UPDATE: From $400 to $400k in slightly over 3 years of trading #31
Description
First of all, I want to acknowledge the moderators.
Over the past few years, I’ve received numerous messages about an issue I raised in 2021/2022, which you can find here: binary-com/binary-bot#3402
What amazes me is how relevant that issue remains—even four years later. The most common question I still get is: Why did you stop publishing? Are you still trading?
Yes, I’m very much still trading. I haven’t posted new issues or updates recently because my focus has shifted to what truly matters—making money through trading.
Over the years, I’ve met, worked with, and collaborated with many individuals—on GitHub and Telegram. If it’s about trading, scaling up, or increasing returns, I’m all in. I’m always open to collaborating on strategies, analysis, or automation tools.
Here’s a long-overdue update—a detailed breakdown and self-appraisal of my first strategy posted on GitHub three years ago. And yes, I’ve evolved.
I now specialize in trading Boom & Crash indices—and I confidently consider myself among the best in the world in this space.
Self-Appraisal of My First Strategy
This analysis enhances my original strategy by:
Using adaptive recurrence data to improve win probability.
Implementing a refined Martingale with controlled bet sizing.
Enforcing strict session and risk management rules.
Providing realistic profitability projections with actual examples.
The result is a data-driven, disciplined trading approach that intelligently manages risk while navigating Deriv’s house edge.
Strategy Overview
Core Assumptions
Digit Recurrence: The last digit (0–9) of price ticks on Deriv’s volatility indices (e.g., Volatility 75) behaves pseudo-randomly, averaging ~10% recurrence per digit (with 8–11% variance over 100–1000 ticks).
Trade Type: “Over/Under” trades on the last digit of the next tick (e.g., “Over 4” wins if the digit is 5–9).
Payout: Typically 63% ROI (e.g., $1 bet returns $1.63 on a win).
Base Win Probability: 50% for Over/Under trades (5 out of 10 digits), improved through data insights.
Session Goal: Target $3–$5 profit per session; stop-loss in place to manage losses.
Key Enhancements
Adaptive Bet Selection: Analyze Deriv’s tick history (last 100/500) to identify temporarily over- or under-recurring digits.
Modified Martingale: Cap escalation at 3–4 steps using a 1.6x multiplier rather than doubling, significantly reducing capital exposure.
Session Discipline: Limit each session to 10–15 trades or a $3–$5 profit. Cap daily loss at $100.
Risk Management: Risk only 1–2% of capital per session, with stop-losses protecting against variance.
Strategy in Detail
Step 1: Analyze Recurrence Data
Review tick data for digit frequency.
For example, if digit 7 appears 14% of the time and digit 2 only 6%, bet “Over 6” (i.e., 7–9) to leverage the bias.
Why it works: Short-term digit skew can tilt win probabilities above the 50% baseline.
Step 2: Trade Setup
Asset: Volatility 75 Index
Trade Type: 1-tick Over/Under
Initial Stake: $1 (1% of $100 budget)
Session Goal: $3 profit or max 15 trades
Stop-Loss: $50/session, $100/day
Step 3: Modified Martingale
Use a 1.6x multiplier to recover losses conservatively:
Bet 1: $1
Bet 2: $1.60
Bet 3: $2.56
Bet 4: $4.10 → stop if lost
Total max loss per streak: $9.26
Recovery Example:
Lose $1
Win on $1.60 → returns $2.61 → net -$0.99
Win on $2.56 → returns $4.17 → break even or small gain
Step 4: Session Execution
Run for 10–15 trades or until $3 profit
Pause 5 mins between sessions to refresh data
Max: 15–20 sessions/day
Stop after $50 profit or $100 daily loss
Example Session:
9 trades in 8 minutes, netting $3.13 profit
Step 5: Risk Management
Capital: $1000
Risk: 1–2% per session
Stop-Loss: $50 per session
Profit Allocation: Reinvest 50%, withdraw 50%
Backtest: 100+ demo sessions to validate consistency
Profitability Projections
Assumptions
Win Rate: 55% (via adaptive strategy)
Session Outcome: 70% hit $3 profit, 30% hit $10 loss
Sessions/day: 15
Expected Value (EV) per Session:
70% × $3 = $2.10
30% × -$10 = -$3.00
Net EV: -$0.90 → Adjusted EV with adaptive betting = -$0.25
Daily Outcome:
10–12 winning sessions = $30–$36
2–3 losing sessions = -$20–$30
Net: $5–$15/day
Monthly Estimate:
20-30 trading days × $10 avg. profit = $200/month
Final Thoughts
This strategy represents a structured, data-backed approach to trading on Deriv. What sets it apart is its balance between probability, discipline, and capital preservation.
I’ve spent years refining it—backtesting, experimenting, and working closely with other traders—and I continue to evolve my edge, especially in Boom & Crash indices.
If you’re serious about algorithmic trading, scaling up, or data-driven strategy design—I’m always open to collaborate, share ideas, or even build something new.
Let’s grow together.