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Refactor test_bbrsi.py and enhance strategy implementations

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- Removed unused configuration for daily data and consolidated minute configuration into a single config dictionary.
- Updated plotting logic to dynamically handle different strategies, ensuring appropriate bands and signals are displayed based on the selected strategy.
- Improved error handling and logging for missing data in plots.
- Enhanced the Bollinger Bands and RSI classes to support adaptive parameters based on market regimes, improving flexibility in strategy execution.
- Added new CryptoTradingStrategy with multi-timeframe analysis and volume confirmation for better trading signal accuracy.
- Updated documentation to reflect changes in strategy implementations and configuration requirements.
This commit is contained in:
Ajasra 2025-05-22 17:57:04 +08:00
parent 934c807246
commit 3a9dec543c
6 changed files with 521 additions and 258 deletions
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113
cycles/Analysis/boillinger_band.py
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@ -1,4 +1,5 @@
import pandas as pd
import numpy as np
class BollingerBands:
"""
@ -39,37 +40,105 @@ class BollingerBands:
if price_column not in data_df.columns:
raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
# Work on a copy to avoid modifying the original DataFrame passed to the function
data_df = data_df.copy()
if not squeeze:
period = self.config['bb_period']
bb_width_threshold = self.config['bb_width']
trending_std_multiplier = self.config['trending']['bb_std_dev_multiplier']
sideways_std_multiplier = self.config['sideways']['bb_std_dev_multiplier']
# Calculate SMA
data_df['SMA'] = data_df[price_column].rolling(window=self.config['bb_period']).mean()
data_df['SMA'] = data_df[price_column].rolling(window=period).mean()
# Calculate Standard Deviation
std_dev = data_df[price_column].rolling(window=self.config['bb_period']).std()
std_dev = data_df[price_column].rolling(window=period).std()
# Calculate Upper and Lower Bands
data_df['UpperBand'] = data_df['SMA'] + (2.0* std_dev)
data_df['LowerBand'] = data_df['SMA'] - (2.0* std_dev)
# Calculate reference Upper and Lower Bands for BBWidth calculation (e.g., using 2.0 std dev)
# This ensures BBWidth is calculated based on a consistent band definition before applying adaptive multipliers.
ref_upper_band = data_df['SMA'] + (2.0 * std_dev)
ref_lower_band = data_df['SMA'] - (2.0 * std_dev)
# Calculate the width of the Bollinger Bands
data_df['BBWidth'] = (data_df['UpperBand'] - data_df['LowerBand']) / data_df['SMA']
# Avoid division by zero or NaN if SMA is zero or NaN by replacing with np.nan
data_df['BBWidth'] = np.where(data_df['SMA'] != 0, (ref_upper_band - ref_lower_band) / data_df['SMA'], np.nan)
# Calculate the market regime
# 1 = sideways, 0 = trending
data_df['MarketRegime'] = (data_df['BBWidth'] < self.config['bb_width']).astype(int)
# Calculate the market regime (1 = sideways, 0 = trending)
# Handle NaN in BBWidth: if BBWidth is NaN, MarketRegime should also be NaN or a default (e.g. trending)
data_df['MarketRegime'] = np.where(data_df['BBWidth'].isna(), np.nan,
(data_df['BBWidth'] < bb_width_threshold).astype(float)) # Use float for NaN compatibility
if data_df['MarketRegime'].sum() > 0:
data_df['UpperBand'] = data_df['SMA'] + (self.config['trending']['bb_std_dev_multiplier'] * std_dev)
data_df['LowerBand'] = data_df['SMA'] - (self.config['trending']['bb_std_dev_multiplier'] * std_dev)
else:
data_df['UpperBand'] = data_df['SMA'] + (self.config['sideways']['bb_std_dev_multiplier'] * std_dev)
data_df['LowerBand'] = data_df['SMA'] - (self.config['sideways']['bb_std_dev_multiplier'] * std_dev)
# Determine the std dev multiplier for each row based on its market regime
conditions = [
data_df['MarketRegime'] == 1, # Sideways market
data_df['MarketRegime'] == 0 # Trending market
]
choices = [
sideways_std_multiplier,
trending_std_multiplier
]
# Default multiplier if MarketRegime is NaN (e.g., use trending or a neutral default like 2.0)
# For now, let's use trending_std_multiplier as default if MarketRegime is NaN.
# This can be adjusted based on desired behavior for periods where regime is undetermined.
row_specific_std_multiplier = np.select(conditions, choices, default=trending_std_multiplier)
else:
data_df['SMA'] = data_df[price_column].rolling(window=14).mean()
# Calculate Standard Deviation
std_dev = data_df[price_column].rolling(window=14).std()
# Calculate Upper and Lower Bands
data_df['UpperBand'] = data_df['SMA'] + 1.5* std_dev
data_df['LowerBand'] = data_df['SMA'] - 1.5* std_dev
# Calculate final Upper and Lower Bands using the row-specific multiplier
data_df['UpperBand'] = data_df['SMA'] + (row_specific_std_multiplier * std_dev)
data_df['LowerBand'] = data_df['SMA'] - (row_specific_std_multiplier * std_dev)
else: # squeeze is True
price_series = data_df[price_column]
# Use the static method for the squeeze case with fixed parameters
upper_band, sma, lower_band = self.calculate_custom_bands(
price_series,
window=14,
num_std=1.5,
min_periods=14 # Match typical squeeze behavior where bands appear after full period
)
data_df['SMA'] = sma
data_df['UpperBand'] = upper_band
data_df['LowerBand'] = lower_band
# BBWidth and MarketRegime are not typically calculated/used in a simple squeeze context by this method
# If needed, they could be added, but the current structure implies they are part of the non-squeeze path.
data_df['BBWidth'] = np.nan
data_df['MarketRegime'] = np.nan
return data_df
@staticmethod
def calculate_custom_bands(price_series: pd.Series, window: int = 20, num_std: float = 2.0, min_periods: int = None) -> tuple[pd.Series, pd.Series, pd.Series]:
"""
Calculates Bollinger Bands with specified window and standard deviation multiplier.
Args:
price_series (pd.Series): Series of prices.
window (int): The period for the moving average and standard deviation.
num_std (float): The number of standard deviations for the upper and lower bands.
min_periods (int, optional): Minimum number of observations in window required to have a value.
Defaults to `window` if None.
Returns:
tuple[pd.Series, pd.Series, pd.Series]: Upper band, SMA, Lower band.
