Complete Specification for an AI-Powered MetaTrader Trading System Integrating Pythagorean, Chaldean, Vedic, Kabbalistic, Chinese Numerology & Financial Astrology
Overview
This document provides a detailed, code‑ready blueprint for a system that:
· Pulls OHLCV data from MetaTrader 5 for any symbol (Forex pair, index, commodity) and timeframe.
· Extracts hundreds of features from six esoteric disciplines.
· Combines these features using an ensemble of machine learning models and a meta‑learner.
· Uses a reinforcement learning agent to determine optimal entry/exit points.
· Continuously learns from new data, increasing prediction accuracy over time.
The specification is designed so that an AI coding assistant (e.g., GitHub Copilot, Codex) can generate a fully functional implementation in Python.
- System Architecture (Layered Design)
┌──────────────────────────────────────────────────────────┐
│ MetaTrader 5 Terminal │
└───────────────────────────┬──────────────────────────────┘
│ (Python API)
┌───────────────────────────▼──────────────────────────────┐
│ 1. Data Ingestion Layer │
│ - Historical & real-time candle fetching │
│ - Symbol metadata (name, first trade date) │
└───────────────────────────┬──────────────────────────────┘
│
┌───────────────────────────▼──────────────────────────────┐
│ 2. Feature Engineering Layer │
│ ┌────────────────────────────────────────────────────┐ │
│ │ Pythagorean / Chaldean Numerology │ │
│ │ Vedic Numerology (Jyotish) │ │
│ │ Kabbalah (Gematria) │ │
│ │ Chinese Numerology (Ba Zi, I Ching) │ │
│ │ Financial Astrology (planets, aspects) │ │
│ └────────────────────────────────────────────────────┘ │
│ -> Unified numerical feature vector │
└───────────────────────────┬──────────────────────────────┘
│
┌───────────────────────────▼──────────────────────────────┐
│ 3. AI Core │
│ - Preprocessing & normalization │
│ - 5 base learners (one per discipline) │
│ - Stacked ensemble meta‑learner │
│ - Reinforcement Learning agent (PPO) for trade actions │
└───────────────────────────┬──────────────────────────────┘
│
┌───────────────────────────▼──────────────────────────────┐
│ 4. Continuous Learning & Optimization │
│ - Performance tracker (rolling accuracy per model) │
│ - Incremental retraining / online learning │
│ - Adaptive weight adjustment in meta‑model │
└───────────────────────────┬──────────────────────────────┘
│
┌───────────────────────────▼──────────────────────────────┐
│ 5. Output & Trade Execution │
│ - Signal (BUY/SELL/NEUTRAL) with confidence │
│ - Entry price, stop loss, take profit │
│ - Optional auto‑execution via MT5 │
└──────────────────────────────────────────────────────────┘
- Data Ingestion Layer
2.1 Connection to MetaTrader 5
· Use MetaTrader5 Python package.
· Initialize connection: mt5.initialize()
· Login with account credentials if required.
2.2 Data Fetching
importMetaTrader5asmt5importpandasaspddeffetch_ohlcv(symbol: str, timeframe: int, start_pos: int, count: int) ->pd.DataFrame:
rates=mt5.copy_rates_from_pos(symbol, timeframe, start_pos, count)
df=pd.DataFrame(rates)
df['time'] =pd.to_datetime(df['time'], unit='s')
returndf
· timeframe constants: mt5.TIMEFRAME_M1, M5, M15, H1, D1, etc.
· Store raw data in a time‑series database (InfluxDB) or local Parquet files for later retraining.
2.3 Symbol Birth Date
· Essential for personal cycles in numerology/astrology.
· Strategy: use the earliest available timestamp for the symbol in the broker’s history as its “birth date/time”.
· If not available, use the first trade date from broker symbol info (mt5.symbol_info(symbol).time).
- Feature Engineering Layer
Each discipline produces a set of numerical (and one‑hot encoded) features. All features are concatenated into a single vector per candle. The timestamp for each candle is t_candle = df['time'].
