current low - previous close|
|
||||||
|
lc_range = abs(low[i] - close[i-1])
|
||||||
|
|
||||||
|
# TR is the maximum of these three values
|
||||||
|
tr[i] = max(hl_range, hc_range, lc_range)
|
||||||
|
|
||||||
|
return tr
|
||||||
|
|
||||||
|
def calculate_atr(self, period=14):
|
||||||
|
"""
|
||||||
|
Calculate Average True Range (ATR) for the price data.
|
||||||
|
|
||||||
|
ATR is the exponential moving average of the True Range over a specified period.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
- period: int, the period for the ATR calculation (default: 14)
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
- Numpy array of ATR values
|
||||||
|
"""
|
||||||
|
|
||||||
|
tr = self.calculate_tr()
|
||||||
|
atr = np.zeros_like(tr)
|
||||||
|
|
||||||
|
# First ATR value is just the first TR
|
||||||
|
atr[0] = tr[0]
|
||||||
|
|
||||||
|
# Calculate exponential moving average (EMA) of TR
|
||||||
|
multiplier = 2.0 / (period + 1)
|
||||||
|
|
||||||
|
for i in range(1, len(tr)):
|
||||||
|
atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
|
||||||
|
|
||||||
|
return atr
|
||||||
|
|
||||||
|
def detect_trends(self):
|
||||||
|
"""
|
||||||
|
Detect trends by identifying local minima and maxima in the price data
|
||||||
|
using scipy.signal.find_peaks.
|
||||||
|
|
||||||
|
Parameters:
|
||||||
|
- prominence: float, required prominence of peaks (relative to the price range)
|
||||||
|
- width: int, required width of peaks in data points
|
||||||
|
|
||||||
|
Returns:
|
||||||
|
- DataFrame with columns for timestamps, prices, and trend indicators
|
||||||
|
- Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
|
||||||
|
"""
|
||||||
|
df = self.data
|
||||||
|
# close_prices = df['close'].values
|
||||||
|
|
||||||
|
# max_peaks, _ = find_peaks(close_prices)
|
||||||
|
# min_peaks, _ = find_peaks(-close_prices)
|
||||||
|
|
||||||
|
# df['is_min'] = False
|
||||||
|
# df['is_max'] = False
|
||||||
|
|
||||||
|
# for peak in max_peaks:
|
||||||
|
# df.at[peak, 'is_max'] = True
|
||||||
|
# for peak in min_peaks:
|
||||||
|
# df.at[peak, 'is_min'] = True
|
||||||
|
|
||||||
|
# result = df[['timestamp', 'close', 'is_min', 'is_max']].copy()
|
||||||
|
|
||||||
|
# Perform linear regression on min_peaks and max_peaks
|
||||||
|
# min_prices = df['close'].iloc[min_peaks].values
|
||||||
|
# max_prices = df['close'].iloc[max_peaks].values
|
||||||
|
|
||||||
|
# Linear regression for min peaks if we have at least 2 points
|
||||||
|
# min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
|
||||||
|
# Linear regression for max peaks if we have at least 2 points
|
||||||
|
# max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
|
||||||
|
|
||||||
|
# Calculate Simple Moving Averages (SMA) for 7 and 15 periods
|
||||||
|
# sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
|
||||||
|
# sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
|
||||||
|
|
||||||
|
analysis_results = {}
|
||||||
|
# analysis_results['linear_regression'] = {
|
||||||
|
# 'min': {
|
||||||
|
# 'slope': min_slope,
|
||||||
|
# 'intercept': min_intercept,
|
||||||
|
# 'r_squared': min_r_value ** 2
|
||||||
|
# },
|
||||||
|
# 'max': {
|
||||||
|
# 'slope': max_slope,
|
||||||
|
# 'intercept': max_intercept,
|
||||||
|
# 'r_squared': max_r_value ** 2
|
||||||
|
# }
|
||||||
|
# }
|
||||||
|
# analysis_results['sma'] = {
|
||||||
|
# '7': sma_7,
|
||||||
|
# '15': sma_15
|
||||||
|
# }
|
||||||
|
|
||||||
|
# Calculate SuperTrend indicators
|
||||||
|
supertrend_results_list = self._calculate_supertrend_indicators()
|
||||||
|
analysis_results['supertrend'] = supertrend_results_list
|
||||||
|
|
||||||
|
return analysis_results
|
||||||
|
|
||||||
|
def calculate_supertrend_indicators(self):
|
||||||
|
"""
|
||||||
|
Calculate SuperTrend indicators with different parameter sets in parallel.
|
||||||
|
Returns:
|
||||||
|
- list, the SuperTrend results
|
||||||
|
"""
|
||||||
|
supertrend_params = [
|
||||||
|
{"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
|
||||||
|
{"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
|
||||||
|
{"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
|
||||||
|
]
|
||||||
|
data = self.data.copy()
|
||||||
|
|
||||||
|
# For just 3 calculations, direct calculation might be faster than process pool
|
||||||
|
results = []
|
||||||
|
for p in supertrend_params:
|
||||||
|
result = calculate_supertrend_external(data, p["period"], p["multiplier"])
|
||||||
|
results.append(result)
|
||||||
|
|
||||||
|
supertrend_results_list = []
|
||||||
|
for params, result in zip(supertrend_params, results):
|
||||||
|
supertrend_results_list.append({
|
||||||
|
"results": result,
|
||||||
|
"params": params
|
||||||
|
})
|
||||||
|
return supertrend_results_list
|
||||||
@ -1,25 +0,0 @@
|
|||||||
import pandas as pd
|
|
||||||
|
|
||||||
class Taxes:
|
|
||||||
def __init__(self, tax_rate=0.20):
|
|
||||||
"""
|
|
||||||
tax_rate: flat tax rate on positive profits (e.g., 0.20 for 20%)
|
|
||||||
"""
|
|
||||||
self.tax_rate = tax_rate
|
|
||||||
|
|
||||||
def add_taxes_to_results_csv(self, input_csv, output_csv=None, profit_col='final_usd'):
|
|
||||||
"""
|
|
||||||
Reads a backtest results CSV, adds tax columns, and writes to a new CSV.