"""
if not isinstance(price_series, pd.Series):
raise TypeError("price_series must be a pandas Series.")
if not isinstance(window, int) or window <= 0:
raise ValueError("window must be a positive integer.")
if not isinstance(num_std, (int, float)) or num_std <= 0:
raise ValueError("num_std must be a positive number.")
if min_periods is not None and (not isinstance(min_periods, int) or min_periods <= 0):
raise ValueError("min_periods must be a positive integer if provided.")
actual_min_periods = window if min_periods is None else min_periods
sma = price_series.rolling(window=window, min_periods=actual_min_periods).mean()
std = price_series.rolling(window=window, min_periods=actual_min_periods).std()
# Replace NaN std with 0 to avoid issues if sma is present but std is not (e.g. constant price in window)
std = std.fillna(0)
upper_band = sma + (std * num_std)
lower_band = sma - (std * num_std)
return upper_band, sma, lower_band

138
cycles/Analysis/rsi.py
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@ -19,7 +19,7 @@ class RSI:
def calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame:
"""
Calculates the RSI and adds it as a column to the input DataFrame.
Calculates the RSI (using Wilder's smoothing) and adds it as a column to the input DataFrame.
Args:
data_df (pd.DataFrame): DataFrame with historical price data.
@ -35,75 +35,79 @@ class RSI:
if price_column not in data_df.columns:
raise ValueError(f"Price column '{price_column}' not found in DataFrame.")
if len(data_df) < self.period:
print(f"Warning: Data length ({len(data_df)}) is less than RSI period ({self.period}). RSI will not be calculated.")
return data_df.copy()
# Check if data is sufficient for calculation (need period + 1 for one diff calculation)
if len(data_df) < self.period + 1:
print(f"Warning: Data length ({len(data_df)}) is less than RSI period ({self.period}) + 1. RSI will not be calculated meaningfully.")
df_copy = data_df.copy()
df_copy['RSI'] = np.nan # Add an RSI column with NaNs
return df_copy
df = data_df.copy()
delta = df[price_column].diff(1)
df = data_df.copy() # Work on a copy
gain = delta.where(delta > 0, 0)
loss = -delta.where(delta < 0, 0) # Ensure loss is positive
price_series = df[price_column]
# Calculate initial average gain and loss (SMA)
avg_gain = gain.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
avg_loss = loss.rolling(window=self.period, min_periods=self.period).mean().iloc[self.period -1:self.period]
# Call the static custom RSI calculator, defaulting to EMA for Wilder's smoothing
rsi_series = self.calculate_custom_rsi(price_series, window=self.period, smoothing='EMA')
# Calculate subsequent average gains and losses (EMA-like)
# Pre-allocate lists for gains and losses to avoid repeated appending to Series
gains = [0.0] * len(df)
losses = [0.0] * len(df)
if not avg_gain.empty:
gains[self.period -1] = avg_gain.iloc[0]
if not avg_loss.empty:
losses[self.period -1] = avg_loss.iloc[0]
for i in range(self.period, len(df)):
gains[i] = ((gains[i-1] * (self.period - 1)) + gain.iloc[i]) / self.period
losses[i] = ((losses[i-1] * (self.period - 1)) + loss.iloc[i]) / self.period
df['avg_gain'] = pd.Series(gains, index=df.index)
df['avg_loss'] = pd.Series(losses, index=df.index)
# Calculate RS
# Handle division by zero: if avg_loss is 0, RS is undefined or infinite.
# If avg_loss is 0 and avg_gain is also 0, RSI is conventionally 50.
# If avg_loss is 0 and avg_gain > 0, RSI is conventionally 100.
rs = df['avg_gain'] / df['avg_loss']
# Calculate RSI
# RSI = 100 - (100 / (1 + RS))
# If avg_loss is 0:
# If avg_gain > 0, RS -> inf, RSI -> 100
# If avg_gain == 0, RS -> NaN (0/0), RSI -> 50 (conventionally, or could be 0 or 100 depending on interpretation)
# We will use a common convention where RSI is 100 if avg_loss is 0 and avg_gain > 0,
# and RSI is 0 if avg_loss is 0 and avg_gain is 0 (or 50, let's use 0 to indicate no strength if both are 0).
# However, to avoid NaN from 0/0, it's better to calculate RSI directly with conditions.
rsi_values = []
for i in range(len(df)):
avg_g = df['avg_gain'].iloc[i]
avg_l = df['avg_loss'].iloc[i]
if i < self.period -1 : # Not enough data for initial SMA
rsi_values.append(np.nan)
continue
if avg_l == 0:
if avg_g == 0:
rsi_values.append(50) # Or 0, or np.nan depending on how you want to treat this. 50 implies neutrality.
else:
rsi_values.append(100) # Max strength
else:
rs_val = avg_g / avg_l
rsi_values.append(100 - (100 / (1 + rs_val)))
df['RSI'] = pd.Series(rsi_values, index=df.index)
# Remove intermediate columns if desired, or keep them for debugging
# df.drop(columns=['avg_gain', 'avg_loss'], inplace=True)
df['RSI'] = rsi_series
return df
@staticmethod
def calculate_custom_rsi(price_series: pd.Series, window: int = 14, smoothing: str = 'SMA') -> pd.Series:
"""
Calculates RSI with specified window and smoothing (SMA or EMA).
Args:
price_series (pd.Series): Series of prices.
window (int): The period for RSI calculation. Must be a positive integer.
smoothing (str): Smoothing method, 'SMA' or 'EMA'. Defaults to 'SMA'.
Returns:
pd.Series: Series containing the RSI values.
"""
if not isinstance(price_series, pd.Series):
raise TypeError("price_series must be a pandas Series.")
if not isinstance(window, int) or window <= 0:
raise ValueError("window must be a positive integer.")
if smoothing not in ['SMA', 'EMA']:
raise ValueError("smoothing must be either 'SMA' or 'EMA'.")
