Net Gain
+6,486.36%
$10,000 → $658,636.02
Quantitative trading systems where Elliott Wave geometry, Fibonacci mathematics and AI confluence scoring meet a deterministic, zero-martingale risk engine.
Python
engine
MQL5
execution
ML
confluence
24/7
monitoring
Strategy equity
$658,636.02
+6,486%
Net Return
87.3%
Win Rate
3.79
Profit Factor
31
Green Months
Audited performance
A complete view of the system’s 31-month backtest record, from trade-level consistency to downside control.
Net Gain
+6,486.36%
$10,000 → $658,636.02
Total Trades
1,926
1,682 wins / 244 losses
Win Rate
87.3%
Across all closed positions
Profit Factor
3.79
$3.79 gained per $1 lost
Max Drawdown
23.72%
Peak-to-trough risk
Streaks
56 / 4
Max winning / losing trades
31 consecutive profitable months
100% positive monthly record from 2024 through 2026.
Compound calculator
Adjust your starting capital, expected monthly return, and horizon. Projections are illustrative and not guaranteed.
Initial deposit
$10,000
Monthly return
8%
Time horizon
12 months
Projected balance
$25,182
+$15,182 projected profit
Copy trading portfolio
Purpose-built strategies for different assets and trading tempos, united by transparent risk rules.
Swing trading around 0.618 golden pocket entries with full risk control and measured exposure.
High-frequency XAUUSD precision using tight stop-loss placement and intraday structure.
Elliott Wave tracking across BTC, ETH, and high-liquidity assets on Bybit and Binance.
Investor onboarding
Elliott Wave engine
The algorithm maps the full market cycle, waits for Fibonacci confluence, and only acts when structure and risk align.
AI & quant stack
A deterministic pipeline: market data in, wave structure parsed, Fibonacci confluence scored by the model, risk sized by formula, order executed.
# ths/engine/wave.py
def signal(candles: Series) -> Order | None:
wave = ElliottParser(degree="intermediate").fit(candles)
if wave.phase != Phase.IMPULSE_5_SETUP:
return None
fib = Fibonacci(wave.w3).retrace([0.382, 0.5, 0.618, 0.786])
conf = confluence(fib, liquidity_zones(candles), atr=ATR(14))
if conf.score < 0.72: # AI confidence gate
return None
risk = RiskEngine(max_dd=0.2372, martingale=False, grid=False)
lots = risk.size(equity, stop=conf.invalidation)
return Order(side=wave.side, lots=lots, sl=conf.invalidation,
tp=fib.extension(1.618))risk.check() → OK · martingale=False · grid=False
Data layer
Tick & M1 aggregation, ATR volatility normalisation, session filters.
Wave parser
Elliott degree labelling with invalidation rules and diagonal detection.
Confluence model
Scores Fibonacci, liquidity and structure agreement; trades only above 0.72.
Risk engine
Position sizing from equity and stop distance, hard drawdown ceiling.
σ
Volatility-adjusted sizing
φ
0.618 golden-pocket entries
E[R]
Positive expectancy filter
ΔDD
Drawdown ceiling 23.72%
2026—2027 roadmap
A focused rollout built around verified distribution, multi-asset expansion, and disciplined capital access.
Live distribution
RoboForex CopyFX accounts and TradingView live ideas.
Crypto expansion
Bybit and Binance crypto copy-trading rollout.
Institutional access
Prop-trading fund challenges and institutional capital pool.
FAQ
Understand the model, the process, and the risk before choosing to participate.
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