|
|
||||||
- input_csv: path to the input CSV file
|
|
||||||
- output_csv: path to the output CSV file (if None, overwrite input)
|
|
||||||
- profit_col: column name for profit (default: 'final_usd')
|
|
||||||
"""
|
|
||||||
df = pd.read_csv(input_csv, delimiter=None)
|
|
||||||
# Compute tax only on positive profits
|
|
||||||
df['tax_paid'] = df[profit_col].apply(lambda x: self.tax_rate * x if x > 0 else 0)
|
|
||||||
df['net_profit_after_tax'] = df[profit_col] - df['tax_paid']
|
|
||||||
df['cumulative_tax_paid'] = df['tax_paid'].cumsum()
|
|
||||||
if not output_csv:
|
|
||||||
output_csv = input_csv
|
|
||||||
df.to_csv(output_csv, index=False)
|
|
||||||
return output_csv
|
|
||||||
@ -1,848 +0,0 @@
|
|||||||
import pandas as pd
|
|
||||||
import numpy as np
|
|
||||||
import logging
|
|
||||||
from scipy.signal import find_peaks
|
|
||||||
from matplotlib.patches import Rectangle
|
|
||||||
from scipy import stats
|
|
||||||
import concurrent.futures
|
|
||||||
from functools import partial
|
|
||||||
from functools import lru_cache
|
|
||||||
import matplotlib.pyplot as plt
|
|
||||||
|
|
||||||
# Color configuration
|
|
||||||
# Plot colors
|
|
||||||
DARK_BG_COLOR = '#181C27'
|
|
||||||
LEGEND_BG_COLOR = '#333333'
|
|
||||||
TITLE_COLOR = 'white'
|
|
||||||
AXIS_LABEL_COLOR = 'white'
|
|
||||||
|
|
||||||
# Candlestick colors
|
|
||||||
CANDLE_UP_COLOR = '#089981' # Green
|
|
||||||
CANDLE_DOWN_COLOR = '#F23645' # Red
|
|
||||||
|
|
||||||
# Marker colors
|
|
||||||
MIN_COLOR = 'red'
|
|
||||||
MAX_COLOR = 'green'
|
|
||||||
|
|
||||||
# Line style colors
|
|
||||||
MIN_LINE_STYLE = 'g--' # Green dashed
|
|
||||||
MAX_LINE_STYLE = 'r--' # Red dashed
|
|
||||||
SMA7_LINE_STYLE = 'y-' # Yellow solid
|
|
||||||
SMA15_LINE_STYLE = 'm-' # Magenta solid
|
|
||||||
|
|
||||||
# SuperTrend colors
|
|
||||||
ST_COLOR_UP = 'g-'
|
|
||||||
ST_COLOR_DOWN = 'r-'
|
|
||||||
|
|
||||||
# Cache the calculation results by function parameters
|
|
||||||
@lru_cache(maxsize=32)
|
|
||||||
def cached_supertrend_calculation(period, multiplier, data_tuple):
|
|
||||||
# Convert tuple back to numpy arrays
|
|
||||||
high = np.array(data_tuple[0])
|
|
||||||
low = np.array(data_tuple[1])
|
|
||||||
close = np.array(data_tuple[2])
|
|
||||||
|
|
||||||
# Calculate TR and ATR using vectorized operations
|
|
||||||
tr = np.zeros_like(close)
|
|
||||||
tr[0] = high[0] - low[0]
|
|
||||||
hc_range = np.abs(high[1:] - close[:-1])
|
|
||||||
lc_range = np.abs(low[1:] - close[:-1])
|
|
||||||
hl_range = high[1:] - low[1:]
|
|
||||||
tr[1:] = np.maximum.reduce([hl_range, hc_range, lc_range])
|
|
||||||
|
|
||||||
# Use numpy's exponential moving average
|
|
||||||
atr = np.zeros_like(tr)
|
|
||||||
atr[0] = tr[0]
|
|
||||||
multiplier_ema = 2.0 / (period + 1)
|
|
||||||
for i in range(1, len(tr)):
|
|
||||||
atr[i] = (tr[i] * multiplier_ema) + (atr[i-1] * (1 - multiplier_ema))
|
|
||||||
|
|
||||||
# Calculate bands
|
|
||||||
upper_band = np.zeros_like(close)
|
|
||||||
lower_band = np.zeros_like(close)
|
|
||||||
for i in range(len(close)):
|
|
||||||
hl_avg = (high[i] + low[i]) / 2
|
|
||||||
upper_band[i] = hl_avg + (multiplier * atr[i])
|
|
||||||
lower_band[i] = hl_avg - (multiplier * atr[i])
|
|
||||||
|
|
||||||
final_upper = np.zeros_like(close)
|
|
||||||
final_lower = np.zeros_like(close)
|
|
||||||
supertrend = np.zeros_like(close)
|
|
||||||
trend = np.zeros_like(close)
|
|
||||||
final_upper[0] = upper_band[0]
|
|
||||||
final_lower[0] = lower_band[0]
|
|
||||||
if close[0] <= upper_band[0]:
|
|
||||||
supertrend[0] = upper_band[0]
|
|
||||||
trend[0] = -1
|
|
||||||
else:
|
|
||||||
supertrend[0] = lower_band[0]
|
|
||||||
trend[0] = 1
|
|
||||||
for i in range(1, len(close)):
|
|
||||||
if (upper_band[i] < final_upper[i-1]) or (close[i-1] > final_upper[i-1]):
|
|
||||||
final_upper[i] = upper_band[i]
|
|
||||||
else:
|
|
||||||
final_upper[i] = final_upper[i-1]
|
|
||||||
if (lower_band[i] > final_lower[i-1]) or (close[i-1] < final_lower[i-1]):
|
|
||||||
final_lower[i] = lower_band[i]
|
|
||||||
else:
|
|
||||||
final_lower[i] = final_lower[i-1]
|
|
||||||
if supertrend[i-1] == final_upper[i-1] and close[i] <= final_upper[i]:
|
|
||||||
supertrend[i] = final_upper[i]
|
|
||||||
trend[i] = -1
|
|
||||||
elif supertrend[i-1] == final_upper[i-1] and close[i] > final_upper[i]:
|
|
||||||
supertrend[i] = final_lower[i]
|
|
||||||
trend[i] = 1
|
|
||||||
elif supertrend[i-1] == final_lower[i-1] and close[i] >= final_lower[i]:
|
|
||||||
supertrend[i] = final_lower[i]
|
|
||||||
trend[i] = 1
|
|
||||||
elif supertrend[i-1] == final_lower[i-1] and close[i] < final_lower[i]:
|
|
||||||
supertrend[i] = final_upper[i]
|
|
||||||
trend[i] = -1
|
|
||||||
return {
|
|
||||||
'supertrend': supertrend,
|
|
||||||
'trend': trend,
|
|
||||||
'upper_band': final_upper,
|
|
||||||
'lower_band': final_lower
|
|
||||||
}
|
|
||||||
|
|
||||||
def calculate_supertrend_external(data, period, multiplier):
|
|
||||||
# Convert DataFrame columns to hashable tuples
|
|
||||||
high_tuple = tuple(data['high'])
|
|
||||||
low_tuple = tuple(data['low'])
|
|
||||||
close_tuple = tuple(data['close'])
|
|
||||||
|
|
||||||
# Call the cached function
|
|
||||||
return cached_supertrend_calculation(period, multiplier, (high_tuple, low_tuple, close_tuple))
|
|
||||||
|
|
||||||
def calculate_okx_fee(amount, is_maker=True):
|
|
||||||
fee_rate = 0.0008 if is_maker else 0.0010
|
|
||||||
return amount * fee_rate
|
|
||||||
|
|
||||||
class TrendDetectorSimple:
|
|
||||||
def __init__(self, data, verbose=False, display=False):
|
|
||||||
"""
|
|
||||||
Initialize the TrendDetectorSimple class.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- data: pandas DataFrame containing price data
|
|
||||||
- verbose: boolean, whether to display detailed logging information
|
|
||||||
- display: boolean, whether to enable display/plotting features
|
|
||||||
"""
|
|
||||||
|
|
||||||
self.data = data
|
|
||||||
self.verbose = verbose
|
|
||||||
self.display = display
|
|
||||||
|
|
||||||
# Only define display-related variables if display is True
|
|
||||||
if self.display:
|
|
||||||
# Plot style configuration
|
|
||||||
self.plot_style = 'dark_background'
|
|
||||||
self.bg_color = DARK_BG_COLOR
|
|
||||||
self.plot_size = (12, 8)
|
|
||||||
|
|
||||||
# Candlestick configuration
|
|
||||||
self.candle_width = 0.6
|
|
||||||
self.candle_up_color = CANDLE_UP_COLOR
|
|
||||||
self.candle_down_color = CANDLE_DOWN_COLOR
|
|
||||||
self.candle_alpha = 0.8
|
|
||||||
self.wick_width = 1
|
|
||||||
|
|
||||||
# Marker configuration
|
|
||||||
self.min_marker = '^'
|
|
||||||
self.min_color = MIN_COLOR
|
|
||||||
self.min_size = 100
|
|
||||||
self.max_marker = 'v'
|
|
||||||
self.max_color = MAX_COLOR
|
|
||||||
self.max_size = 100
|
|
||||||
self.marker_zorder = 100
|
|
||||||
|
|
||||||
# Line configuration
|
|
||||||
self.line_width = 1
|
|
||||||
self.min_line_style = MIN_LINE_STYLE
|
|
||||||
self.max_line_style = MAX_LINE_STYLE
|
|
||||||
self.sma7_line_style = SMA7_LINE_STYLE
|
|
||||||
self.sma15_line_style = SMA15_LINE_STYLE
|
|
||||||
|
|
||||||
# Text configuration
|
|
||||||
self.title_size = 14
|
|
||||||
self.title_color = TITLE_COLOR
|
|
||||||
self.axis_label_size = 12
|
|
||||||
self.axis_label_color = AXIS_LABEL_COLOR
|
|
||||||
|
|
||||||
# Legend configuration
|
|
||||||
self.legend_loc = 'best'
|
|
||||||
self.legend_bg_color = LEGEND_BG_COLOR
|
|
||||||
|
|
||||||
# Configure logging
|
|
||||||
logging.basicConfig(level=logging.INFO if verbose else logging.WARNING,
|
|
||||||
format='%(asctime)s - %(levelname)s - %(message)s')
|
|
||||||
self.logger = logging.getLogger('TrendDetectorSimple')
|
|
||||||
|
|
||||||
# Convert data to pandas DataFrame if it's not already
|
|
||||||
if not isinstance(self.data, pd.DataFrame):
|
|
||||||
if isinstance(self.data, list):
|
|
||||||
self.data = pd.DataFrame({'close': self.data})
|
|
||||||
else:
|
|
||||||
raise ValueError("Data must be a pandas DataFrame or a list")
|
|
||||||
|
|
||||||
def calculate_tr(self):
|
|
||||||
"""
|
|
||||||
Calculate True Range (TR) for the price data.