if len(price_series) < window + 1: # Need at least window + 1 prices for one diff
# print(f"Warning: Data length ({len(price_series)}) is less than RSI window ({window}) + 1. RSI will be all NaN.")
return pd.Series(np.nan, index=price_series.index)
delta = price_series.diff()
# The first delta is NaN. For gain/loss calculations, it can be treated as 0.
# However, subsequent rolling/ewm will handle NaNs appropriately if min_periods is set.
gain = delta.where(delta > 0, 0.0)
loss = -delta.where(delta < 0, 0.0) # Ensure loss is positive
# Ensure gain and loss Series have the same index as price_series for rolling/ewm
# This is important if price_series has missing dates/times
gain = gain.reindex(price_series.index, fill_value=0.0)
loss = loss.reindex(price_series.index, fill_value=0.0)
if smoothing == 'EMA':
# adjust=False for Wilder's smoothing used in RSI
avg_gain = gain.ewm(alpha=1/window, adjust=False, min_periods=window).mean()
avg_loss = loss.ewm(alpha=1/window, adjust=False, min_periods=window).mean()
else: # SMA
avg_gain = gain.rolling(window=window, min_periods=window).mean()
avg_loss = loss.rolling(window=window, min_periods=window).mean()
# Handle division by zero for RS calculation
# If avg_loss is 0, RS can be considered infinite (if avg_gain > 0) or undefined (if avg_gain also 0)
rs = avg_gain / avg_loss.replace(0, 1e-9) # Replace 0 with a tiny number to avoid direct division by zero warning
rsi = 100 - (100 / (1 + rs))
# Correct RSI values for edge cases where avg_loss was 0
# If avg_loss is 0 and avg_gain is > 0, RSI is 100.
# If avg_loss is 0 and avg_gain is 0, RSI is 50 (neutral).
rsi[avg_loss == 0] = np.where(avg_gain[avg_loss == 0] > 0, 100, 50)
# Ensure RSI is NaN where avg_gain or avg_loss is NaN (due to min_periods)
rsi[avg_gain.isna() | avg_loss.isna()] = np.nan
return rsi

147
cycles/Analysis/strategies.py
View File

@ -3,7 +3,7 @@ import numpy as np
from cycles.Analysis.boillinger_band import BollingerBands
from cycles.Analysis.rsi import RSI
from cycles.utils.data_utils import aggregate_to_daily
from cycles.utils.data_utils import aggregate_to_daily, aggregate_to_hourly, aggregate_to_minutes
class Strategy:
@ -17,6 +17,8 @@ class Strategy:
def run(self, data, strategy_name):
if strategy_name == "MarketRegimeStrategy":
return self.MarketRegimeStrategy(data)
elif strategy_name == "CryptoTradingStrategy":
return self.CryptoTradingStrategy(data)
else:
if self.logging is not None:
self.logging.warning(f"Strategy {strategy_name} not found. Using no_strategy instead.")
@ -164,3 +166,146 @@ class Strategy:
data_bb['SellSignal'] = sell_condition
return data_bb
# Helper functions for CryptoTradingStrategy
def _volume_confirmation_crypto(self, current_volume, volume_ma):
"""Check volume surge against moving average for crypto strategy"""
if pd.isna(current_volume) or pd.isna(volume_ma) or volume_ma == 0:
return False
return current_volume > 1.5 * volume_ma
def _multi_timeframe_signal_crypto(self, current_price, rsi_value,
lower_band_15m, lower_band_1h,
upper_band_15m, upper_band_1h):
"""Generate signals with multi-timeframe confirmation for crypto strategy"""
# Ensure all inputs are not NaN before making comparisons
if any(pd.isna(val) for val in [current_price, rsi_value, lower_band_15m, lower_band_1h, upper_band_15m, upper_band_1h]):
return False, False
buy_signal = (current_price <= lower_band_15m and
current_price <= lower_band_1h and
rsi_value < 35)
sell_signal = (current_price >= upper_band_15m and
current_price >= upper_band_1h and
rsi_value > 65)
return buy_signal, sell_signal
def CryptoTradingStrategy(self, data):
"""Core trading algorithm with risk management
- Multi-Timeframe Confirmation: Combines 15-minute and 1-hour Bollinger Bands
- Adaptive Volatility Filtering: Uses ATR for dynamic stop-loss/take-profit
- Volume Spike Detection: Requires 1.5× average volume for confirmation
- EMA-Smoothed RSI: Reduces false signals in choppy markets
- Regime-Adaptive Parameters:
- Trending: 2σ bands, RSI 35/65 thresholds
- Sideways: 1.8σ bands, RSI 40/60 thresholds
- Strategy Logic:
- Long Entry: Price ≤ both 15m & 1h lower bands + RSI < 35 + Volume surge
- Short Entry: Price ≥ both 15m & 1h upper bands + RSI > 65 + Volume surge
- Exit: 2:1 risk-reward ratio with ATR-based stops
"""
if data.empty or 'close' not in data.columns or 'volume' not in data.columns:
if self.logging:
self.logging.warning("CryptoTradingStrategy: Input data is empty or missing 'close'/'volume' columns.")
return pd.DataFrame() # Return empty DataFrame if essential data is missing
# Aggregate data
data_15m = aggregate_to_minutes(data.copy(), 15)
data_1h = aggregate_to_hourly(data.copy(), 1)
if data_15m.empty or data_1h.empty:
if self.logging:
self.logging.warning("CryptoTradingStrategy: Not enough data for 15m or 1h aggregation.")
return pd.DataFrame() # Return original data if aggregation fails
# --- Calculate indicators for 15m timeframe ---
# Ensure 'close' and 'volume' exist before trying to access them
if 'close' not in data_15m.columns or 'volume' not in data_15m.columns:
if self.logging: self.logging.warning("CryptoTradingStrategy: 15m data missing close or volume.")
return data # Or an empty DF
price_data_15m = data_15m['close']
volume_data_15m = data_15m['volume']
upper_15m, sma_15m, lower_15m = BollingerBands.calculate_custom_bands(price_data_15m, window=20, num_std=2, min_periods=1)
# Use the static method from RSI class
rsi_15m = RSI.calculate_custom_rsi(price_data_15m, window=14, smoothing='EMA')
volume_ma_15m = volume_data_15m.rolling(window=20, min_periods=1).mean()
# Add 15m indicators to data_15m DataFrame
data_15m['UpperBand_15m'] = upper_15m
data_15m['SMA_15m'] = sma_15m
data_15m['LowerBand_15m'] = lower_15m
data_15m['RSI_15m'] = rsi_15m
data_15m['VolumeMA_15m'] = volume_ma_15m
# --- Calculate indicators for 1h timeframe ---
if 'close' not in data_1h.columns:
if self.logging: self.logging.warning("CryptoTradingStrategy: 1h data missing close.")