3.1 Common Helpers
· reduce_to_single_digit(n): repeatedly sum digits until < 10, except master numbers 11,22,33 (return as is).
· letter_to_number(char, mapping_dict): map A-Z/a-z to integer.
3.2 Pythagorean Numerology
Mapping:
A=1, B=2, C=3, D=4, E=5, F=6, G=7, H=8, I=9, J=1, K=2, L=3, M=4, N=5, O=6, P=7, Q=8, R=9, S=1, T=2, U=3, V=4, W=5, X=6, Y=7, Z=8.
Features for a symbol (e.g., “EURUSD”):
· pyth_destiny: sum of all letters (converted via mapping) reduced to single digit/master.
· pyth_soul_urge: sum of vowels only (A,E,I,O,U) reduced.
· pyth_personality: sum of consonants reduced.
· pyth_symbol_number = pyth_destiny
Features from candle timestamp:
· pyth_universal_day = reduce_to_single_digit(day + month + year) (year=YYYY)
· pyth_personal_year, pyth_personal_month, pyth_personal_day for the symbol:
· Personal Year = reduce_to_single_digit( (month_of_birth + day_of_birth + current_year) )
· Personal Month = reduce_to_single_digit( personal_year + current_month )
· Personal Day = reduce_to_single_digit( personal_month + current_day )
Additional:
· Compatibility scores: if pyth_destiny equals pyth_universal_day → 1 else 0, etc.
· Count of repeating digits in prices (close price string) reduced.
3.3 Chaldean Numerology
Mapping:
A=1, B=2, C=3, D=4, E=5, F=8, G=3, H=5, I=1, J=1, K=2, L=3, M=4, N=5, O=7, P=8, Q=1, R=2, S=3, T=4, U=6, V=6, W=6, X=5, Y=1, Z=7.
Features:
· Same as Pythagorean but using Chaldean mapping: chal_destiny, chal_soul_urge, chal_personality.
· Chaldean “compound number” interpretations are not used directly; only the reduced numbers.
3.4 Vedic Numerology (Jyotish)
Use the Swiss Ephemeris (pyswisseph) for planetary longitudes.
Symbol birth chart (from symbol birth date/time):
· Compute planetary positions and nakshatras.
· Determine the current Mahadasha/Antardasha lord using Vimshottari Dasha system (start from Moon’s nakshatra at birth).
· For any given candle time, compute the current dasha sequence and return the ruling planet ID.
Features per candle:
· vedic_dasha_lord: planet number (0‑8 for Sun‑Ketu).
· vedic_antardasha_lord.
· Moon nakshatra index at candle time (0‑26).
· vedic_day_number: sum of digits of the day in Vedic reduction.
· Planetary aspects (if any planet is retrograde, boolean features).
· Position of transiting planets in the symbol’s birth houses (1‑12). House cusps calculated with whole sign houses.
3.5 Kabbalah (Gematria)
Convert each letter of symbol name to Hebrew letters and sum their gematria values. Use a standard mapping (e.g., Aleph=1, Bet=2, ...). Since the symbol is Latin, we transliterate: A→Aleph, B→Bet, C→Gimel (or same as B/K), etc. Use a fixed transliteration table.
Features:
· kab_symbol_value (reduced).
· kab_candle_close_value: sum of digits of close price * 100 (to avoid decimals) reduced.
· kab_sephira_match: map reduced value to Sefirot (1‑10). Provide one‑hot or integer.
· Ratio of kab_symbol_value to kab_candle_close_value.
3.6 Chinese Numerology (Ba Zi & I Ching)
Ba Zi (Four Pillars):
· Use the solar calendar. A reliable library is lunardate or a custom implementation based on published tables. We need the Heavenly Stem (0‑9) and Earthly Branch (0‑11) for Year, Month, Day, Hour.
· Each pillar gives an element (Wood, Fire, Earth, Metal, Water) and Yin/Yang.
· Extract the Day Master (Heavenly Stem of Day Pillar) – this is the “self” element.
· Derive the strength of the Day Master based on surrounding branches and season.
· Features:
· Day Master element index (0‑4) one‑hot.