|
|
||||||
|
|
||||||
True Range is the greatest of:
|
|
||||||
1. Current high - current low
|
|
||||||
2. |Current high - previous close|
|
|
||||||
3. |Current low - previous close|
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
- Numpy array of TR values
|
|
||||||
"""
|
|
||||||
df = self.data.copy()
|
|
||||||
high = df['high'].values
|
|
||||||
low = df['low'].values
|
|
||||||
close = df['close'].values
|
|
||||||
|
|
||||||
tr = np.zeros_like(close)
|
|
||||||
tr[0] = high[0] - low[0] # First TR is just the first day's range
|
|
||||||
|
|
||||||
for i in range(1, len(close)):
|
|
||||||
# Current high - current low
|
|
||||||
hl_range = high[i] - low[i]
|
|
||||||
# |Current high - previous close|
|
|
||||||
hc_range = abs(high[i] - close[i-1])
|
|
||||||
# |Current low - previous close|
|
|
||||||
lc_range = abs(low[i] - close[i-1])
|
|
||||||
|
|
||||||
# TR is the maximum of these three values
|
|
||||||
tr[i] = max(hl_range, hc_range, lc_range)
|
|
||||||
|
|
||||||
return tr
|
|
||||||
|
|
||||||
def calculate_atr(self, period=14):
|
|
||||||
"""
|
|
||||||
Calculate Average True Range (ATR) for the price data.
|
|
||||||
|
|
||||||
ATR is the exponential moving average of the True Range over a specified period.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- period: int, the period for the ATR calculation (default: 14)
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
- Numpy array of ATR values
|
|
||||||
"""
|
|
||||||
|
|
||||||
tr = self.calculate_tr()
|
|
||||||
atr = np.zeros_like(tr)
|
|
||||||
|
|
||||||
# First ATR value is just the first TR
|
|
||||||
atr[0] = tr[0]
|
|
||||||
|
|
||||||
# Calculate exponential moving average (EMA) of TR
|
|
||||||
multiplier = 2.0 / (period + 1)
|
|
||||||
|
|
||||||
for i in range(1, len(tr)):
|
|
||||||
atr[i] = (tr[i] * multiplier) + (atr[i-1] * (1 - multiplier))
|
|
||||||
|
|
||||||
return atr
|
|
||||||
|
|
||||||
def detect_trends(self):
|
|
||||||
"""
|
|
||||||
Detect trends by identifying local minima and maxima in the price data
|
|
||||||
using scipy.signal.find_peaks.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- prominence: float, required prominence of peaks (relative to the price range)
|
|
||||||
- width: int, required width of peaks in data points
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
- DataFrame with columns for timestamps, prices, and trend indicators
|
|
||||||
- Dictionary containing analysis results including linear regression, SMAs, and SuperTrend indicators
|
|
||||||
"""
|
|
||||||
df = self.data
|
|
||||||
# close_prices = df['close'].values
|
|
||||||
|
|
||||||
# max_peaks, _ = find_peaks(close_prices)
|
|
||||||
# min_peaks, _ = find_peaks(-close_prices)
|
|
||||||
|
|
||||||
# df['is_min'] = False
|
|
||||||
# df['is_max'] = False
|
|
||||||
|
|
||||||
# for peak in max_peaks:
|
|
||||||
# df.at[peak, 'is_max'] = True
|
|
||||||
# for peak in min_peaks:
|
|
||||||
# df.at[peak, 'is_min'] = True
|
|
||||||
|
|
||||||
# result = df[['timestamp', 'close', 'is_min', 'is_max']].copy()
|
|
||||||
|
|
||||||
# Perform linear regression on min_peaks and max_peaks
|
|
||||||
# min_prices = df['close'].iloc[min_peaks].values
|
|
||||||
# max_prices = df['close'].iloc[max_peaks].values
|
|
||||||
|
|
||||||
# Linear regression for min peaks if we have at least 2 points
|
|
||||||
# min_slope, min_intercept, min_r_value, _, _ = stats.linregress(min_peaks, min_prices)
|
|
||||||
# Linear regression for max peaks if we have at least 2 points
|
|
||||||
# max_slope, max_intercept, max_r_value, _, _ = stats.linregress(max_peaks, max_prices)
|
|
||||||
|
|
||||||
# Calculate Simple Moving Averages (SMA) for 7 and 15 periods
|
|
||||||
# sma_7 = pd.Series(close_prices).rolling(window=7, min_periods=1).mean().values
|
|
||||||
# sma_15 = pd.Series(close_prices).rolling(window=15, min_periods=1).mean().values
|
|
||||||
|
|
||||||
analysis_results = {}
|
|
||||||
# analysis_results['linear_regression'] = {
|
|
||||||
# 'min': {
|
|
||||||
# 'slope': min_slope,
|
|
||||||
# 'intercept': min_intercept,
|
|
||||||
# 'r_squared': min_r_value ** 2
|
|
||||||
# },
|
|
||||||
# 'max': {
|
|
||||||
# 'slope': max_slope,
|
|
||||||
# 'intercept': max_intercept,
|
|
||||||
# 'r_squared': max_r_value ** 2
|
|
||||||
# }
|
|
||||||
# }
|
|
||||||
# analysis_results['sma'] = {
|
|
||||||
# '7': sma_7,
|
|
||||||
# '15': sma_15
|
|
||||||
# }
|
|
||||||
|
|
||||||
# Calculate SuperTrend indicators
|
|
||||||
supertrend_results_list = self._calculate_supertrend_indicators()
|
|
||||||
analysis_results['supertrend'] = supertrend_results_list
|
|
||||||
|
|
||||||
return analysis_results
|
|
||||||
|
|
||||||
def _calculate_supertrend_indicators(self):
|
|
||||||
"""
|
|
||||||
Calculate SuperTrend indicators with different parameter sets in parallel.
|
|
||||||
Returns:
|
|
||||||
- list, the SuperTrend results
|
|
||||||
"""
|
|
||||||
supertrend_params = [
|
|
||||||
{"period": 12, "multiplier": 3.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
|
|
||||||
{"period": 10, "multiplier": 1.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN},
|
|
||||||
{"period": 11, "multiplier": 2.0, "color_up": ST_COLOR_UP, "color_down": ST_COLOR_DOWN}
|
|
||||||
]
|
|
||||||
data = self.data.copy()
|
|
||||||
|
|
||||||
# For just 3 calculations, direct calculation might be faster than process pool
|
|
||||||
results = []
|
|
||||||
for p in supertrend_params:
|
|
||||||
result = calculate_supertrend_external(data, p["period"], p["multiplier"])
|
|
||||||
results.append(result)
|
|
||||||
|
|
||||||
supertrend_results_list = []
|
|
||||||
for params, result in zip(supertrend_params, results):
|
|
||||||
supertrend_results_list.append({
|
|
||||||
"results": result,
|
|
||||||
"params": params
|
|
||||||
})
|
|
||||||
return supertrend_results_list
|
|
||||||
|
|
||||||
def plot_trends(self, trend_data, analysis_results, view="both"):
|
|
||||||
"""
|
|
||||||
Plot the price data with detected trends using a candlestick chart.