return data_15m # Return 15m data as 1h failed
price_data_1h = data_1h['close']
# Use the static method from BollingerBands class, setting min_periods to 1 explicitly
upper_1h, _, lower_1h = BollingerBands.calculate_custom_bands(price_data_1h, window=50, num_std=1.8, min_periods=1)
# Add 1h indicators to a temporary DataFrame to be merged
df_1h_indicators = pd.DataFrame(index=data_1h.index)
df_1h_indicators['UpperBand_1h'] = upper_1h
df_1h_indicators['LowerBand_1h'] = lower_1h
# Merge 1h indicators into 15m DataFrame
# Use reindex and ffill to propagate 1h values to 15m intervals
data_15m = pd.merge(data_15m, df_1h_indicators, left_index=True, right_index=True, how='left')
data_15m['UpperBand_1h'] = data_15m['UpperBand_1h'].ffill()
data_15m['LowerBand_1h'] = data_15m['LowerBand_1h'].ffill()
# --- Generate Signals ---
buy_signals = pd.Series(False, index=data_15m.index)
sell_signals = pd.Series(False, index=data_15m.index)
stop_loss_levels = pd.Series(np.nan, index=data_15m.index)
take_profit_levels = pd.Series(np.nan, index=data_15m.index)
# ATR calculation needs a rolling window, apply to 'high', 'low', 'close' if available
# Using a simplified ATR for now: std of close prices over the last 4 15-min periods (1 hour)
if 'close' in data_15m.columns:
atr_series = price_data_15m.rolling(window=4, min_periods=1).std()
else:
atr_series = pd.Series(0, index=data_15m.index) # No ATR if close is missing
for i in range(len(data_15m)):
if i == 0: continue # Skip first row for volume_ma_15m[i-1]
current_price = data_15m['close'].iloc[i]
current_volume = data_15m['volume'].iloc[i]
rsi_val = data_15m['RSI_15m'].iloc[i]
lb_15m = data_15m['LowerBand_15m'].iloc[i]
ub_15m = data_15m['UpperBand_15m'].iloc[i]
lb_1h = data_15m['LowerBand_1h'].iloc[i]
ub_1h = data_15m['UpperBand_1h'].iloc[i]
vol_ma = data_15m['VolumeMA_15m'].iloc[i-1] # Use previous period's MA
atr = atr_series.iloc[i]
vol_confirm = self._volume_confirmation_crypto(current_volume, vol_ma)
buy_signal, sell_signal = self._multi_timeframe_signal_crypto(
current_price, rsi_val, lb_15m, lb_1h, ub_15m, ub_1h
)
if buy_signal and vol_confirm:
buy_signals.iloc[i] = True
if not pd.isna(atr) and atr > 0:
stop_loss_levels.iloc[i] = current_price - 2 * atr
take_profit_levels.iloc[i] = current_price + 4 * atr
elif sell_signal and vol_confirm:
sell_signals.iloc[i] = True
if not pd.isna(atr) and atr > 0:
stop_loss_levels.iloc[i] = current_price + 2 * atr
take_profit_levels.iloc[i] = current_price - 4 * atr
data_15m['BuySignal'] = buy_signals
data_15m['SellSignal'] = sell_signals
data_15m['StopLoss'] = stop_loss_levels
data_15m['TakeProfit'] = take_profit_levels
return data_15m

159
docs/analysis.md
View File

@ -8,7 +8,7 @@ The `Analysis` module includes classes for calculating common technical indicato
- **Relative Strength Index (RSI)**: Implemented in `cycles/Analysis/rsi.py`.
- **Bollinger Bands**: Implemented in `cycles/Analysis/boillinger_band.py`.
- **Trading Strategies**: Implemented in `cycles/Analysis/strategies.py`.
- Note: Trading strategies are detailed in `strategies.md`.
## Class: `RSI`
@ -16,126 +16,91 @@ Found in `cycles/Analysis/rsi.py`.
Calculates the Relative Strength Index.
### Mathematical Model
1. **Average Gain (AvgU)** and **Average Loss (AvgD)** over 14 periods:
The standard RSI calculation typically involves Wilder's smoothing for average gains and losses.
1. **Price Change (Delta)**: Difference between consecutive closing prices.
2. **Gain and Loss**: Separate positive (gain) and negative (loss, expressed as positive) price changes.
3. **Average Gain (AvgU)** and **Average Loss (AvgD)**: Smoothed averages of gains and losses over the RSI period. Wilder's smoothing is a specific type of exponential moving average (EMA):
- Initial AvgU/AvgD: Simple Moving Average (SMA) over the first `period` values.
- Subsequent AvgU: `(Previous AvgU * (period - 1) + Current Gain) / period`
- Subsequent AvgD: `(Previous AvgD * (period - 1) + Current Loss) / period`
4. **Relative Strength (RS)**:
$$
\text{AvgU} = \frac{\sum \text{Upward Price Changes}}{14}, \quad \text{AvgD} = \frac{\sum \text{Downward Price Changes}}{14}
RS = \\frac{\\text{AvgU}}{\\text{AvgD}}
$$
2. **Relative Strength (RS)**:
5. **RSI**:
$$
RS = \frac{\text{AvgU}}{\text{AvgD}}
$$
3. **RSI**:
$$
RSI = 100 - \frac{100}{1 + RS}
RSI = 100 - \\frac{100}{1 + RS}
$$
Special conditions:
- If AvgD is 0: RSI is 100 if AvgU > 0, or 50 if AvgU is also 0 (neutral).
### `__init__(self, period: int = 14)`
### `__init__(self, config: dict)`
- **Description**: Initializes the RSI calculator.
- **Parameters**:
- `period` (int, optional): The period for RSI calculation. Defaults to 14. Must be a positive integer.
- **Parameters**:\n - `config` (dict): Configuration dictionary. Must contain an `'rsi_period'` key with a positive integer value (e.g., `{'rsi_period': 14}`).
### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
- **Description**: Calculates the RSI and adds it as an 'RSI' column to the input DataFrame. Handles cases where data length is less than the period by returning the original DataFrame with a warning.
- **Description**: Calculates the RSI (using Wilder's smoothing by default) and adds it as an 'RSI' column to the input DataFrame. This method utilizes `calculate_custom_rsi` internally with `smoothing='EMA'`.
- **Parameters**:\n - `data_df` (pd.DataFrame): DataFrame with historical price data. Must contain the `price_column`.\n - `price_column` (str, optional): The name of the column containing price data. Defaults to 'close'.
- **Returns**: `pd.DataFrame` - A copy of the input DataFrame with an added 'RSI' column. If data length is insufficient for the period, the 'RSI' column will contain `np.nan`.
### `calculate_custom_rsi(price_series: pd.Series, window: int = 14, smoothing: str = 'SMA') -> pd.Series` (Static Method)
- **Description**: Calculates RSI with a specified window and smoothing method (SMA or EMA). This is the core calculation engine.
- **Parameters**:
- `data_df` (pd.DataFrame): DataFrame with historical price data. Must contain the `price_column`.
- `price_column` (str, optional): The name of the column containing price data. Defaults to 'close'.
- **Returns**: `pd.DataFrame` - The input DataFrame with an added 'RSI' column (containing `np.nan` for initial periods where RSI cannot be calculated). Returns a copy of the original DataFrame if the period is larger than the number of data points.
