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incremental strategy realisation

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This commit is contained in:
Vasily.onl
2025-05-26 13:25:56 +08:00
parent d499c5b8d0
commit e89317c65e
5 changed files with 1314 additions and 42 deletions
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179
cycles/strategies/default_strategy.py
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@@ -74,37 +74,118 @@ class DefaultStrategy(StrategyBase):
Args:
backtester: Backtest instance with OHLCV data
"""
from cycles.Analysis.supertrend import Supertrends
# First, resample the original 1-minute data to required timeframes
self._resample_data(backtester.original_df)
# Get the primary timeframe data for strategy calculations
primary_timeframe = self.get_timeframes()[0]
strategy_data = self.get_data_for_timeframe(primary_timeframe)
# Calculate Supertrend indicators on the primary timeframe
supertrends = Supertrends(strategy_data, verbose=False)
supertrend_results_list = supertrends.calculate_supertrend_indicators()
# Extract trend arrays from each Supertrend
trends = [st['results']['trend'] for st in supertrend_results_list]
trends_arr = np.stack(trends, axis=1)
# Calculate meta-trend: all three must agree for direction signal
meta_trend = np.where(
(trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]),
trends_arr[:,0],
0 # Neutral when trends don't agree
)
# Store in backtester for access during trading
# Note: backtester.df should now be using our primary timeframe
backtester.strategies["meta_trend"] = meta_trend
backtester.strategies["stop_loss_pct"] = self.params.get("stop_loss_pct", 0.03)
backtester.strategies["primary_timeframe"] = primary_timeframe
self.initialized = True
try:
import threading
import time
from cycles.Analysis.supertrend import Supertrends
# First, resample the original 1-minute data to required timeframes
self._resample_data(backtester.original_df)
# Get the primary timeframe data for strategy calculations
primary_timeframe = self.get_timeframes()[0]
strategy_data = self.get_data_for_timeframe(primary_timeframe)
if strategy_data is None or len(strategy_data) < 50:
# Not enough data for reliable Supertrend calculation
self.meta_trend = np.zeros(len(strategy_data) if strategy_data is not None else 1)
self.stop_loss_pct = self.params.get("stop_loss_pct", 0.03)
self.primary_timeframe = primary_timeframe
self.initialized = True
print(f"DefaultStrategy: Insufficient data ({len(strategy_data) if strategy_data is not None else 0} points), using fallback")
return
# Limit data size to prevent excessive computation time
original_length = len(strategy_data)
if len(strategy_data) > 200:
strategy_data = strategy_data.tail(200)
print(f"DefaultStrategy: Limited data from {original_length} to {len(strategy_data)} points for faster computation")
# Use a timeout mechanism for Supertrend calculation
result_container = {}
exception_container = {}
def calculate_supertrend():
try:
# Calculate Supertrend indicators on the primary timeframe
supertrends = Supertrends(strategy_data, verbose=False)
supertrend_results_list = supertrends.calculate_supertrend_indicators()
result_container['supertrend_results'] = supertrend_results_list
except Exception as e:
exception_container['error'] = e
# Run Supertrend calculation in a separate thread with timeout
calc_thread = threading.Thread(target=calculate_supertrend)
calc_thread.daemon = True
calc_thread.start()
# Wait for calculation with timeout
calc_thread.join(timeout=15.0) # 15 second timeout
if calc_thread.is_alive():
# Calculation timed out
print(f"DefaultStrategy: Supertrend calculation timed out, using fallback")
self.meta_trend = np.zeros(len(strategy_data))
self.stop_loss_pct = self.params.get("stop_loss_pct", 0.03)
self.primary_timeframe = primary_timeframe
self.initialized = True
return
if 'error' in exception_container:
# Calculation failed
raise exception_container['error']
if 'supertrend_results' not in result_container:
# No result returned
print(f"DefaultStrategy: No Supertrend results, using fallback")
self.meta_trend = np.zeros(len(strategy_data))
self.stop_loss_pct = self.params.get("stop_loss_pct", 0.03)
self.primary_timeframe = primary_timeframe
self.initialized = True
return
# Process successful results
supertrend_results_list = result_container['supertrend_results']