|
|
||||||
Also plots SuperTrend indicators with three different parameter sets.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- trend_data: DataFrame, the output from detect_trends()
|
|
||||||
- analysis_results: Dictionary containing analysis results from detect_trends()
|
|
||||||
- view: str, one of 'both', 'trend', 'supertrend'; determines which plot(s) to display
|
|
||||||
|
|
||||||
Returns:
|
|
||||||
- None (displays the plot)
|
|
||||||
"""
|
|
||||||
if not self.display:
|
|
||||||
return # Do nothing if display is False
|
|
||||||
|
|
||||||
plt.style.use(self.plot_style)
|
|
||||||
|
|
||||||
if view == "both":
|
|
||||||
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(self.plot_size[0]*2, self.plot_size[1]))
|
|
||||||
else:
|
|
||||||
fig, ax = plt.subplots(figsize=self.plot_size)
|
|
||||||
ax1 = ax2 = None
|
|
||||||
if view == "trend":
|
|
||||||
ax1 = ax
|
|
||||||
elif view == "supertrend":
|
|
||||||
ax2 = ax
|
|
||||||
|
|
||||||
fig.patch.set_facecolor(self.bg_color)
|
|
||||||
if ax1: ax1.set_facecolor(self.bg_color)
|
|
||||||
if ax2: ax2.set_facecolor(self.bg_color)
|
|
||||||
|
|
||||||
df = self.data.copy()
|
|
||||||
|
|
||||||
if ax1:
|
|
||||||
self._plot_trend_analysis(ax1, df, trend_data, analysis_results)
|
|
||||||
|
|
||||||
if ax2:
|
|
||||||
self._plot_supertrend_analysis(ax2, df, analysis_results['supertrend'])
|
|
||||||
|
|
||||||
plt.tight_layout()
|
|
||||||
plt.show()
|
|
||||||
|
|
||||||
def _plot_candlesticks(self, ax, df):
|
|
||||||
"""
|
|
||||||
Plot candlesticks on the given axis.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to plot on
|
|
||||||
- df: pandas.DataFrame, the data to plot
|
|
||||||
"""
|
|
||||||
from matplotlib.patches import Rectangle
|
|
||||||
|
|
||||||
for i in range(len(df)):
|
|
||||||
# Get OHLC values for this candle
|
|
||||||
open_val = df['open'].iloc[i]
|
|
||||||
close_val = df['close'].iloc[i]
|
|
||||||
high_val = df['high'].iloc[i]
|
|
||||||
low_val = df['low'].iloc[i]
|
|
||||||
|
|
||||||
# Determine candle color
|
|
||||||
color = self.candle_up_color if close_val >= open_val else self.candle_down_color
|
|
||||||
|
|
||||||
# Plot candle body
|
|
||||||
body_height = abs(close_val - open_val)
|
|
||||||
bottom = min(open_val, close_val)
|
|
||||||
rect = Rectangle((i - self.candle_width/2, bottom), self.candle_width, body_height,
|
|
||||||
color=color, alpha=self.candle_alpha)
|
|
||||||
ax.add_patch(rect)
|
|
||||||
|
|
||||||
# Plot candle wicks
|
|
||||||
ax.plot([i, i], [low_val, high_val], color=color, linewidth=self.wick_width)
|
|
||||||
|
|
||||||
def _plot_trend_analysis(self, ax, df, trend_data, analysis_results):
|
|
||||||
"""
|
|
||||||
Plot trend analysis on the given axis.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to plot on
|
|
||||||
- df: pandas.DataFrame, the data to plot
|
|
||||||
- trend_data: pandas.DataFrame, the trend data
|
|
||||||
- analysis_results: dict, the analysis results
|
|
||||||
"""
|
|
||||||
# Draw candlesticks
|
|
||||||
self._plot_candlesticks(ax, df)
|
|
||||||
|
|
||||||
# Plot minima and maxima points
|
|
||||||
self._plot_min_max_points(ax, df, trend_data)
|
|
||||||
|
|
||||||
# Plot trend lines and moving averages
|
|
||||||
if analysis_results:
|
|
||||||
self._plot_trend_lines(ax, df, analysis_results)
|
|
||||||
|
|
||||||
# Configure the subplot
|
|
||||||
self._configure_subplot(ax, 'Price Chart with Trend Analysis', len(df))
|
|
||||||
|
|
||||||
def _plot_min_max_points(self, ax, df, trend_data):
|
|
||||||
"""
|
|
||||||
Plot minimum and maximum points on the given axis.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to plot on
|
|
||||||
- df: pandas.DataFrame, the data to plot
|
|
||||||
- trend_data: pandas.DataFrame, the trend data
|
|
||||||
"""
|
|
||||||
min_indices = trend_data.index[trend_data['is_min'] == True].tolist()
|
|
||||||
if min_indices:
|
|
||||||
min_y = [df['close'].iloc[i] for i in min_indices]
|
|
||||||
ax.scatter(min_indices, min_y, color=self.min_color, s=self.min_size,
|
|
||||||
marker=self.min_marker, label='Local Minima', zorder=self.marker_zorder)
|
|
||||||
|
|
||||||
max_indices = trend_data.index[trend_data['is_max'] == True].tolist()
|
|
||||||
if max_indices:
|
|
||||||
max_y = [df['close'].iloc[i] for i in max_indices]
|
|
||||||
ax.scatter(max_indices, max_y, color=self.max_color, s=self.max_size,
|
|
||||||
marker=self.max_marker, label='Local Maxima', zorder=self.marker_zorder)
|
|
||||||
|
|
||||||
def _plot_trend_lines(self, ax, df, analysis_results):
|
|
||||||
"""
|
|
||||||
Plot trend lines on the given axis.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to plot on
|
|
||||||
- df: pandas.DataFrame, the data to plot
|
|
||||||
- analysis_results: dict, the analysis results
|
|
||||||
"""
|
|
||||||
x_vals = np.arange(len(df))
|
|
||||||
|
|
||||||
# Minima regression line (support)
|
|
||||||
min_slope = analysis_results['linear_regression']['min']['slope']
|
|
||||||
min_intercept = analysis_results['linear_regression']['min']['intercept']
|
|
||||||
min_line = min_slope * x_vals + min_intercept
|
|
||||||
ax.plot(x_vals, min_line, self.min_line_style, linewidth=self.line_width,
|
|
||||||
label='Minima Regression')
|
|
||||||
|
|
||||||
# Maxima regression line (resistance)
|
|
||||||
max_slope = analysis_results['linear_regression']['max']['slope']
|
|
||||||
max_intercept = analysis_results['linear_regression']['max']['intercept']
|
|
||||||
max_line = max_slope * x_vals + max_intercept
|
|
||||||
ax.plot(x_vals, max_line, self.max_line_style, linewidth=self.line_width,
|
|
||||||
label='Maxima Regression')
|
|
||||||
|
|
||||||
# SMA-7 line
|
|
||||||
sma_7 = analysis_results['sma']['7']
|
|
||||||
ax.plot(x_vals, sma_7, self.sma7_line_style, linewidth=self.line_width,
|
|
||||||
label='SMA-7')
|
|
||||||
|
|
||||||
# SMA-15 line
|
|
||||||
sma_15 = analysis_results['sma']['15']
|
|
||||||
valid_idx_15 = ~np.isnan(sma_15)
|
|
||||||
ax.plot(x_vals[valid_idx_15], sma_15[valid_idx_15], self.sma15_line_style,
|
|
||||||
linewidth=self.line_width, label='SMA-15')
|
|
||||||
|
|
||||||
def _configure_subplot(self, ax, title, data_length):
|
|
||||||
"""
|
|
||||||
Configure the subplot with title, labels, limits, and legend.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to configure
|
|
||||||
- title: str, the title of the subplot
|
|
||||||
- data_length: int, the length of the data
|
|
||||||
"""
|
|
||||||
# Set title and labels
|
|
||||||
ax.set_title(title, fontsize=self.title_size, color=self.title_color)
|
|
||||||
ax.set_xlabel('Date', fontsize=self.axis_label_size, color=self.axis_label_color)
|
|
||||||
ax.set_ylabel('Price', fontsize=self.axis_label_size, color=self.axis_label_color)
|
|
||||||
|
|
||||||
# Set appropriate x-axis limits
|
|
||||||
ax.set_xlim(-0.5, data_length - 0.5)
|
|
||||||
|
|
||||||
# Add a legend
|
|
||||||
ax.legend(loc=self.legend_loc, facecolor=self.legend_bg_color)
|
|
||||||
|
|
||||||
def _plot_supertrend_analysis(self, ax, df, supertrend_results_list=None):
|
|
||||||
"""
|
|
||||||
Plot SuperTrend analysis on the given axis.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to plot on
