Profittown Sniper Smc
ICT Smart Money Concept Sniper Bot
Install / Use
npx skills add manuelinfosec/profittown-sniper-smcInstalls into whichever agent you are using.
README
📘 Quant Spec Document: Step Index 100 Sniper AI Bot (Hybrid Mode Enabled)
Objective:
Design and implement a high-precision, rule-based trading bot that executes sniper Smart Money Concept (SMC) trades on Step Index 100 to grow accounts aggressively, targeting $1k → $3k per day, and transitioning into a swing-trading mode to compound profits toward $10k–$30k+ weekly, all while using tight risk control and flawless execution.
🔁 Dual Mode Strategy
1. Normal Day-Trade Mode
- Short-term sniper trades (M15 time frame)
- $1k starting capital → $3k target via multiple high-RRR trades
- Tight stop-loss (5–15 pips)
- Up to 5 trades/day (max risk: 10–20% per trade)
2. Hybrid Swing Mode (Runs in Parallel)
- Bot continues day trading until a valid swing setup is found
- When swing trade is detected (H1/H4 confluence), bot enters it using profit only
- Once swing trade is placed, day trade engine pauses
- Swing trades target 300–1000+ pip moves with extended holding period
🧠 Strategy Overview
✅ Core Logic:
- Trade only when all SMC conditions are met:
- Break of Structure (BOS)
- Valid Order Block (OB)
- Liquidity Sweep
- OB within 61.8–78.6% Fibonacci retracement
- Clean price structure above/below OB
- Impulse from OB caused BOS
✅ Entry Type:
- Sniper LIMIT order at OB wick
- Stop-loss (SL): 5–15 pips (day), 100–300 pips (swing)
- Take-profit (TP): Minimum 1:3 RRR
🔍 Perfect OB Filter Module
🎯 Purpose:
Filter out weak or fake OBs. Only execute high-confluence sniper setups.
✅ Filter Scoring Logic (max score = 6):
- +1: OB caused a clean displacement
- +1: OB is unmitigated
- +1: Liquidity sweep occurred just before OB
- +1: OB aligns with Fib retracement zone (61.8–78.6%)
- +1: Clean structure around OB (no wick chaos)
- +1: OB impulse caused BOS
🧪 Acceptance Threshold:
- Only accept OBs with score ≥ 5
📈 Swing Trade Detection Logic (High Precision)
🔹 Requirements for Swing Trade Entry:
- Timeframe: 4H (structure), 1H/4H (entry logic)
- Confirmed BOS on 4H timeframe (major structural break)
- Identify last unmitigated OB that caused BOS
- OB must sit within 61.8–78.6% Fib retracement zone
- Liquidity must have been swept just before OB formation
- Clean price action and minimal traffic around OB zone
- Confluence score must be ≥ 5/6
🔹 Execution:
- Entry: Limit order at OB wick
- SL: Just outside OB sweep (100–300 pips)
- TP: Swing high/low or 1:3 to 1:6 RRR target (300–1000+ pips)
- Risk: Only use profit buffer from day trades (never principal)
🤖 Bot Execution Flow
🔹 Day Trade Mode
if detect_bos():
ob = detect_order_block()
if is_perfect_ob(ob, bos, candles, liquidity_zones, fib_zone):
lot_size = calculate_lot_size(account_balance, sl)
place_limit_order(entry, sl, tp, lot_size)
monitor_trade()
update_profit_tracker()
🔹 Swing Mode Scanner (Runs in Parallel)
if detect_bos(tf='H4'):
ob = detect_order_block(tf='H4')
if is_perfect_ob(ob, bos, candles_h4, liquidity_zones, fib_zone):
lot = calculate_lot_size(day_profit_buffer, swing_sl)
place_limit_order(entry, swing_sl, swing_tp, lot)
pause_day_trading()
💸 Risk Management Rules
| Rule | Value | | --- | --- | | Per trade risk (Day) | 10–20% of current balance | | SL size (Day) | 5–15 pips | | TP ratio (Day) | Minimum 1:3 RRR | | Max trades/day | 3–5 | | Swing risk | Use only profit from Day mode | | SL size (Swing) | 100–300 pips | | TP ratio (Swing) | 1:3 – 1:6 |
📊 Trade Frequency Estimates
| Trade Type | Frequency | | --- | --- | | 100–300 pip sniper trade | 2–4 times daily | | 300–500 pip swing trade | 1–3 times weekly | | 500–1000 pip multi-leg trade | 1x per week |
🔐 Profit Allocation & Swing Trigger
- Bot tracks daily profit growth
- Continues day mode until high-confluence swing OB appears
- When swing setup detected:
- Uses profit buffer only as capital (e.g., $3k)
- Pauses day trades
- Monitors swing trade until TP or SL is hit
- Resets logic next session
🧱 Modules Required
1. Market Scanner
- Detect BOS, swing highs/lows, OB candidates on multiple TFs
2. Perfect OB Filter
- Score OB validity, reject weak setups
3. Risk Engine
- Auto-calculate lot sizes from SL + risk %
4. Trade Engine
- Place LIMIT orders with SL/TP logic for day + swing modes
5. Performance Tracker
- Track daily profits, monitor buffer, activate swing scan
🧠 Quant Notes:
- Use Python + MT5 SDK (or Deriv API)
- Separate
day_engine()andswing_engine()modules - Enable parallel monitoring of swing conditions
- Trade log should mark:
[type: day]or[type: swing]
🔁 Learning & Optimization Module (Self-Training Logic)
🔹 Purpose:
Continuously improve entry accuracy, filter tuning, and SL/TP logic by learning from live and historical performance.
🔹 Core Features:
- Trade Logger: Save full trade context (TF, entry zone, SL/TP, confluence score, outcome)
- Accuracy Monitor: Calculate win rate, RRR, stop-out behavior per setup type
- Filter Optimizer: Adjust confluence thresholds based on win/loss streaks
- OB Replay Evaluator: Re-analyze all trades post-close to identify what changed between entry and exit
🔹 Future Extensions:
- Integrate LSTM or attention-based model to learn which confluence mixes produce the best results
- Enable semi-supervised fine-tuning of OB filters from large datasets
- Build heatmaps of successful OB zones and structure types
🏁 Final Notes:
This hybrid sniper bot runs day mode continuously until a valid swing trade is found. Once detected, it redirects profit into the swing setup and pauses intraday trades.
Day builds margin → swing scales the gains.
This approach maximizes trade frequency, efficiency, and capital scaling within 24/7 synthetic markets.
Video:
https://youtu.be/wVqb9xmCOuk?si=g8-rs2Ro5_X7fHXb
Related Skills
valuecell
11.0kValueCell is a community-driven, multi-agent platform for financial applications.
QuantDinger
10.4kAI quantitative trading platform for crypto, stocks, and forex with backtesting, live trading, market data, and multi-agent research.vibe-trading ,trading-agents,ai-trader,ai-trading
beanquery-mcp
50Beancount MCP Server is an experimental implementation that utilizes the Model Context Protocol (MCP) to enable AI assistants to query and analyze Beancount ledger files using Beancount Query Language (BQL) and the beanquery tool.
finance-skills
3.1kA collection of skills for AI financial analysis.