· Count of supporting/elemental clashes.
· chinese_day_number: sum of digits of day in Chinese reckoning reduced.
I Ching:
· Generate a hexagram number (1‑64) from the candle timestamp. Simple method: hash_64 = int(timestamp.timestamp()) % 64 + 1.
· Features: iching_hexagram (int), changing lines if using more complex seed.
3.7 Financial Astrology
Using pyswisseph for the exact candle timestamp.
Planetary positions:
· For Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, True Node (mean Node). Save longitude in degrees (0‑360).
· Compute the Moon phase: angle between Moon and Sun, binned into 8 phases (New, Crescent, First Quarter, Gibbous, Full, Disseminating, Last Quarter, Balsamic). One‑hot encoded.
Aspects between planets:
· Define a list of pairs (fast planets with slower ones) and check aspect with orb ±3°:
· Conjunction (0°), Opposition (180°), Trine (120°), Square (90°), Sextile (60°).
· For each aspect type and planet pair, create a binary feature (e.g., asp_sun_moon_conjunction). This yields many features; select the most impactful or include all.
Planets in symbol’s natal houses:
· Calculate house cusps for symbol birth chart using whole sign houses. For a given candle, check which natal house contains each transiting planet. Encode as 12 binary features per planet.
Retrogrades:
· Binary features for each planet if retrograde.
3.8 Feature Vector Assembly
All raw features from the above are concatenated. The total dimension may be around 300‑500 features. The order must be fixed. Use a dict to collect and then pd.Series. Example:
features= {
'pyth_destiny': ...,
'pyth_universal_day': ...,
...
'vedic_dasha_lord': ...,
...
'asp_sun_moon_trine': 0or1,
...
}
feature_vector=list(features.values())
- AI Core
4.1 Preprocessing
· Scale numerical continuous features with StandardScaler or MinMaxScaler (fit on training data).
· Categorical features (like elements) are already one‑hot encoded in feature engineering.
4.2 Base Learners
Train five independent models, each using only the feature subset from its own discipline:
- Pythagorean/Chaldean model (lightgbm.LGBMClassifier)
- Vedic model (XGBoost)
- Kabbalah model (RandomForest)
- Chinese model (CatBoost)
- Financial Astrology model (LightGBM)
Each model outputs a probability of upward movement (class 1) in the next prediction_horizon candles (e.g., 5 candles). The target is binary: 1 if close[t+horizon] > close[t] * (1 + threshold), else 0 (threshold, e.g., 0.0002 for forex). Use historical data for training (e.g., 70% train, 30% validation).
4.3 Stacked Meta‑Model
Train a logistic regression or small neural network on the out‑of‑fold predictions of the base models to avoid overfitting. Input: 5 probabilities (one from each base model) plus maybe a few global features (like volatility, time of day). Output: final probability p_meta.
During live inference, base models produce their probabilities, meta‑model combines them.
# Pseudo-codebase_probs= [model.predict_proba(features_subset)[:, 1] formodelinbase_models]
meta_input=np.column_stack(base_probs+ [additional_features])
final_prob=meta_model.predict_proba(meta_input)[:, 1]
4.4 Reinforcement Learning Agent for Entry/Exit
Environment:
· State: final meta probability, current position (0=none, 1=long, -1=short), unrealized PnL, recent price momentum, and a few market features (ATR, volatility).
· Actions: 0=No trade, 1=Buy, 2=Sell, 3=Close position.
· Reward: change in account equity after the action (with trading costs). Use episode of fixed length (e.g., 100 steps).
Use Stable‑Baselines3 PPO agent. The agent is trained separately from the ensemble using the meta probabilities as input. The agent learns to filter signals and manage risk.
- Continuous Learning & Optimization
5.1 Performance Tracking
Maintain a database table trade_log with: timestamp, symbol, timeframe, predicted probability, actual outcome (price movement), model weights at that time. Calculate rolling accuracy (e.g., over last 200 trades) per base model.
5.2 Retraining Scheduler
· Full retraining of base & meta models every weekend using all available history up to that point. Optionally, fine‑tune incrementally every day on new data.