|
|
||||||
- df: pandas.DataFrame, the data to plot
|
|
||||||
- supertrend_results_list: list, the SuperTrend results (optional)
|
|
||||||
"""
|
|
||||||
self._plot_candlesticks(ax, df)
|
|
||||||
self._plot_supertrend_lines(ax, df, supertrend_results_list, style='Both')
|
|
||||||
self._configure_subplot(ax, 'Multiple SuperTrend Indicators', len(df))
|
|
||||||
|
|
||||||
def _plot_supertrend_lines(self, ax, df, supertrend_results_list, style="Horizontal"):
|
|
||||||
"""
|
|
||||||
Plot SuperTrend lines on the given axis.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to plot on
|
|
||||||
- df: pandas.DataFrame, the data to plot
|
|
||||||
- supertrend_results_list: list, the SuperTrend results
|
|
||||||
"""
|
|
||||||
x_vals = np.arange(len(df))
|
|
||||||
|
|
||||||
if style == 'Horizontal' or style == 'Both':
|
|
||||||
if len(supertrend_results_list) != 3:
|
|
||||||
raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
|
|
||||||
|
|
||||||
trends = [st["results"]["trend"] for st in supertrend_results_list]
|
|
||||||
|
|
||||||
band_height = 0.02 * (df["high"].max() - df["low"].min())
|
|
||||||
y_base = df["low"].min() - band_height * 1.5
|
|
||||||
|
|
||||||
prev_color = None
|
|
||||||
for i in range(1, len(x_vals)):
|
|
||||||
t_vals = [t[i] for t in trends]
|
|
||||||
up_count = t_vals.count(1)
|
|
||||||
down_count = t_vals.count(-1)
|
|
||||||
|
|
||||||
if down_count == 3:
|
|
||||||
color = "red"
|
|
||||||
elif down_count == 2 and up_count == 1:
|
|
||||||
color = "orange"
|
|
||||||
elif down_count == 1 and up_count == 2:
|
|
||||||
color = "yellow"
|
|
||||||
elif up_count == 3:
|
|
||||||
color = "green"
|
|
||||||
else:
|
|
||||||
continue # skip if unknown or inconsistent values
|
|
||||||
|
|
||||||
ax.add_patch(Rectangle(
|
|
||||||
(x_vals[i-1], y_base),
|
|
||||||
1,
|
|
||||||
band_height,
|
|
||||||
color=color,
|
|
||||||
linewidth=0,
|
|
||||||
alpha=0.6
|
|
||||||
))
|
|
||||||
# Draw a vertical line at the change of color
|
|
||||||
if prev_color and prev_color != color:
|
|
||||||
ax.axvline(x_vals[i-1], color="grey", alpha=0.3, linewidth=1)
|
|
||||||
prev_color = color
|
|
||||||
|
|
||||||
ax.set_ylim(bottom=y_base - band_height * 0.5)
|
|
||||||
if style == 'Curves' or style == 'Both':
|
|
||||||
for st in supertrend_results_list:
|
|
||||||
params = st["params"]
|
|
||||||
results = st["results"]
|
|
||||||
supertrend = results["supertrend"]
|
|
||||||
trend = results["trend"]
|
|
||||||
|
|
||||||
# Plot SuperTrend line with color based on trend
|
|
||||||
for i in range(1, len(x_vals)):
|
|
||||||
if trend[i] == 1: # Uptrend
|
|
||||||
ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_up"], linewidth=self.line_width)
|
|
||||||
else: # Downtrend
|
|
||||||
ax.plot(x_vals[i-1:i+1], supertrend[i-1:i+1], params["color_down"], linewidth=self.line_width)
|
|
||||||
self._plot_metasupertrend_lines(ax, df, supertrend_results_list)
|
|
||||||
self._add_supertrend_legend(ax, supertrend_results_list)
|
|
||||||
|
|
||||||
def _plot_metasupertrend_lines(self, ax, df, supertrend_results_list):
|
|
||||||
"""
|
|
||||||
Plot a Meta SuperTrend line where all individual SuperTrends agree on trend.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to plot on
|
|
||||||
- df: pandas.DataFrame, the data to plot
|
|
||||||
- supertrend_results_list: list, each item contains SuperTrend 'results' and 'params'
|
|
||||||
"""
|
|
||||||
x_vals = np.arange(len(df))
|
|
||||||
|
|
||||||
if len(supertrend_results_list) != 3:
|
|
||||||
raise ValueError("Expected exactly 3 SuperTrend results for meta calculation")
|
|
||||||
|
|
||||||
trends = [st["results"]["trend"] for st in supertrend_results_list]
|
|
||||||
supertrends = [st["results"]["supertrend"] for st in supertrend_results_list]
|
|
||||||
params = supertrend_results_list[0]["params"] # Use first config for styling
|
|
||||||
|
|
||||||
trends_arr = np.stack(trends, axis=1)
|
|
||||||
meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]), trends_arr[:,0], 0)
|
|
||||||
|
|
||||||
for i in range(1, len(x_vals)):
|
|
||||||
t1, t2, t3 = trends[0][i], trends[1][i], trends[2][i]
|
|
||||||
if t1 == t2 == t3:
|
|
||||||
meta_trend = t1
|
|
||||||
# Average the 3 supertrend values
|
|
||||||
st_avg_prev = np.mean([s[i-1] for s in supertrends])
|
|
||||||
st_avg_curr = np.mean([s[i] for s in supertrends])
|
|
||||||
color = params["color_up"] if meta_trend == 1 else params["color_down"]
|
|
||||||
ax.plot(x_vals[i-1:i+1], [st_avg_prev, st_avg_curr], color, linewidth=self.line_width)
|
|
||||||
|
|
||||||
def _add_supertrend_legend(self, ax, supertrend_results_list):
|
|
||||||
"""
|
|
||||||
Add SuperTrend legend entries to the given axis.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- ax: matplotlib.axes.Axes, the axis to add legend entries to
|
|
||||||
- supertrend_results_list: list, the SuperTrend results
|
|
||||||
"""
|
|
||||||
for st in supertrend_results_list:
|
|
||||||
params = st["params"]
|
|
||||||
period = params["period"]
|
|
||||||
multiplier = params["multiplier"]
|
|
||||||
color_up = params["color_up"]
|
|
||||||
color_down = params["color_down"]
|
|
||||||
|
|
||||||
ax.plot([], [], color_up, linewidth=self.line_width,
|
|
||||||
label=f'ST (P:{period}, M:{multiplier}) Up')
|
|
||||||
ax.plot([], [], color_down, linewidth=self.line_width,
|
|
||||||
label=f'ST (P:{period}, M:{multiplier}) Down')
|
|
||||||
|
|
||||||
def backtest_meta_supertrend(self, min1_df, initial_usd=10000, stop_loss_pct=0.05, debug=False):
|
|
||||||
"""
|
|
||||||
Backtest a simple strategy using the meta supertrend (all three supertrends agree).
|
|
||||||
Buys when meta supertrend is positive, sells when negative, applies a percentage stop loss.
|
|
||||||
|
|
||||||
Parameters:
|
|
||||||
- min1_df: pandas DataFrame, 1-minute timeframe data for more accurate stop loss checking (optional)
|
|
||||||
- initial_usd: float, starting USD amount
|
|
||||||
- stop_loss_pct: float, stop loss as a fraction (e.g. 0.05 for 5%)
|
|
||||||
- debug: bool, whether to print debug info
|
|
||||||
"""
|
|
||||||
df = self.data.copy().reset_index(drop=True)
|
|
||||||
df['timestamp'] = pd.to_datetime(df['timestamp'])
|
|
||||||
|
|
||||||
# Get meta supertrend (all three agree)
|
|
||||||
supertrend_results_list = self._calculate_supertrend_indicators()
|
|
||||||
trends = [st['results']['trend'] for st in supertrend_results_list]
|
|
||||||
trends_arr = np.stack(trends, axis=1)
|
|
||||||
meta_trend = np.where((trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]),
|
|
||||||
trends_arr[:,0], 0)
|
|
||||||
|
|
||||||
position = 0 # 0 = no position, 1 = long
|
|
||||||
entry_price = 0
|
|
||||||
usd = initial_usd
|
|
||||||
coin = 0
|
|
||||||
trade_log = []
|
|
||||||
max_balance = initial_usd
|
|
||||||
drawdowns = []
|
|
||||||
trades = []
|
|
||||||
entry_time = None
|
|
||||||
current_trade_min1_start_idx = None
|
|
||||||
|
|
||||||
min1_df['timestamp'] = pd.to_datetime(min1_df.index)
|
|
||||||
|
|
||||||
for i in range(1, len(df)):
|
|
||||||
if i % 100 == 0 and debug:
|
|
||||||
self.logger.debug(f"Progress: {i}/{len(df)} rows processed.")