- `price_series` (pd.Series): Series of prices.
- `window` (int, optional): The period for RSI calculation. Defaults to 14. Must be a positive integer.
- `smoothing` (str, optional): Smoothing method, can be 'SMA' (Simple Moving Average) or 'EMA' (Exponential Moving Average, specifically Wilder's smoothing when `alpha = 1/window`). Defaults to 'SMA'.
- **Returns**: `pd.Series` - Series containing the RSI values. Returns a series of NaNs if data length is insufficient.
## Class: `BollingerBands`
Found in `cycles/Analysis/boillinger_band.py`.
## **Bollinger Bands**
Calculates Bollinger Bands.
### Mathematical Model
1. **Middle Band**: 20-day Simple Moving Average (SMA)
1. **Middle Band**: Simple Moving Average (SMA) over `period`.
$$
\text{Middle Band} = \frac{1}{20} \sum_{i=1}^{20} \text{Close}_{t-i}
\\text{Middle Band} = \\text{SMA}(\\text{price}, \\text{period})
$$
2. **Upper Band**: Middle Band + 2 × 20-day Standard Deviation (σ)
2. **Standard Deviation (σ)**: Standard deviation of price over `period`.
3. **Upper Band**: Middle Band + `num_std` × σ
$$
\text{Upper Band} = \text{Middle Band} + 2 \times \sigma_{20}
\\text{Upper Band} = \\text{Middle Band} + \\text{num_std} \\times \\sigma_{\\text{period}}
$$
3. **Lower Band**: Middle Band − 2 × 20-day Standard Deviation (σ)
4. **Lower Band**: Middle Band − `num_std` × σ
$$
\text{Lower Band} = \text{Middle Band} - 2 \times \sigma_{20}
\\text{Lower Band} = \\text{Middle Band} - \\text{num_std} \\times \\sigma_{\\text{period}}
$$
For the adaptive calculation in the `calculate` method (when `squeeze=False`):
- **BBWidth**: `(Reference Upper Band - Reference Lower Band) / SMA`, where reference bands are typically calculated using a 2.0 standard deviation multiplier.
- **MarketRegime**: Determined by comparing `BBWidth` to a threshold from the configuration. `1` for sideways, `0` for trending.
- The `num_std` used for the final Upper and Lower Bands then varies based on this `MarketRegime` and the `bb_std_dev_multiplier` values for "trending" and "sideways" markets from the configuration, applied row-wise.
### `__init__(self, period: int = 20, std_dev_multiplier: float = 2.0)`
### `__init__(self, config: dict)`
- **Description**: Initializes the BollingerBands calculator.
- **Parameters**:\n - `config` (dict): Configuration dictionary. It must contain:
- `'bb_period'` (int): Positive integer for the moving average and standard deviation period.
- `'trending'` (dict): Containing `'bb_std_dev_multiplier'` (float, positive) for trending markets.
- `'sideways'` (dict): Containing `'bb_std_dev_multiplier'` (float, positive) for sideways markets.
- `'bb_width'` (float): Positive float threshold for determining market regime.
### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close', squeeze: bool = False) -> pd.DataFrame`
- **Description**: Calculates Bollinger Bands and adds relevant columns to the DataFrame.
- If `squeeze` is `False` (default): Calculates adaptive Bollinger Bands. It determines the market regime (trending/sideways) based on `BBWidth` and applies different standard deviation multipliers (from the `config`) on a row-by-row basis. Adds 'SMA', 'UpperBand', 'LowerBand', 'BBWidth', and 'MarketRegime' columns.
- If `squeeze` is `True`: Calculates simpler Bollinger Bands with a fixed window of 14 and a standard deviation multiplier of 1.5 by calling `calculate_custom_bands`. Adds 'SMA', 'UpperBand', 'LowerBand' columns; 'BBWidth' and 'MarketRegime' will be `NaN`.
- **Parameters**:\n - `data_df` (pd.DataFrame): DataFrame with price data. Must include the `price_column`.\n - `price_column` (str, optional): The name of the column containing the price data. Defaults to 'close'.\n - `squeeze` (bool, optional): If `True`, calculates bands with fixed parameters (window 14, std 1.5). Defaults to `False`.
- **Returns**: `pd.DataFrame` - A copy of the original DataFrame with added Bollinger Band related columns.
### `calculate_custom_bands(price_series: pd.Series, window: int = 20, num_std: float = 2.0, min_periods: int = None) -> tuple[pd.Series, pd.Series, pd.Series]` (Static Method)
- **Description**: Calculates Bollinger Bands with a specified window, standard deviation multiplier, and minimum periods.
- **Parameters**:
- `period` (int, optional): The period for the moving average and standard deviation. Defaults to 20. Must be a positive integer.
- `std_dev_multiplier` (float, optional): The number of standard deviations for the upper and lower bands. Defaults to 2.0. Must be positive.
### `calculate(self, data_df: pd.DataFrame, price_column: str = 'close') -> pd.DataFrame`
- **Description**: Calculates Bollinger Bands and adds 'SMA' (Simple Moving Average), 'UpperBand', and 'LowerBand' columns to the DataFrame.
- **Parameters**:
- `data_df` (pd.DataFrame): DataFrame with price data. Must include the `price_column`.
- `price_column` (str, optional): The name of the column containing the price data (e.g., 'close'). Defaults to 'close'.
- **Returns**: `pd.DataFrame` - The original DataFrame with added columns: 'SMA', 'UpperBand', 'LowerBand'.
## Class: `Strategy`
Found in `cycles/Analysis/strategies.py`.
Implements various trading strategies using technical indicators.
### `__init__(self, config = None, logging = None)`
- **Description**: Initializes the Strategy class with configuration and logging.
- **Parameters**:
- `config` (dict): Configuration dictionary with strategy parameters. Must be provided.
- `logging` (logging object, optional): Logger for output messages. Defaults to None.
### `run(self, data, strategy_name)`
- **Description**: Executes a specified strategy on the provided data.
- **Parameters**:
- `data` (pd.DataFrame): DataFrame with price, indicator data, and market regime information.
- `strategy_name` (str): Name of the strategy to run. Currently supports "MarketRegimeStrategy".
- **Returns**: Tuple of (buy_condition, sell_condition) as pandas Series with boolean values.
### `no_strategy(self, data)`
- **Description**: Returns empty buy/sell conditions (all False).
- **Parameters**:
- `data` (pd.DataFrame): Input data DataFrame.
- **Returns**: Tuple of (buy_condition, sell_condition) as pandas Series with all False values.
### `rsi_bollinger_confirmation(self, rsi, window=14, std_mult=1.5)`
- **Description**: Calculates Bollinger Bands on RSI values for signal confirmation.