· Use MLflow to track experiments and select the best performing model version.
5.3 Adaptive Weights in Meta‑Model
During retraining, the meta‑model can be given recent performance metrics as additional input, or we can use a dynamic weighting scheme where base model probabilities are multiplied by a weight proportional to their recent Sharpe ratio or accuracy before feeding into the meta‑model. The specification: implement a Weighted Average Ensemble that adjusts weights periodically. Simpler: the meta‑model itself is retrained on new data, which naturally adjusts the importance of each base model.
- Output & Trade Execution
6.1 Signal Generation
· At each new candle close, compute features, obtain final_prob.
· Use a threshold: if final_prob > 0.6 and RL agent outputs action=1 → BUY signal. If final_prob < 0.4 and RL agent outputs action=2 → SELL signal.
· Confidence level: map final_prob distance from 0.5 to a 0‑100% scale.
6.2 Risk Management
· Stop loss: set at a price level derived from ATR (e.g., 1.5 * ATR) or from a significant Gann/numerology level (e.g., using square of 9 based on price).
· Take profit: 2:1 reward-to-risk ratio or Fibonacci extensions.
6.3 Auto Execution
Use mt5.order_send() with the signal details. Include verification of margin, spread, and existing positions.
- Implementation Code Structure
project/
├── data/
│ ├── ingestion.py # MT5 data fetcher
│ └── storage.py # InfluxDB/Parquet handler
├── features/
│ ├── base.py # Common reduce, letter maps
│ ├── pythagorean.py
│ ├── chaldean.py
│ ├── vedic.py # uses pyswisseph
│ ├── kabbalah.py
│ ├── chinese.py # Ba Zi + I Ching
│ ├── astrology.py # planets, aspects, houses
│ └── builder.py # assemble feature vector
├── models/
│ ├── base_learners.py # train/evaluate per discipline
│ ├── meta_model.py # stacked ensemble
│ ├── rl_env.py # Gym env for trading
│ └── agent.py # RL agent training/inference
├── continuous_learning/
│ ├── tracker.py # performance metrics
│ └── retrainer.py # scheduled retraining
├── execution/
│ ├── signal.py # generate trade signal
│ └── order_manager.py # MT5 order execution
├── config.py # constants, paths, timeframes
├── main.py # orchestration loop
└── requirements.txt
7.1 Example: Pythagorean Feature Class
# features/pythagorean.pyclassPythagoreanFeatures:
MAPPING= { 'A':1, 'B':2, ... , 'Z':8 }
VOWELS=set('AEIOU')
@staticmethoddefname_to_numbers(name: str):
return [PythagoreanFeatures.MAPPING.get(c.upper(), 0) forcinnameifc.isalpha()]
@staticmethoddefdestiny(name: str) ->int:
nums=PythagoreanFeatures.name_to_numbers(name)
returnreduce_to_single_digit(sum(nums))
...7.2 Astrology Feature Example
importswissephasswedefget_planet_pos(jd, planet_id):
result, ret=swe.calc_ut(jd, planet_id)
returnresult[0] # longitudedefaspects_between(p1_long, p2_long, orb=3.0):
diff=abs(p1_long-p2_long) %360ifdiff<=orbordiff>=360-orb: return'conjunction'ifabs(diff-60) <=orb: return'sextile'ifabs(diff-90) <=orb: return'square'ifabs(diff-120) <=orb: return'trine'ifabs(diff-180) <=orb: return'opposition'returnNone
- Complete Prediction Pipeline (Pseudocode)
defrun_live_pipeline(symbol, timeframe):
# 1. get latest candledf=fetch_ohlcv(symbol, timeframe, 0, 100) # enough for feature lagcandle=df.iloc[-1]
t=candle['time']
# 2. feature extractionf_pyth=PythagoreanFeatures.compute(symbol, t)
f_chald=ChaldeanFeatures.compute(symbol, t)
f_vedic=VedicFeatures.compute(symbol, t)
f_kab=KabbalahFeatures.compute(symbol, t)
f_chin=ChineseFeatures.compute(symbol, t)
f_astro=AstrologyFeatures.compute(symbol, t)
feature_vec=assemble(f_pyth, f_chald, f_vedic, f_kab, f_chin, f_astro)
# 3. get base model probabilitiesprobs= []
fori, modelinenumerate(base_models):
subset=subset_feature(feature_vec, i)
probs.append(model.predict_proba([subset])[0,1])
meta_input=probs+ [candle['atr'], candle['rsi']] # etc.final_prob=meta_model.predict_proba([meta_input])[0,1]