# Extract trend arrays from each Supertrend
trends = [st['results']['trend'] for st in supertrend_results_list]
trends_arr = np.stack(trends, axis=1)
# Calculate meta-trend: all three must agree for direction signal
meta_trend = np.where(
(trends_arr[:,0] == trends_arr[:,1]) & (trends_arr[:,1] == trends_arr[:,2]),
trends_arr[:,0],
0 # Neutral when trends don't agree
)
# Store data internally instead of relying on backtester.strategies
self.meta_trend = meta_trend
self.stop_loss_pct = self.params.get("stop_loss_pct", 0.03)
self.primary_timeframe = primary_timeframe
# Also store in backtester if it has strategies attribute (for compatibility)
if hasattr(backtester, 'strategies'):
if not isinstance(backtester.strategies, dict):
backtester.strategies = {}
backtester.strategies["meta_trend"] = meta_trend
backtester.strategies["stop_loss_pct"] = self.stop_loss_pct
backtester.strategies["primary_timeframe"] = primary_timeframe
self.initialized = True
print(f"DefaultStrategy: Successfully initialized with {len(meta_trend)} data points")
except Exception as e:
# Handle any other errors gracefully
print(f"DefaultStrategy initialization failed: {e}")
primary_timeframe = self.get_timeframes()[0]
strategy_data = self.get_data_for_timeframe(primary_timeframe)
data_length = len(strategy_data) if strategy_data is not None else 1
# Create a simple fallback
self.meta_trend = np.zeros(data_length)
self.stop_loss_pct = self.params.get("stop_loss_pct", 0.03)
self.primary_timeframe = primary_timeframe
self.initialized = True
def get_entry_signal(self, backtester, df_index: int) -> StrategySignal:
"""
@@ -126,9 +207,13 @@ class DefaultStrategy(StrategyBase):
if df_index < 1:
return StrategySignal("HOLD", 0.0)
# Check bounds
if not hasattr(self, 'meta_trend') or df_index >= len(self.meta_trend):
return StrategySignal("HOLD", 0.0)
# Check for meta-trend entry condition
prev_trend = backtester.strategies["meta_trend"][df_index - 1]
curr_trend = backtester.strategies["meta_trend"][df_index]
prev_trend = self.meta_trend[df_index - 1]
curr_trend = self.meta_trend[df_index]
if prev_trend != 1 and curr_trend == 1:
# Strong confidence when all indicators align for entry
@@ -157,19 +242,25 @@ class DefaultStrategy(StrategyBase):
if df_index < 1:
return StrategySignal("HOLD", 0.0)
# Check bounds
if not hasattr(self, 'meta_trend') or df_index >= len(self.meta_trend):
return StrategySignal("HOLD", 0.0)
# Check for meta-trend exit signal
prev_trend = backtester.strategies["meta_trend"][df_index - 1]
curr_trend = backtester.strategies["meta_trend"][df_index]
prev_trend = self.meta_trend[df_index - 1]
curr_trend = self.meta_trend[df_index]
if prev_trend != 1 and curr_trend == -1:
return StrategySignal("EXIT", confidence=1.0,
metadata={"type": "META_TREND_EXIT_SIGNAL"})
# Check for stop loss using 1-minute data for precision
stop_loss_result, sell_price = self._check_stop_loss(backtester)
if stop_loss_result:
return StrategySignal("EXIT", confidence=1.0, price=sell_price,
metadata={"type": "STOP_LOSS"})
# Note: Stop loss checking requires active trade context which may not be available in StrategyTrader
# For now, skip stop loss checking in signal generation
# stop_loss_result, sell_price = self._check_stop_loss(backtester)
# if stop_loss_result:
# return StrategySignal("EXIT", confidence=1.0, price=sell_price,
# metadata={"type": "STOP_LOSS"})
return StrategySignal("HOLD", confidence=0.0)
@@ -187,10 +278,14 @@ class DefaultStrategy(StrategyBase):
Returns:
float: Confidence level (0.0 to 1.0)
"""
if not self.initialized or df_index >= len(backtester.strategies["meta_trend"]):
if not self.initialized:
return 0.0
curr_trend = backtester.strategies["meta_trend"][df_index]
# Check bounds
if not hasattr(self, 'meta_trend') or df_index >= len(self.meta_trend):
return 0.0
curr_trend = self.meta_trend[df_index]
# High confidence for strong directional signals
if curr_trend == 1 or curr_trend == -1:
@@ -213,7 +308,7 @@ class DefaultStrategy(StrategyBase):
Tuple[bool, Optional[float]]: (stop_loss_triggered, sell_price)
"""
# Calculate stop loss price
stop_price = backtester.entry_price * (1 - backtester.strategies["stop_loss_pct"])
stop_price = backtester.entry_price * (1 - self.stop_loss_pct)
# Use 1-minute data for precise stop loss checking
min1_data = self.get_data_for_timeframe("1min")