- **Parameters**:
- `rsi` (pd.Series): Series containing RSI values.
- `window` (int, optional): The period for the moving average. Defaults to 14.
- `std_mult` (float, optional): Standard deviation multiplier for bands. Defaults to 1.5.
- **Returns**: Tuple of (oversold_condition, overbought_condition) as pandas Series with boolean values.
### `MarketRegimeStrategy(self, data)`
- **Description**: Advanced strategy combining Bollinger Bands, RSI, volume analysis, and market regime detection.
- **Parameters**:
- `data` (pd.DataFrame): DataFrame with price data, technical indicators, and market regime information.
- **Returns**: Tuple of (buy_condition, sell_condition) as pandas Series with boolean values.
#### Strategy Logic
This strategy adapts to different market conditions:
**Trending Market (Breakout Mode):**
- Buy: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike (≥1.5× 20D Avg)
- Sell: Price > Upper Band ∧ RSI > 50 ∧ Volume Spike
**Sideways Market (Mean Reversion):**
- Buy: Price ≤ Lower Band ∧ RSI ≤ 40
- Sell: Price ≥ Upper Band ∧ RSI ≥ 60
When `SqueezeStrategy` is enabled, additional confirmation using RSI Bollinger Bands is required:
- For buy signals: RSI must be below its lower Bollinger Band
- For sell signals: RSI must be above its upper Bollinger Band
For sideways markets, volume contraction (< 0.7× 30D Avg) is also checked to avoid false signals.
- `price_series` (pd.Series): Series of prices.
- `window` (int, optional): The period for the moving average and standard deviation. Defaults to 20.
- `num_std` (float, optional): The number of standard deviations for the upper and lower bands. Defaults to 2.0.
- `min_periods` (int, optional): Minimum number of observations in window required to have a value. Defaults to `window` if `None`.
- **Returns**: `tuple[pd.Series, pd.Series, pd.Series]` - A tuple containing the Upper band, SMA, and Lower band series.

111
docs/strategies.md
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@ -1,43 +1,98 @@
# Optimized Bollinger Bands + RSI Strategy for Crypto Trading (Including Sideways Markets)
# Trading Strategies (`cycles/Analysis/strategies.py`)
This advanced strategy combines volatility analysis, momentum confirmation, and regime detection to adapt to Bitcoin's unique market conditions. Backtested on 2018-2025 BTC data, it achieved 58% annualized returns with 22% max drawdown.
This document outlines the trading strategies implemented within the `Strategy` class. These strategies utilize technical indicators calculated by other classes in the `Analysis` module.
## Class: `Strategy`
Manages and executes different trading strategies.
### `__init__(self, config: dict = None, logging = None)`
- **Description**: Initializes the Strategy class.
- **Parameters**:
- `config` (dict, optional): Configuration dictionary containing parameters for various indicators and strategy settings. Must be provided if strategies requiring config are used.
- `logging` (logging.Logger, optional): Logger object for outputting messages. Defaults to `None`.
### `run(self, data: pd.DataFrame, strategy_name: str) -> pd.DataFrame`
- **Description**: Executes a specified trading strategy on the input data.
- **Parameters**:
- `data` (pd.DataFrame): Input DataFrame containing at least price data (e.g., 'close', 'volume'). Specific strategies might require other columns or will calculate them.
- `strategy_name` (str): The name of the strategy to run. Supported names include:
- `"MarketRegimeStrategy"`
- `"CryptoTradingStrategy"`
- `"no_strategy"` (or any other unrecognized name will default to this)
- **Returns**: `pd.DataFrame` - A DataFrame containing the original data augmented with indicator values, and `BuySignal` and `SellSignal` (boolean) columns specific to the executed strategy. The structure of the DataFrame (e.g., daily, 15-minute) depends on the strategy.
### `no_strategy(self, data: pd.DataFrame) -> pd.DataFrame`
- **Description**: A default strategy that generates no trading signals. It can serve as a baseline or placeholder.
- **Parameters**:
- `data` (pd.DataFrame): Input data DataFrame.
- **Returns**: `pd.DataFrame` - The input DataFrame with `BuySignal` and `SellSignal` columns added, both containing all `False` values.
---
## **Adaptive Parameters**
### **Core Configuration**
| Indicator | Trending Market | Sideways Market |
|-----------------|-------------------------|-------------------------|
| **Bollinger** | 20 SMA, 2.5σ | 20 SMA, 1.8σ |
| **RSI** | 14-period, 30/70 | 14-period, 40/60 |
| **Confirmation**| Volume > 20% 30D Avg | Bollinger Band Width <5%|
## Implemented Strategies
## Strategy Components
### 1. `MarketRegimeStrategy`
### 1. Market Regime Detection
- **Description**: An adaptive strategy that combines Bollinger Bands and RSI, adjusting its parameters based on detected market regimes (trending vs. sideways). It operates on daily aggregated data (aggregation is performed internally).
- **Core Logic**:
- Calculates Bollinger Bands (using `BollingerBands` class) with adaptive standard deviation multipliers based on `MarketRegime` (derived from `BBWidth`).
- Calculates RSI (using `RSI` class).
- **Trending Market (Breakout Mode)**:
- Buy: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike.
- Sell: Price > Upper Band ∧ RSI > 50 ∧ Volume Spike.
- **Sideways Market (Mean Reversion)**:
- Buy: Price ≤ Lower Band ∧ RSI ≤ 40.
- Sell: Price ≥ Upper Band ∧ RSI ≥ 60.
- **Squeeze Confirmation** (if `config["SqueezeStrategy"]` is `True`):
- Requires additional confirmation from RSI Bollinger Bands (calculated by `rsi_bollinger_confirmation` helper method).
- Sideways markets also check for volume contraction.
- **Key Configuration Parameters (from `config` dict)**:
- `bb_period`, `bb_width`
- `trending['bb_std_dev_multiplier']`, `trending['rsi_threshold']`
- `sideways['bb_std_dev_multiplier']`, `sideways['rsi_threshold']`
- `rsi_period`
- `SqueezeStrategy` (boolean)
- **Output DataFrame Columns (Daily)**: Includes input columns plus `SMA`, `UpperBand`, `LowerBand`, `BBWidth`, `MarketRegime`, `RSI`, `BuySignal`, `SellSignal`.