# 4. RL decisionstate=get_state(final_prob, current_position, ...)
action=rl_agent.predict(state, deterministic=True)[0]
# 5. signalreturngenerate_signal(action, final_prob, candle)
- Notes for AI Code Generation
· Use exact tables for letter mappings provided; they are standard.
· For Vedic astrology, the pyswisseph library requires ephemeris files; download them automatically in setup.
· For Chinese Ba Zi, provide a complete implementation of the solar calendar conversion or use a reliable library like cnlunar. The feature extraction must be deterministic.
· The continuous learning mechanism must store predictions and actual outcomes; use a simple SQLite database initially.
· All models should be persisted with joblib or pickle and versioned.
· Add extensive logging and error handling.
This specification is now complete and can be directly turned into Python code by an AI programming assistant.
Complete Specification for an AI-Powered MetaTrader Trading System Integrating Pythagorean, Chaldean, Vedic, Kabbalistic, Chinese Numerology & Financial Astrology
Overview
This document provides a detailed, code‑ready blueprint for a system that:
· Pulls OHLCV data from MetaTrader 5 for any symbol (Forex pair, index, commodity) and timeframe.
· Extracts hundreds of features from six esoteric disciplines.
· Combines these features using an ensemble of machine learning models and a meta‑learner.
· Uses a reinforcement learning agent to determine optimal entry/exit points.
· Continuously learns from new data, increasing prediction accuracy over time.
The specification is designed so that an AI coding assistant (e.g., GitHub Copilot, Codex) can generate a fully functional implementation in Python.
2.1 Connection to MetaTrader 5
· Use MetaTrader5 Python package.
· Initialize connection: mt5.initialize()
· Login with account credentials if required.
2.2 Data Fetching
· timeframe constants: mt5.TIMEFRAME_M1, M5, M15, H1, D1, etc.
· Store raw data in a time‑series database (InfluxDB) or local Parquet files for later retraining.
2.3 Symbol Birth Date
· Essential for personal cycles in numerology/astrology.
· Strategy: use the earliest available timestamp for the symbol in the broker’s history as its “birth date/time”.
· If not available, use the first trade date from broker symbol info (mt5.symbol_info(symbol).time).
Each discipline produces a set of numerical (and one‑hot encoded) features. All features are concatenated into a single vector per candle. The timestamp for each candle is t_candle = df['time'].
3.1 Common Helpers
· reduce_to_single_digit(n): repeatedly sum digits until < 10, except master numbers 11,22,33 (return as is).
· letter_to_number(char, mapping_dict): map A-Z/a-z to integer.
3.2 Pythagorean Numerology
Mapping:
A=1, B=2, C=3, D=4, E=5, F=6, G=7, H=8, I=9, J=1, K=2, L=3, M=4, N=5, O=6, P=7, Q=8, R=9, S=1, T=2, U=3, V=4, W=5, X=6, Y=7, Z=8.
Features for a symbol (e.g., “EURUSD”):
· pyth_destiny: sum of all letters (converted via mapping) reduced to single digit/master.
· pyth_soul_urge: sum of vowels only (A,E,I,O,U) reduced.
· pyth_personality: sum of consonants reduced.
· pyth_symbol_number = pyth_destiny
Features from candle timestamp:
· pyth_universal_day = reduce_to_single_digit(day + month + year) (year=YYYY)
· pyth_personal_year, pyth_personal_month, pyth_personal_day for the symbol:
· Personal Year = reduce_to_single_digit( (month_of_birth + day_of_birth + current_year) )
· Personal Month = reduce_to_single_digit( personal_year + current_month )
· Personal Day = reduce_to_single_digit( personal_month + current_day )
Additional:
· Compatibility scores: if pyth_destiny equals pyth_universal_day → 1 else 0, etc.
