Smartmoney — Penunjuk dan Strategi

Order Block Volumatic FVG Strategy Inspired by: Volumatic Fair Value Gaps — License: CC BY-NC-SA 4.0 (Creative Commons Attribution–NonCommercial–ShareAlike). This script is a non-commercial derivative work that credits the original author and keeps the same license. What this strategy does This turns BigBeluga’s visual FVG concept into an entry/exit strategy. It scans bullish and bearish FVG boxes, measures how deep price has mitigated into a box (as a percentage), and opens a long/short when your mitigation threshold and filters are satisfied. Risk is managed with a fixed Stop Loss % and a Trailing Stop that activates only after a user-defined profit trigger. Additions vs. the original indicator ✅ Strategy entries based on % mitigation into FVGs (long/short). ✅ Lower-TF volume split using upticks/downticks; fallback if LTF data is missing (distributes prior bar volume by close’s position in its H–L range) to avoid NaN/0. ✅ Per-FVG total volume filter (min/max) so you can skip weak boxes. ✅ Age filter (min bars since the FVG was created) to avoid fresh/immature boxes. ✅ Bull% / Bear% share filter (the 46%/53% numbers you see inside each FVG). ✅ Optional candle confirmation and cooldown between trades. ✅ Risk management: fixed SL % + Trailing Stop with a profit trigger (doesn’t trail until your trigger is reached). ✅ Pine v6 safety: no unsupported args, no indexof/clamp/when, reverse-index deletes, guards against zero/NaN. How a trade is decided (logic overview) Detect FVGs (same rules as the original visual logic). For each FVG currently intersected by the bar, compute: Mitigation % (how deep price has entered the box). Bull%/Bear% split (internal volume share). Total volume (printed on the box) from LTF aggregation or fallback. Age (bars) since the box was created. Apply your filters: Mitigation ≥ Long/Short threshold. Volume between your min and max (if enabled). Age ≥ min bars (if enabled). Bull% / Bear% within your limits (if enabled). (Optional) the current candle must be in trade direction (confirm). If multiple FVGs qualify on the same bar, the strategy uses the most recent one. Enter long/short (no pyramiding). Exit with: Fixed Stop Loss %, and Trailing Stop that only starts after price reaches your profit trigger %. Input settings (quick guide) Mitigation source: close or high/low. Use high/low for intrabar touches; close is stricter. Mitigation % thresholds: minimal mitigation for Long and Short. TOTAL Volume filter: skip FVGs with too little/too much total volume (per box). Bull/Bear share filter: require, e.g., Long only if Bull% ≥ 50; avoid Short when Bull% is high (Short Bull% max). Age filter (bars): e.g., ≥ 20–30 bars to avoid fresh boxes. Confirm candle: require candle direction to match the trade. Cooldown (bars): minimum bars between entries. Risk: Stop Loss % (fixed from entry price). Activate trailing at +% profit (the trigger). Trailing distance % (the trailing gap once active). Lower-TF aggregation: Auto: TF/Divisor → picks 1/3/5m automatically. Fixed: choose 1/3/5/15m explicitly. If LTF can’t be fetched, fallback allocates prior bar’s volume by its close position in the bar’s H–L. Suggested starting presets (you should optimize per market) Mitigation: 60–80% for both Long/Short. Bull/Bear share: Long: Bull% ≥ 50–70, Bear% ≤ 100. Short: Bull% ≤ 60 (avoid shorting into strong support), Bear% ≥ 0–70 as you prefer. Age: ≥ 20–30 bars. Volume: pick a min that filters noise for your symbol/timeframe. Risk: SL 4–6%, trailing trigger 1–2%, distance 1–2% (crypto example). Set slippage/fees in Strategy Properties. Notes, limitations & best practices Data differences: The LTF split uses request.security_lower_tf. If the exchange/data feed has sparse LTF data, the fallback kicks in (it’s deliberate to avoid NaNs but is a heuristic). Real-time vs backtest: The current bar can update until close; results on historical bars use closed data. Use “Bar Replay” to understand intrabar effects. No pyramiding: Only one position at a time. Modify pyramiding in the header if you need scaling. Assets: For spot/crypto, TradingView “volume” is exchange volume; in some markets it may be tick volume—interpret filters accordingly. Risk disclosure: Past performance ≠ future results. Use appropriate position sizing and risk controls; this is not financial advice. Credits Visual FVG concept and original implementation: BigBeluga. This derivative strategy adds entry/exit logic, volume/age/share filters, robust LTF handling, and risk management while preserving the original spirit. License remains CC BY-NC-SA 4.0 (non-commercial, attribution required, share-alike).