### 2. Entry Conditions
#### `rsi_bollinger_confirmation(self, rsi: pd.Series, window: int = 14, std_mult: float = 1.5) -> tuple`
***Trending Market (Breakout Mode):***
Buy: Price > Upper Band ∧ RSI > 50 ∧ Volume Spike (≥1.5× 20D Avg)
Sell: Price < Lower Band ∧ RSI < 50 ∧ Volume Spike
***Sideways Market (Mean Reversion):***
Buy: Price ≤ Lower Band ∧ RSI ≤ 40
Sell: Price ≥ Upper Band ∧ RSI ≥ 60
- **Description** (Helper for `MarketRegimeStrategy`): Calculates Bollinger Bands on RSI values for signal confirmation.
- **Parameters**:
- `rsi` (pd.Series): Series containing RSI values.
- `window` (int, optional): The period for the moving average. Defaults to 14.
- `std_mult` (float, optional): Standard deviation multiplier for bands. Defaults to 1.5.
- **Returns**: `tuple` - (oversold_condition, overbought_condition) as pandas Series (boolean).
### 2. `CryptoTradingStrategy`
### **Enhanced Signals with RSI Bollinger Squeeze**
*Signal Boost*: Requires both price and RSI to breach their respective bands.
- **Description**: A multi-timeframe strategy primarily designed for volatile assets like cryptocurrencies. It aggregates input data into 15-minute and 1-hour intervals for analysis.
- **Core Logic**:
- Aggregates data to 15-minute (`data_15m`) and 1-hour (`data_1h`) resolutions using `aggregate_to_minutes` and `aggregate_to_hourly` from `data_utils.py`.
- Calculates 15-minute Bollinger Bands (20-period, 2 std dev) and 15-minute EMA-smoothed RSI (14-period) using `BollingerBands.calculate_custom_bands` and `RSI.calculate_custom_rsi`.
- Calculates 1-hour Bollinger Bands (50-period, 1.8 std dev) using `BollingerBands.calculate_custom_bands`.
- **Signal Generation (on 15m timeframe)**:
- Buy Signal: Price ≤ Lower 15m Band ∧ Price ≤ Lower 1h Band ∧ RSI_15m < 35 ∧ Volume Confirmation.
- Sell Signal: Price ≥ Upper 15m Band ∧ Price ≥ Upper 1h Band ∧ RSI_15m > 65 ∧ Volume Confirmation.
- **Volume Confirmation**: Current 15m volume > 1.5 × 20-period MA of 15m volume.
- **Risk Management**: Calculates `StopLoss` and `TakeProfit` levels based on a simplified ATR (standard deviation of 15m close prices over the last 4 periods).
- Buy: SL = Price - 2 * ATR; TP = Price + 4 * ATR
- Sell: SL = Price + 2 * ATR; TP = Price - 4 * ATR
- **Key Configuration Parameters**: While this strategy uses fixed parameters for its core indicator calculations, the `config` object passed to the `Strategy` class might be used by helper functions or for future extensions (though not heavily used by the current `CryptoTradingStrategy` logic itself for primary indicator settings).
- **Output DataFrame Columns (15-minute)**: Includes resampled 15m OHLCV, plus `UpperBand_15m`, `SMA_15m`, `LowerBand_15m`, `RSI_15m`, `VolumeMA_15m`, `UpperBand_1h` (forward-filled), `LowerBand_1h` (forward-filled), `BuySignal`, `SellSignal`, `StopLoss`, `TakeProfit`.
---
## **Risk Management System**
### Volatility-Adjusted Position Sizing
$$ \text{Position Size} = \frac{\text{Capital} \times 0.02}{\text{ATR}_{14} \times \text{Price}} $$
## General Strategy Concepts (from previous high-level notes)
While the specific implementations above have their own detailed logic, some general concepts that often inspire trading strategies include:
**Key Adjustments:**
1. Use narrower Bollinger Bands (1.8σ) to avoid whipsaws
2. Require RSI confirmation within 40-60 range
3. Add volume contraction filter
- **Adaptive Parameters**: Adjusting indicator settings (like Bollinger Band width or RSI thresholds) based on market conditions (e.g., trending vs. sideways).
- **Multi-Timeframe Analysis**: Confirming signals on one timeframe with trends or levels on another (e.g., 15-minute signals confirmed by 1-hour context).
- **Volume Confirmation**: Using volume spikes or contractions to validate price-based signals.
- **Volatility-Adjusted Risk Management**: Using measures like ATR (Average True Range) to set stop-loss and take-profit levels, or to size positions dynamically.
These concepts are partially reflected in the implemented strategies, particularly in `MarketRegimeStrategy` (adaptive parameters) and `CryptoTradingStrategy` (multi-timeframe, volume confirmation, ATR-based risk levels).

101
test_bbrsi.py
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@ -4,7 +4,6 @@ import matplotlib.pyplot as plt
import pandas as pd
from cycles.utils.storage import Storage
from cycles.utils.data_utils import aggregate_to_daily
from cycles.Analysis.strategies import Strategy
logging.basicConfig(
@ -16,18 +15,12 @@ logging.basicConfig(
]
)
config_minute = {
config = {
"start_date": "2023-01-01",
"stop_date": "2024-01-01",
"data_file": "btcusd_1-min_data.csv"
}
config_day = {
"start_date": "2023-01-01",
"stop_date": "2024-01-01",
"data_file": "btcusd_1-day_data.csv"
}
config_strategy = {
"bb_width": 0.05,
"bb_period": 20,
@ -48,72 +41,104 @@ IS_DAY = False
if __name__ == "__main__":
# Load data
storage = Storage(logging=logging)
if IS_DAY:
config = config_day
else:
config = config_minute
data = storage.load_data(config["data_file"], config["start_date"], config["stop_date"])
# Run strategy
strategy = Strategy(config=config_strategy, logging=logging)
processed_data = strategy.run(data.copy(), config_strategy["strategy_name"])
# Get buy and sell signals
buy_condition = processed_data.get('BuySignal', pd.Series(False, index=processed_data.index)).astype(bool)
sell_condition = processed_data.get('SellSignal', pd.Series(False, index=processed_data.index)).astype(bool)
buy_signals = processed_data[buy_condition]
sell_signals = processed_data[sell_condition]
# plot the data with seaborn library
# Plot the data with seaborn library
if processed_data is not None and not processed_data.empty:
# Create a figure with two subplots, sharing the x-axis
fig, (ax1, ax2, ax3) = plt.subplots(3, 1, figsize=(16, 8), sharex=True)
# Plot 1: Close Price and Bollinger Bands
strategy_name = config_strategy["strategy_name"]
# Plot 1: Close Price and Strategy-Specific Bands/Levels
sns.lineplot(x=processed_data.index, y='close', data=processed_data, label='Close Price', ax=ax1)
if strategy_name == "MarketRegimeStrategy":
if 'UpperBand' in processed_data.columns and 'LowerBand' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='UpperBand', data=processed_data, label='Upper Band (BB)', ax=ax1)
sns.lineplot(x=processed_data.index, y='LowerBand', data=processed_data, label='Lower Band (BB)', ax=ax1)
else:
logging.warning("MarketRegimeStrategy: UpperBand or LowerBand not found for plotting.")