· Count of repeating digits in prices (close price string) reduced.
3.3 Chaldean Numerology
Mapping:
A=1, B=2, C=3, D=4, E=5, F=8, G=3, H=5, I=1, J=1, K=2, L=3, M=4, N=5, O=7, P=8, Q=1, R=2, S=3, T=4, U=6, V=6, W=6, X=5, Y=1, Z=7.
Features:
· Same as Pythagorean but using Chaldean mapping: chal_destiny, chal_soul_urge, chal_personality.
· Chaldean “compound number” interpretations are not used directly; only the reduced numbers.
3.4 Vedic Numerology (Jyotish)
Use the Swiss Ephemeris (pyswisseph) for planetary longitudes.
Symbol birth chart (from symbol birth date/time):
· Compute planetary positions and nakshatras.
· Determine the current Mahadasha/Antardasha lord using Vimshottari Dasha system (start from Moon’s nakshatra at birth).
· For any given candle time, compute the current dasha sequence and return the ruling planet ID.
Features per candle:
· vedic_dasha_lord: planet number (0‑8 for Sun‑Ketu).
· vedic_antardasha_lord.
· Moon nakshatra index at candle time (0‑26).
· vedic_day_number: sum of digits of the day in Vedic reduction.
· Planetary aspects (if any planet is retrograde, boolean features).
· Position of transiting planets in the symbol’s birth houses (1‑12). House cusps calculated with whole sign houses.
3.5 Kabbalah (Gematria)
Convert each letter of symbol name to Hebrew letters and sum their gematria values. Use a standard mapping (e.g., Aleph=1, Bet=2, ...). Since the symbol is Latin, we transliterate: A→Aleph, B→Bet, C→Gimel (or same as B/K), etc. Use a fixed transliteration table.
Features:
· kab_symbol_value (reduced).
· kab_candle_close_value: sum of digits of close price * 100 (to avoid decimals) reduced.
· kab_sephira_match: map reduced value to Sefirot (1‑10). Provide one‑hot or integer.
· Ratio of kab_symbol_value to kab_candle_close_value.
3.6 Chinese Numerology (Ba Zi & I Ching)
Ba Zi (Four Pillars):
· Use the solar calendar. A reliable library is lunardate or a custom implementation based on published tables. We need the Heavenly Stem (0‑9) and Earthly Branch (0‑11) for Year, Month, Day, Hour.
· Each pillar gives an element (Wood, Fire, Earth, Metal, Water) and Yin/Yang.
· Extract the Day Master (Heavenly Stem of Day Pillar) – this is the “self” element.
· Derive the strength of the Day Master based on surrounding branches and season.
· Features:
· Day Master element index (0‑4) one‑hot.
· Count of supporting/elemental clashes.
· chinese_day_number: sum of digits of day in Chinese reckoning reduced.
I Ching:
· Generate a hexagram number (1‑64) from the candle timestamp. Simple method: hash_64 = int(timestamp.timestamp()) % 64 + 1.
· Features: iching_hexagram (int), changing lines if using more complex seed.
3.7 Financial Astrology
Using pyswisseph for the exact candle timestamp.
Planetary positions:
· For Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, Pluto, True Node (mean Node). Save longitude in degrees (0‑360).
· Compute the Moon phase: angle between Moon and Sun, binned into 8 phases (New, Crescent, First Quarter, Gibbous, Full, Disseminating, Last Quarter, Balsamic). One‑hot encoded.
Aspects between planets:
· Define a list of pairs (fast planets with slower ones) and check aspect with orb ±3°:
· Conjunction (0°), Opposition (180°), Trine (120°), Square (90°), Sextile (60°).
· For each aspect type and planet pair, create a binary feature (e.g., asp_sun_moon_conjunction). This yields many features; select the most impactful or include all.