Hazel nut BB Strategy, volume base- lite version Hazel nut BB Strategy, volume base — lite version Having knowledge and information in financial markets is only useful when a trader operates with a well-defined trading strategy. Trading strategies assist in capital management, profit-taking, and reducing potential losses. This strategy is built upon the core principle of supply and demand dynamics. Alongside this foundation, one of the widely used technical tools — the Bollinger Bands — is employed to structure a framework for profit management and risk control. In this strategy, the interaction of these tools is explained in detail. A key point to note is that for calculating buy and sell volumes, a lower timeframe function is used. When applied with a tick-level resolution, this provides the most precise measurement of buyer/seller flows. However, this comes with a limitation of reduced historical depth. Users should be aware of this trade-off: if precise tick-level data is required, shorter timeframes should be considered to extend historical coverage . The strategy offers multiple configuration options. Nevertheless, it should be treated strictly as a supportive tool rather than a standalone trading system. Decisions must integrate personal analysis and other instruments. For example, in highly volatile assets with narrow ranges, it is recommended to adjust profit-taking and stop-loss percentages to smaller values. ◉ Volume Settings • Buyer and seller volume (up/down volume) are requested from a lower timeframe, with an option to override the automatic resolution. • A global lookback period is applied to calculate moving averages and cumulative sums of buy/sell/delta volumes. • Ratios of buyers/sellers to total volume are derived both on the current bar and across the lookback window. ◉ Bollinger Band • Bands are computed using configurable moving averages (SMA, EMA, RMA, WMA, VWMA). • Inputs allow control of length, standard deviation multiplier, and offset. • The basis, upper, and lower bands are plotted, with a shaded background between them. ◉ Progress & Proximity • Relative position of the price to the Bollinger basis is expressed as percentages (qPlus/qMinus). • “Near band” conditions are triggered when price progress toward the upper or lower band exceeds a user-defined threshold (%). • A signed score (sScore) represents how far the close has moved above or below the basis relative to band width. ◉ Info Table • Optional compact table summarizing: • - Upper/lower band margins • - Buyer/seller volumes with moving averages • - Delta and cumulative delta • - Buyer/seller ratios per bar and across the window • - Money flow values (buy/sell/delta × price) for bar-level and summed periods • The table is neutral-colored and resizable for different chart layouts. ◉ Zone Event Gate • Tracks entry into and exit from “near band” zones. • Arming logic: a side is armed when price enters a band proximity zone. • Trigger logic: on exit, a trade event is generated if cumulative buyer or seller volume dominates over a configurable window. ◉ Trading Logic • Orders are placed only on zone-exit events, conditional on volume dominance. • Position sizing is defined as a fixed percentage of strategy equity. • Long entries occur when leaving the lower zone with buyer dominance; short entries occur when leaving the upper zone with seller dominance. ◉ Exit Rules • Open positions are managed by a strict priority sequence: • 1. Stop-loss (% of entry price) • 2. Take-profit (% of entry price) • 3. Opposite-side event (zone exit with dominance in the other direction) • Stop-loss and take-profit levels are configurable ◉ Notes • This lite version is intended to demonstrate the interaction of Bollinger Bands and volume-based dominance logic. • It provides a framework to observe how price reacts at band boundaries under varying buy/sell pressure, and how zone exits can be systematically converted into entry/exit signals. When configuring this strategy, it is essential to carefully review the settings within the Strategy Tester. Ensure that the chosen parameters and historical data options are correctly aligned with the intended use. Accurate back testing depends on applying proper configurations for historical reference. The figure below illustrates sample result and configuration type.

Combo Backtest 123 Reversal & Smart Money Index (SMI) This is combo strategies for get a cumulative signal. First strategy This System was created from the Book "How I Tripled My Money In The Futures Market" by Ulf Jensen, Page 183. This is reverse type of strategies. The strategy buys at market, if close price is higher than the previous close during 2 days and the meaning of 9-days Stochastic Slow Oscillator is lower than 50. The strategy sells at market, if close price is lower than the previous close price during 2 days and the meaning of 9-days Stochastic Fast Oscillator is higher than 50. Second strategy Smart money index (SMI) or smart money flow index is a technical analysis indicator demonstrating investors sentiment. The index was invented and popularized by money manager Don Hays. The indicator is based on intra-day price patterns. The main idea is that the majority of traders (emotional, news-driven) overreact at the beginning of the trading day because of the overnight news and economic data. There is also a lot of buying on market orders and short covering at the opening. Smart, experienced investors start trading closer to the end of the day having the opportunity to evaluate market performance. Therefore, the basic strategy is to bet against the morning price trend and bet with the evening price trend. The SMI may be calculated for many markets and market indices (S&P 500, DJIA, etc.) The SMI sends no clear signal whether the market is bullish or bearish. There are also no fixed absolute or relative readings signaling about the trend. Traders need to look at the SMI dynamics relative to that of the market. If, for example, SMI rises sharply when the market falls, this fact would mean that smart money is buying, and the market is to revert to an uptrend soon. The opposite situation is also true. A rapidly falling SMI during a bullish market means that smart money is selling and that market is to revert to a downtrend soon. The SMI is, therefore, a trend-based indicator. Some analysts use the smart money index to claim that precious metals such as gold will continually maintain value in the future. WARNING: - For purpose educate only - This script to change bars colors.

Smart Money Index (SMI) Backtest Attention: If you would to use this indicator on the ES, you should have intraday data 60min in your account. Smart money index (SMI) or smart money flow index is a technical analysis indicator demonstrating investors sentiment. The index was invented and popularized by money manager Don Hays. The indicator is based on intra-day price patterns. The main idea is that the majority of traders (emotional, news-driven) overreact at the beginning of the trading day because of the overnight news and economic data. There is also a lot of buying on market orders and short covering at the opening. Smart, experienced investors start trading closer to the end of the day having the opportunity to evaluate market performance. Therefore, the basic strategy is to bet against the morning price trend and bet with the evening price trend. The SMI may be calculated for many markets and market indices (S&P 500, DJIA, etc.) The SMI sends no clear signal whether the market is bullish or bearish. There are also no fixed absolute or relative readings signaling about the trend. Traders need to look at the SMI dynamics relative to that of the market. If, for example, SMI rises sharply when the market falls, this fact would mean that smart money is buying, and the market is to revert to an uptrend soon. The opposite situation is also true. A rapidly falling SMI during a bullish market means that smart money is selling and that market is to revert to a downtrend soon. The SMI is, therefore, a trend-based indicator. Some analysts use the smart money index to claim that precious metals such as gold will continually maintain value in the future. You can change long to short in the Input Settings WARNING: - For purpose educate only - This script to change bars colors.