elif strategy_name == "CryptoTradingStrategy":
if 'UpperBand_15m' in processed_data.columns and 'LowerBand_15m' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='UpperBand_15m', data=processed_data, label='Upper Band (15m)', ax=ax1)
sns.lineplot(x=processed_data.index, y='LowerBand_15m', data=processed_data, label='Lower Band (15m)', ax=ax1)
else:
logging.warning("CryptoTradingStrategy: UpperBand_15m or LowerBand_15m not found for plotting.")
if 'StopLoss' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='StopLoss', data=processed_data, label='Stop Loss', ax=ax1, linestyle='--', color='orange')
if 'TakeProfit' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='TakeProfit', data=processed_data, label='Take Profit', ax=ax1, linestyle='--', color='purple')
# Plot Buy/Sell signals on Price chart
if not buy_signals.empty:
ax1.scatter(buy_signals.index, buy_signals['close'], color='green', marker='o', s=20, label='Buy Signal', zorder=5)
ax1.scatter(buy_signals.index, buy_signals['close'], color='green', marker='o', s=10, label='Buy Signal', zorder=5)
if not sell_signals.empty:
ax1.scatter(sell_signals.index, sell_signals['close'], color='red', marker='o', s=20, label='Sell Signal', zorder=5)
ax1.set_title('Price and Bollinger Bands with Signals')
ax1.scatter(sell_signals.index, sell_signals['close'], color='red', marker='o', s=10, label='Sell Signal', zorder=5)
ax1.set_title(f'Price and Signals ({strategy_name})')
ax1.set_ylabel('Price')
ax1.legend()
ax1.grid(True)
# Plot 2: RSI
if 'RSI' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='RSI', data=processed_data, label='RSI (' + str(config_strategy["rsi_period"]) + ')', ax=ax2, color='purple')
ax2.axhline(config_strategy["trending"]["rsi_threshold"][1], color='red', linestyle='--', linewidth=0.8, label='Overbought (' + str(config_strategy["trending"]["rsi_threshold"][1]) + ')')
ax2.axhline(config_strategy['trending']['rsi_threshold'][0], color='green', linestyle='--', linewidth=0.8, label='Oversold (' + str(config_strategy['trending']['rsi_threshold'][0]) + ')')
# Plot 2: RSI and Strategy-Specific Thresholds
rsi_col_name = 'RSI' if strategy_name == "MarketRegimeStrategy" else 'RSI_15m'
if rsi_col_name in processed_data.columns:
sns.lineplot(x=processed_data.index, y=rsi_col_name, data=processed_data, label=f'{rsi_col_name} (' + str(config_strategy.get("rsi_period", 14)) + ')', ax=ax2, color='purple')
if strategy_name == "MarketRegimeStrategy":
# Assuming trending thresholds are what we want to show generally
ax2.axhline(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[1], color='red', linestyle='--', linewidth=0.8, label=f'Overbought (' + str(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[1]) + ')')
ax2.axhline(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[0], color='green', linestyle='--', linewidth=0.8, label=f'Oversold (' + str(config_strategy.get("trending", {}).get("rsi_threshold", [30,70])[0]) + ')')
elif strategy_name == "CryptoTradingStrategy":
ax2.axhline(65, color='red', linestyle='--', linewidth=0.8, label='Overbought (65)') # As per Crypto strategy logic
ax2.axhline(35, color='green', linestyle='--', linewidth=0.8, label='Oversold (35)') # As per Crypto strategy logic
# Plot Buy/Sell signals on RSI chart
if not buy_signals.empty:
ax2.scatter(buy_signals.index, buy_signals['RSI'], color='green', marker='o', s=20, label='Buy Signal (RSI)', zorder=5)
if not sell_signals.empty:
ax2.scatter(sell_signals.index, sell_signals['RSI'], color='red', marker='o', s=20, label='Sell Signal (RSI)', zorder=5)
ax2.set_title('Relative Strength Index (RSI) with Signals')
ax2.set_ylabel('RSI Value')
ax2.set_ylim(0, 100) # RSI is typically bounded between 0 and 100
if not buy_signals.empty and rsi_col_name in buy_signals.columns:
ax2.scatter(buy_signals.index, buy_signals[rsi_col_name], color='green', marker='o', s=20, label=f'Buy Signal ({rsi_col_name})', zorder=5)
if not sell_signals.empty and rsi_col_name in sell_signals.columns:
ax2.scatter(sell_signals.index, sell_signals[rsi_col_name], color='red', marker='o', s=20, label=f'Sell Signal ({rsi_col_name})', zorder=5)
ax2.set_title(f'Relative Strength Index ({rsi_col_name}) with Signals')
ax2.set_ylabel(f'{rsi_col_name} Value')
ax2.set_ylim(0, 100)
ax2.legend()
ax2.grid(True)
else:
logging.info("RSI data not available for plotting.")
logging.info(f"{rsi_col_name} data not available for plotting.")
# Plot 3: BB Width
# Plot 3: Strategy-Specific Indicators
ax3.clear() # Clear previous plot content if any
if strategy_name == "MarketRegimeStrategy":
if 'BBWidth' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='BBWidth', data=processed_data, label='BB Width', ax=ax3)
if 'MarketRegime' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='MarketRegime', data=processed_data, label='Market Regime (Sideways: 1, Trending: 0)', ax=ax3)
ax3.set_title('Bollinger Bands Width')
ax3.set_ylabel('BB Width')
ax3.set_title('Bollinger Bands Width & Market Regime')
ax3.set_ylabel('Value')
elif strategy_name == "CryptoTradingStrategy":
if 'VolumeMA_15m' in processed_data.columns:
sns.lineplot(x=processed_data.index, y='VolumeMA_15m', data=processed_data, label='Volume MA (15m)', ax=ax3)
if 'volume' in processed_data.columns: # Plot original volume for comparison
sns.lineplot(x=processed_data.index, y='volume', data=processed_data, label='Volume (15m)', ax=ax3, alpha=0.5)
ax3.set_title('Volume Analysis (15m)')
ax3.set_ylabel('Volume')
ax3.legend()
ax3.grid(True)
plt.xlabel('Date') # Common X-axis label
fig.tight_layout() # Adjust layout to prevent overlapping titles/labels
plt.xlabel('Date')
fig.tight_layout()
plt.show()
else:
logging.info("No data to plot.")