|
|
||||||
|
|
||||||
price_open = df['open'].iloc[i]
|
|
||||||
price_high = df['high'].iloc[i]
|
|
||||||
price_low = df['low'].iloc[i]
|
|
||||||
price_close = df['close'].iloc[i]
|
|
||||||
date = df['timestamp'].iloc[i]
|
|
||||||
prev_mt = meta_trend[i-1]
|
|
||||||
curr_mt = meta_trend[i]
|
|
||||||
|
|
||||||
# Check stop loss if in position
|
|
||||||
if position == 1:
|
|
||||||
stop_price = entry_price * (1 - stop_loss_pct)
|
|
||||||
|
|
||||||
if current_trade_min1_start_idx is None:
|
|
||||||
# First check after entry, find the entry point in 1-min data
|
|
||||||
current_trade_min1_start_idx = min1_df.index[min1_df.index >= entry_time][0]
|
|
||||||
|
|
||||||
# Get the end index for current check
|
|
||||||
current_min1_end_idx = min1_df.index[min1_df.index <= date][-1]
|
|
||||||
|
|
||||||
# Check all 1-minute candles in between for stop loss
|
|
||||||
min1_slice = min1_df.loc[current_trade_min1_start_idx:current_min1_end_idx]
|
|
||||||
if (min1_slice['low'] <= stop_price).any():
|
|
||||||
# Stop loss triggered, find the exact candle
|
|
||||||
stop_candle = min1_slice[min1_slice['low'] <= stop_price].iloc[0]
|
|
||||||
# More realistic fill: if open < stop, fill at open, else at stop
|
|
||||||
if stop_candle['open'] < stop_price:
|
|
||||||
sell_price = stop_candle['open']
|
|
||||||
else:
|
|
||||||
sell_price = stop_price
|
|
||||||
if debug:
|
|
||||||
print(f"STOP LOSS triggered: entry={entry_price}, stop={stop_price}, sell_price={sell_price}, entry_time={entry_time}, stop_time={stop_candle.name}")
|
|
||||||
btc_to_sell = coin
|
|
||||||
usd_gross = btc_to_sell * sell_price
|
|
||||||
exit_fee = calculate_okx_fee(usd_gross, is_maker=False) # taker fee
|
|
||||||
usd = usd_gross - exit_fee
|
|
||||||
trade_log.append({
|
|
||||||
'type': 'STOP',
|
|
||||||
'entry': entry_price,
|
|
||||||
'exit': sell_price,
|
|
||||||
'entry_time': entry_time,
|
|
||||||
'exit_time': stop_candle.name,
|
|
||||||
'fee_usd': exit_fee
|
|
||||||
})
|
|
||||||
coin = 0
|
|
||||||
position = 0
|
|
||||||
entry_price = 0
|
|
||||||
current_trade_min1_start_idx = None
|
|
||||||
continue
|
|
||||||
|
|
||||||
# Update the start index for next check
|
|
||||||
current_trade_min1_start_idx = current_min1_end_idx
|
|
||||||
|
|
||||||
# Entry: only if not in position and signal changes to 1
|
|
||||||
if position == 0 and prev_mt != 1 and curr_mt == 1:
|
|
||||||
# Buy at open, fee is charged in USD
|
|
||||||
entry_fee = calculate_okx_fee(usd, is_maker=False)
|
|
||||||
usd_after_fee = usd - entry_fee
|
|
||||||
coin = usd_after_fee / price_open
|
|
||||||
entry_price = price_open
|
|
||||||
entry_time = date
|
|
||||||
usd = 0
|
|
||||||
position = 1
|
|
||||||
current_trade_min1_start_idx = None # Will be set on first stop loss check
|
|
||||||
trade_log.append({
|
|
||||||
'type': 'BUY',
|
|
||||||
'entry': entry_price,
|
|
||||||
'exit': None,
|
|
||||||
'entry_time': entry_time,
|
|
||||||
'exit_time': None,
|
|
||||||
'fee_usd': entry_fee
|
|
||||||
})
|
|
||||||
|
|
||||||
# Exit: only if in position and signal changes from 1 to -1
|
|
||||||
elif position == 1 and prev_mt == 1 and curr_mt == -1:
|
|
||||||
# Sell at open, fee is charged in USD
|
|
||||||
btc_to_sell = coin
|
|
||||||
usd_gross = btc_to_sell * price_open
|
|
||||||
exit_fee = calculate_okx_fee(usd_gross, is_maker=False)
|
|
||||||
usd = usd_gross - exit_fee
|
|
||||||
trade_log.append({
|
|
||||||
'type': 'SELL',
|
|
||||||
'entry': entry_price,
|
|
||||||
'exit': price_open,
|
|
||||||
'entry_time': entry_time,
|
|
||||||
'exit_time': date,
|
|
||||||
'fee_usd': exit_fee
|
|
||||||
})
|
|
||||||
coin = 0
|
|
||||||
position = 0
|
|
||||||
entry_price = 0
|
|
||||||
current_trade_min1_start_idx = None
|
|
||||||
|
|
||||||
# Track drawdown
|
|
||||||
balance = usd if position == 0 else coin * price_close
|
|
||||||
if balance > max_balance:
|
|
||||||
max_balance = balance
|
|
||||||
drawdown = (max_balance - balance) / max_balance
|
|
||||||
drawdowns.append(drawdown)
|
|
||||||
|
|
||||||
# If still in position at end, sell at last close
|
|
||||||
if position == 1:
|
|
||||||
btc_to_sell = coin
|
|
||||||
usd_gross = btc_to_sell * df['close'].iloc[-1]
|
|
||||||
exit_fee = calculate_okx_fee(usd_gross, is_maker=False)
|
|
||||||
usd = usd_gross - exit_fee
|
|
||||||
trade_log.append({
|
|
||||||
'type': 'EOD',
|
|
||||||
'entry': entry_price,
|
|
||||||
'exit': df['close'].iloc[-1],
|
|
||||||
'entry_time': entry_time,
|
|
||||||
'exit_time': df['timestamp'].iloc[-1],
|
|
||||||
'fee_usd': exit_fee
|
|
||||||
})
|
|
||||||
coin = 0
|
|
||||||
position = 0
|
|
||||||
entry_price = 0
|
|
||||||
|
|
||||||
# Calculate statistics
|
|
||||||
final_balance = usd
|
|
||||||
n_trades = len(trade_log)
|
|
||||||
wins = [1 for t in trade_log if t['exit'] is not None and t['exit'] > t['entry']]
|
|
||||||
win_rate = len(wins) / n_trades if n_trades > 0 else 0
|
|
||||||
max_drawdown = max(drawdowns) if drawdowns else 0
|
|
||||||
avg_trade = np.mean([t['exit']/t['entry']-1 for t in trade_log if t['exit'] is not None]) if trade_log else 0
|
|
||||||
|
|
||||||
trades = []
|
|
||||||
total_fees_usd = 0.0
|
|
||||||
for trade in trade_log:
|
|
||||||
if trade['exit'] is not None:
|
|
||||||
profit_pct = (trade['exit'] - trade['entry']) / trade['entry']
|
|
||||||
else:
|
|
||||||
profit_pct = 0.0
|
|
||||||
trades.append({
|
|
||||||
'entry_time': trade['entry_time'],
|
|
||||||
'exit_time': trade['exit_time'],
|
|
||||||
'entry': trade['entry'],
|
|
||||||
'exit': trade['exit'],
|
|
||||||
'profit_pct': profit_pct,
|
|
||||||
'type': trade.get('type', 'SELL'),
|
|
||||||
'fee_usd': trade.get('fee_usd')
|
|
||||||
})
|
|
||||||
fee_usd = trade.get('fee_usd')
|
|
||||||
total_fees_usd += fee_usd
|
|
||||||
|
|
||||||
results = {
|
|
||||||
"initial_usd": initial_usd,
|
|
||||||
"final_usd": final_balance,
|
|
||||||
"n_trades": n_trades,
|
|
||||||
"win_rate": win_rate,
|
|
||||||
"max_drawdown": max_drawdown,
|
|
||||||
"avg_trade": avg_trade,
|
|
||||||
"trade_log": trade_log,
|
|
||||||
"trades": trades,
|
|
||||||
"total_fees_usd": total_fees_usd,
|
|
||||||
}
|
|
||||||
if n_trades > 0:
|
|
||||||
results["first_trade"] = {
|
|
||||||
"entry_time": trade_log[0]['entry_time'],
|
|
||||||
"entry": trade_log[0]['entry']
|
|
||||||
}
|
|
||||||
results["last_trade"] = {
|
|
||||||
"exit_time": trade_log[-1]['exit_time'],
|
|
||||||
"exit": trade_log[-1]['exit']