Planets in symbol’s natal houses:
· Calculate house cusps for symbol birth chart using whole sign houses. For a given candle, check which natal house contains each transiting planet. Encode as 12 binary features per planet.
Retrogrades:
· Binary features for each planet if retrograde.
3.8 Feature Vector Assembly
All raw features from the above are concatenated. The total dimension may be around 300‑500 features. The order must be fixed. Use a dict to collect and then pd.Series. Example:
4.1 Preprocessing
· Scale numerical continuous features with StandardScaler or MinMaxScaler (fit on training data).
· Categorical features (like elements) are already one‑hot encoded in feature engineering.
4.2 Base Learners
Train five independent models, each using only the feature subset from its own discipline:
Each model outputs a probability of upward movement (class 1) in the next prediction_horizon candles (e.g., 5 candles). The target is binary: 1 if close[t+horizon] > close[t] * (1 + threshold), else 0 (threshold, e.g., 0.0002 for forex). Use historical data for training (e.g., 70% train, 30% validation).
4.3 Stacked Meta‑Model
Train a logistic regression or small neural network on the out‑of‑fold predictions of the base models to avoid overfitting. Input: 5 probabilities (one from each base model) plus maybe a few global features (like volatility, time of day). Output: final probability p_meta.
During live inference, base models produce their probabilities, meta‑model combines them.
4.4 Reinforcement Learning Agent for Entry/Exit
Environment:
· State: final meta probability, current position (0=none, 1=long, -1=short), unrealized PnL, recent price momentum, and a few market features (ATR, volatility).
· Actions: 0=No trade, 1=Buy, 2=Sell, 3=Close position.
· Reward: change in account equity after the action (with trading costs). Use episode of fixed length (e.g., 100 steps).
Use Stable‑Baselines3 PPO agent. The agent is trained separately from the ensemble using the meta probabilities as input. The agent learns to filter signals and manage risk.
5.1 Performance Tracking
Maintain a database table trade_log with: timestamp, symbol, timeframe, predicted probability, actual outcome (price movement), model weights at that time. Calculate rolling accuracy (e.g., over last 200 trades) per base model.
5.2 Retraining Scheduler
· Full retraining of base & meta models every weekend using all available history up to that point. Optionally, fine‑tune incrementally every day on new data.
· Use MLflow to track experiments and select the best performing model version.
5.3 Adaptive Weights in Meta‑Model
During retraining, the meta‑model can be given recent performance metrics as additional input, or we can use a dynamic weighting scheme where base model probabilities are multiplied by a weight proportional to their recent Sharpe ratio or accuracy before feeding into the meta‑model. The specification: implement a Weighted Average Ensemble that adjusts weights periodically. Simpler: the meta‑model itself is retrained on new data, which naturally adjusts the importance of each base model.
6.1 Signal Generation
· At each new candle close, compute features, obtain final_prob.
· Use a threshold: if final_prob > 0.6 and RL agent outputs action=1 → BUY signal. If final_prob < 0.4 and RL agent outputs action=2 → SELL signal.
· Confidence level: map final_prob distance from 0.5 to a 0‑100% scale.
6.2 Risk Management
· Stop loss: set at a price level derived from ATR (e.g., 1.5 * ATR) or from a significant Gann/numerology level (e.g., using square of 9 based on price).
· Take profit: 2:1 reward-to-risk ratio or Fibonacci extensions.
6.3 Auto Execution
Use mt5.order_send() with the signal details. Include verification of margin, spread, and existing positions.
7.1 Example: Pythagorean Feature Class
7.2 Astrology Feature Example
· Use exact tables for letter mappings provided; they are standard.
· For Vedic astrology, the pyswisseph library requires ephemeris files; download them automatically in setup.
· For Chinese Ba Zi, provide a complete implementation of the solar calendar conversion or use a reliable library like cnlunar. The feature extraction must be deterministic.
· The continuous learning mechanism must store predictions and actual outcomes; use a simple SQLite database initially.
· All models should be persisted with joblib or pickle and versioned.
· Add extensive logging and error handling.
This specification is now complete and can be directly turned into Python code by an AI programming assistant.