|
|
||||||
}
|
|
||||||
return results
|
|
||||||
|
|
||||||
@ -1,23 +0,0 @@
|
|||||||
import sys
|
|
||||||
import os
|
|
||||||
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
|
|
||||||
from taxes import Taxes
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
if len(sys.argv) < 2:
|
|
||||||
print("Usage: python apply_taxes_to_file.py <input_csv> [profit_col]")
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
input_csv = sys.argv[1]
|
|
||||||
profit_col = sys.argv[2] if len(sys.argv) > 2 else 'final_usd'
|
|
||||||
|
|
||||||
if not os.path.isfile(input_csv):
|
|
||||||
print(f"File not found: {input_csv}")
|
|
||||||
sys.exit(1)
|
|
||||||
|
|
||||||
base, ext = os.path.splitext(input_csv)
|
|
||||||
output_csv = f"{base}_taxed.csv"
|
|
||||||
|
|
||||||
taxes = Taxes() # Default 20% tax rate
|
|
||||||
taxes.add_taxes_to_results_csv(input_csv, output_csv, profit_col=profit_col)
|
|
||||||
print(f"Taxed file saved as: {output_csv}")
|
|
||||||
@ -169,15 +169,19 @@ class Storage:
|
|||||||
filtered_row = {k: v for k, v in row.items() if k in fieldnames}
|
filtered_row = {k: v for k, v in row.items() if k in fieldnames}
|
||||||
writer.writerow(filtered_row)
|
writer.writerow(filtered_row)
|
||||||
|
|
||||||
def write_results_combined(self, filename, fieldnames, rows):
|
def write_backtest_results(self, filename, fieldnames, rows, metadata_lines=None):
|
||||||
"""Write a combined results to a CSV file
|
"""Write a combined results to a CSV file
|
||||||
Args:
|
Args:
|
||||||
filename: filename to write to
|
filename: filename to write to
|
||||||
fieldnames: list of fieldnames
|
fieldnames: list of fieldnames
|
||||||
rows: list of rows
|
rows: list of rows
|
||||||
|
metadata_lines: optional list of strings to write as header comments
|
||||||
"""
|
"""
|
||||||
fname = os.path.join(self.results_dir, filename)
|
fname = os.path.join(self.results_dir, filename)
|
||||||
with open(fname, "w", newline="") as csvfile:
|
with open(fname, "w", newline="") as csvfile:
|
||||||
|
if metadata_lines:
|
||||||
|
for line in metadata_lines:
|
||||||
|
csvfile.write(f"{line}\n")
|
||||||
writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter='\t')
|
writer = csv.DictWriter(csvfile, fieldnames=fieldnames, delimiter='\t')
|
||||||
writer.writeheader()
|
writer.writeheader()
|
||||||
for row in rows:
|
for row in rows:
|
||||||
|
|||||||
120
main.py
120
main.py
@ -4,15 +4,14 @@ import logging
|
|||||||
import concurrent.futures
|
import concurrent.futures
|
||||||
import os
|
import os
|
||||||
import datetime
|
import datetime
|
||||||
import queue
|
import argparse
|
||||||
|
import json
|
||||||
|
import ast
|
||||||
|
|
||||||
from cycles.trend_detector_simple import TrendDetectorSimple
|
|
||||||
from cycles.taxes import Taxes
|
|
||||||
from cycles.utils.storage import Storage
|
from cycles.utils.storage import Storage
|
||||||
from cycles.utils.gsheets import GSheetBatchPusher
|
|
||||||
from cycles.utils.system import SystemUtils
|
from cycles.utils.system import SystemUtils
|
||||||
|
from cycles.backtest import Backtest
|
||||||
|
|
||||||
# Set up logging
|
|
||||||
logging.basicConfig(
|
logging.basicConfig(
|
||||||
level=logging.INFO,
|
level=logging.INFO,
|
||||||
format="%(asctime)s [%(levelname)s] %(message)s",
|
format="%(asctime)s [%(levelname)s] %(message)s",
|
||||||
@ -22,19 +21,17 @@ logging.basicConfig(
|
|||||||
]
|
]
|
||||||
)
|
)
|
||||||
|
|
||||||
# Global queue for batching Google Sheets updates
|
|
||||||
results_queue = queue.Queue()
|
|
||||||
|
|
||||||
def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd, debug=False):
|
def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd, debug=False):
|
||||||
"""Process the entire timeframe with all stop loss values (no monthly split)"""
|
"""Process the entire timeframe with all stop loss values (no monthly split)"""
|
||||||
df = df.copy().reset_index(drop=True)
|
df = df.copy().reset_index(drop=True)
|
||||||
trend_detector = TrendDetectorSimple(df, verbose=False)
|
|
||||||
|
|
||||||
results_rows = []
|
results_rows = []
|
||||||
trade_rows = []
|
trade_rows = []
|
||||||
|
|
||||||
for stop_loss_pct in stop_loss_pcts:
|
for stop_loss_pct in stop_loss_pcts:
|
||||||
results = trend_detector.backtest_meta_supertrend(
|
results = Backtest.run(
|
||||||
min1_df,
|
min1_df,
|
||||||
|
df,
|
||||||
initial_usd=initial_usd,
|
initial_usd=initial_usd,
|
||||||
stop_loss_pct=stop_loss_pct,
|
stop_loss_pct=stop_loss_pct,
|
||||||
debug=debug
|
debug=debug
|
||||||
@ -100,9 +97,10 @@ def process_timeframe_data(min1_df, df, stop_loss_pcts, rule_name, initial_usd,
|
|||||||
print("Large loss trade:", trade)
|
print("Large loss trade:", trade)
|
||||||
return results_rows, trade_rows
|
return results_rows, trade_rows
|
||||||
|
|
||||||
def process_timeframe(timeframe_info, debug=False):
|
def process(timeframe_info, debug=False):
|
||||||
"""Process a single (timeframe, stop_loss_pct) combination (no monthly split)"""
|
"""Process a single (timeframe, stop_loss_pct) combination (no monthly split)"""
|
||||||
rule, data_1min, stop_loss_pct, initial_usd = timeframe_info
|
rule, data_1min, stop_loss_pct, initial_usd = timeframe_info
|
||||||
|
|
||||||
if rule == "1T":
|
if rule == "1T":
|
||||||
df = data_1min.copy()
|
df = data_1min.copy()
|
||||||
else:
|
else:
|
||||||
@ -114,7 +112,6 @@ def process_timeframe(timeframe_info, debug=False):
|
|||||||
'volume': 'sum'
|
'volume': 'sum'
|
||||||
}).dropna()
|
}).dropna()
|
||||||
df = df.reset_index()
|
df = df.reset_index()
|
||||||
# Only process one stop loss
|
|
||||||
results_rows, all_trade_rows = process_timeframe_data(data_1min, df, [stop_loss_pct], rule, initial_usd, debug=debug)
|
results_rows, all_trade_rows = process_timeframe_data(data_1min, df, [stop_loss_pct], rule, initial_usd, debug=debug)
|
||||||
return results_rows, all_trade_rows
|
return results_rows, all_trade_rows
|
||||||
|
|
||||||
@ -166,32 +163,69 @@ def get_nearest_price(df, target_date):
|
|||||||
return nearest_time, price
|
return nearest_time, price
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
# Configuration
|
debug = True
|
||||||
# start_date = '2022-01-01'
|
|
||||||
# stop_date = '2023-01-01'
|
|
||||||
start_date = '2024-05-15'
|
|
||||||
stop_date = '2025-05-15'
|
|
||||||
|
|
||||||
initial_usd = 10000
|
parser = argparse.ArgumentParser(description="Run backtest with config file.")
|
||||||
|
parser.add_argument("config", type=str, nargs="?", help="Path to config JSON file.")
|
||||||
|
args = parser.parse_args()
|
||||||
|
|
||||||
debug = False
|
# Default values (from config.json)
|
||||||
|
default_config = {
|
||||||
timestamp = datetime.datetime.now().strftime("%Y%m%d%H%M")
|
"start_date": "2024-05-15",
|
||||||
|
"stop_date": datetime.datetime.today().strftime('%Y-%m-%d'),
|
||||||
|
"initial_usd": 10000,
|
||||||
|
"timeframes": ["1D"],
|
||||||
|
"stop_loss_pcts": [0.01, 0.02, 0.03],
|
||||||
|
}
|
||||||
|
|
||||||
|
if args.config:
|
||||||
|
with open(args.config, 'r') as f:
|
||||||
|
config = json.load(f)
|
||||||
|
else:
|
||||||
|
print("No config file provided. Please enter the following values (press Enter to use default):")
|
||||||
|
|
||||||
|
start_date = input(f"Start date [{default_config['start_date']}]: ") or default_config['start_date']
|
||||||
|
stop_date = input(f"Stop date [{default_config['stop_date']}]: ") or default_config['stop_date']
|
||||||
|
|
||||||
|
initial_usd_str = input(f"Initial USD [{default_config['initial_usd']}]: ") or str(default_config['initial_usd'])
|
||||||
|
initial_usd = float(initial_usd_str)
|
||||||
|
|
||||||
|
timeframes_str = input(f"Timeframes (comma separated) [{', '.join(default_config['timeframes'])}]: ") or ','.join(default_config['timeframes'])
|
||||||
|
timeframes = [tf.strip() for tf in timeframes_str.split(',') if tf.strip()]
|
||||||
|
|
||||||
|
stop_loss_pcts_str = input(f"Stop loss pcts (comma separated) [{', '.join(str(x) for x in default_config['stop_loss_pcts'])}]: ") or ','.join(str(x) for x in default_config['stop_loss_pcts'])
|
||||||
|
stop_loss_pcts = [float(x.strip()) for x in stop_loss_pcts_str.split(',') if x.strip()]
|
||||||
|
|
||||||
|
config = {
|
||||||
|
'start_date': start_date,
|
||||||
|
'stop_date': stop_date,
|
||||||
|
'initial_usd': initial_usd,
|
||||||
|
'timeframes': timeframes,
|
||||||
|
'stop_loss_pcts': stop_loss_pcts,
|
||||||
|
}
|
||||||
|
|
||||||
|
# Use config values
|
||||||
|
start_date = config['start_date']
|
||||||
|
stop_date = config['stop_date']
|
||||||
|
initial_usd = config['initial_usd']
|
||||||
|
timeframes = config['timeframes']
|
||||||
|
stop_loss_pcts = config['stop_loss_pcts']
|
||||||
|
|
||||||
|
timestamp = datetime.datetime.now().strftime("%Y_%m_%d_%H_%M")
|
||||||
|
|
||||||
storage = Storage(logging=logging)
|
storage = Storage(logging=logging)
|
||||||
system_utils = SystemUtils(logging=logging)
|
system_utils = SystemUtils(logging=logging)
|
||||||
|
|
||||||
timeframes = ["1D"]
|
|
||||||
stop_loss_pcts = [0.01, 0.02, 0.03]
|
|
||||||
|
|
||||||
# Load data once
|
|
||||||
data_1min = storage.load_data('btcusd_1-min_data.csv', start_date, stop_date)
|
data_1min = storage.load_data('btcusd_1-min_data.csv', start_date, stop_date)
|
||||||
|
|
||||||
nearest_start_time, start_price = get_nearest_price(data_1min, start_date)
|
nearest_start_time, start_price = get_nearest_price(data_1min, start_date)
|
||||||
nearest_stop_time, stop_price = get_nearest_price(data_1min, stop_date)
|
nearest_stop_time, stop_price = get_nearest_price(data_1min, stop_date)
|
||||||
|
|
||||||
logging.info(f"Price at start_date ({start_date}) [nearest timestamp: {nearest_start_time}]: {start_price}")
|
metadata_lines = [
|
||||||
logging.info(f"Price at stop_date ({stop_date}) [nearest timestamp: {nearest_stop_time}]: {stop_price}")
|
f"Start date\t{start_date}\tPrice\t{start_price}",
|
||||||
|
f"Stop date\t{stop_date}\tPrice\t{stop_price}",
|
||||||
|
f"Initial USD\t{initial_usd}"
|
||||||
|
]
|
||||||
|
|
||||||
tasks = [
|
tasks = [
|
||||||
(name, data_1min, stop_loss_pct, initial_usd)
|
(name, data_1min, stop_loss_pct, initial_usd)
|
||||||
@ -201,29 +235,35 @@ if __name__ == "__main__":
|
|||||||
|
|
||||||
workers = system_utils.get_optimal_workers()
|
workers = system_utils.get_optimal_workers()
|
||||||
|
|
||||||
# Process tasks with optimized concurrency
|
if debug:
|
||||||
with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
|
|
||||||
futures = {executor.submit(process_timeframe, task, debug): task for task in tasks}
|
|
||||||
all_results_rows = []
|
all_results_rows = []
|
||||||
all_trade_rows = []
|
all_trade_rows = []
|
||||||
for future in concurrent.futures.as_completed(futures):
|
for task in tasks:
|
||||||
results, trades = future.result()
|
results, trades = process(task, debug)
|
||||||
if results or trades:
|
if results or trades:
|
||||||
all_results_rows.extend(results)
|
all_results_rows.extend(results)
|
||||||
all_trade_rows.extend(trades)
|
all_trade_rows.extend(trades)
|
||||||
|
else:
|
||||||
|
with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as executor:
|
||||||
|
futures = {executor.submit(process, task, debug): task for task in tasks}
|
||||||
|
all_results_rows = []
|
||||||
|
all_trade_rows = []
|
||||||
|
|
||||||
# Write all results to a single CSV file
|
for future in concurrent.futures.as_completed(futures):
|
||||||
combined_filename = os.path.join(f"{timestamp}_backtest_combined.csv")
|
results, trades = future.result()
|
||||||
combined_fieldnames = [
|
|
||||||
|
if results or trades:
|
||||||
|
all_results_rows.extend(results)
|
||||||
|
all_trade_rows.extend(trades)
|
||||||
|
|
||||||
|
backtest_filename = os.path.join(f"{timestamp}_backtest.csv")
|
||||||
|
backtest_fieldnames = [
|
||||||
"timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
|
"timeframe", "stop_loss_pct", "n_trades", "n_stop_loss", "win_rate",
|
||||||
"max_drawdown", "avg_trade", "profit_ratio", "final_usd", "total_fees_usd"
|
"max_drawdown", "avg_trade", "profit_ratio", "final_usd", "total_fees_usd"
|
||||||
]
|
]
|
||||||
storage.write_results_combined(combined_filename, combined_fieldnames, all_results_rows)
|
storage.write_backtest_results(backtest_filename, backtest_fieldnames, all_results_rows, metadata_lines)
|
||||||
|
|
||||||
# Now, group all_trade_rows by (timeframe, stop_loss_pct)
|
trades_fieldnames = ["entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"]
|
||||||
trades_fieldnames = [
|
|
||||||
"entry_time", "exit_time", "entry_price", "exit_price", "profit_pct", "type", "fee_usd"
|
|
||||||
]
|
|
||||||
storage.write_trades(all_trade_rows, trades_fieldnames)
|
storage.write_trades(all_trade_rows, trades_fieldnames)
|
||||||
|
|
||||||
|
|
||||||
Loading…
x
Reference in New Issue
Block a user