Nifty Breakout Levels Strategy (v7 Hybrid)Nifty Breakout Levels Strategy (v7 Hybrid – Compounding from Start Date)
Instrument / TF: Designed for current-month NIFTY futures on 1-hour timeframe, with at most 1 trade per day.
Entry logic: Uses a 10-bar breakout box with a 0.3% buffer, plus EMA-based trend + proximity filter.
Longs: price in breakout-high zone, above EMA50/EMA200 and within proximityPts.
Shorts: price in breakout-low zone and strong downtrend (EMA10 < EMA20 < EMA50 < EMA200, price below EMA200).
Trades only when ATR(14) > atrTradeThresh and during regular hours (till 15:15).
Risk / exits: Stop loss is ATR-adaptive – max of slBasePoints (100 pts) and ATR * atrSLFactor; TP is fixed (tpPoints, e.g. 350 pts).
Longs have stepped trailing profit levels (100/150/200/250/320 pts) that lock in gains on pullbacks.
Shorts have trailing loss-reduction levels (80/120/140 pts) to cut improving losses.
Additional exit: 1H EMA50 2-bar reversal against the position, plus optional EOD flatten at 3:15 PM.
Compounding engine: From a chosen start date, equity is rebased to startCapital, and lot size scales dynamically as equity / capitalPerLot, with automatic lot reductions at three drawdown thresholds (ddCut1 / 2 / 3).
Automation: All entries and exits are exposed via alertconditions (long/short entry & exit) so the strategy can be connected to broker/webhook automation.
Indikatoren und Strategien
MTF EMA Hariss 369The strategy has been prepared in a simplistic manner and easy to understand the concept by any novice trader.
Indicators used:
Current Time frame 20 EMA- Gives clear look about current time frame dynamic support and resistance and trend as well.
Higher Time Frame 20 EMA: Gives macro level trend, support and resistance
Kama: Capture volatility and trend direction.
RVOL: Main factor of price movement.
Buy when price closes above current time frame 20 ema and current time frame 20 ema is above higher time frame 20 ema. Stop loss just below the low of last candle. One can use current time frame 20 ema, higher time frame 20 ema or kama as stop loss depending upon type of asset class and risk appetite. The ideal way is to keep 20 ema as trailing sl if one wants to trail with trend.
Sell when price closes below current time frame 20 ema and current time frame 20 ema is lower than higher time frame 20 ema. Stop loss just above high of last candle.
Ideal target is 1.5 or 2 times of stop loss.
Entry and exit time depends on trading style. Eg. if you want to enter and exit in 5 min time frame, then choose 15 min or 1h as higher time frame as trend filter. Buy and sell signals are also plotted based on this strategy. One should always go with the higher time frame trend. Opting higher time frame trend filter always filters out market noises.
15m ORB Breakout NAS100 (5m Mgmt) v6 - OptimizedOpening Range Breakout Strategy
Buy and sell signals are given upon break of market session opening range. Best utilized for 30 minute NY opening range, managed on 5 min timeframe on NAS100. Tweak the settings for higher win rate on backtesting dashboard before implementing strategy.
15m & 1h Breakout — NY Prev Window Define a session anchored at 09:15 New York time, adjusted safely around weekends.
For each new session, store the high and low of the previous session’s 09:15→09:15 window.
During a configurable entry window (default: 09:30–11:15 NY time), watch for close-based breakouts:
Long when price closes above the previous window high + buffer.
Short when price closes below the previous window low − buffer.
Take exactly one trade per session, with fixed TP/SL in pips, and optional:
EMA trend filters for longs and shorts.
Range (volatility) filter on the previous window.
Option to skip Thursdays.
The strategy is designed mainly for intraday timeframes (e.g. 15m / 1h), but the logic is timeframe-agnostic.
4H Confirmation + 1H SFP BOS Retest4H Confirmation + 1H Entry (SFP + BOS + Retest)Run it on 1H
Uses 4H EMAs for higher-timeframe direction (confirmation)
Uses 1H SFP + BOS + retest + RSI for entries
This gives you more trades, still guided by the 4H trend
stormytrading orb botshows entries for 15m orb based on 5m break and retest made solely for mnq or nq, works good with smt
shows trades for ldn, nyc, nyc overlap and Asia session, pls follow stormy trading on insta for more
HMA+RVOL Strategy Hariss 369The Hull Moving Average (HMA) is a smooth, fast, and highly responsive moving average created by Alan Hull. It reduces lag significantly while still maintaining smoothness, making it one of the most popular tools for trend detection and entries. It is widely used for trend filter. Hull Moving Average(HMA) with RVOL strengthens the trend as volume is prime factor of price movement.
Trading with HMA: Simple method is buy when price closes above HMA , stop less below the low of last candle and target is 1.5 or 2 times of stop loss. The reverse is for sell. The HMA automatically turns to green on bull trend and red on bear trend for better visual confirmation.
Adding RVOL to HMA is better method of trading. Buy signal is initiated when price closes above HMA and RVOL is greater than 1.2. Sell signal is initiated when price closes below 89 HMA and rovl is greater than 1.2. One can change the value of RVOL according to trading style and type asset being traded.
It is a back tested strategy.
yangwen1.0This script is an initial concept of mine. I attempted to use the 5-minute chart as ticks for catching bottoms and picking tops, but it's unable to avoid whipsaws. I've tested many methods to evade whipsaws, but they ultimately result in poor entry points, causing me to miss the bottoms and tops of price swings. I sincerely hope someone with better approaches can discuss this with me. Thank you.
DEMA ATR Strategy [PrimeAutomation]⯁ OVERVIEW
The DEMA ATR Strategy combines trend-following logic with adaptive volatility filters to identify strong momentum phases and manage trades dynamically.
It uses a Double Exponential Moving Average (DEMA) anchored to ATR volatility bands, creating a self-adjusting trend baseline.
When the adjusted DEMA shifts direction, the strategy enters positions and scales out profit in phases based on ATR-driven targets.
This system adapts to volatility, filters noise, and seeks sustained directional moves.
⯁ KEY FEATURES
DEMA-Volatility Hybrid Filter
Uses Double EMA with ATR expansion/compression logic to form a dynamic trend baseline.
Directional Shift Entries
Entries occur when the adjusted DEMA flips trend (bullish crossover or bearish crossunder vs its past value).
Noise Reduction Mechanism
ATR range caps extreme moves and prevents false flips during choppy volatility spikes.
Multi-Level Take Profits
Targets scale out positions at 1×, 2×, and 3× ATR multiples in the trade direction.
Volatility-Adaptive Targets
ATR multiplier ensures profit targets expand/contract based on market conditions.
Single-Direction Exposure
No pyramiding; the strategy flips position only when trend shifts.
Automated Trade Finalization
When all profit targets trigger, the position is fully closed.
⯁ STRATEGY LOGIC
Trend Direction:
DEMA baseline is modified using ATR upper/lower envelopes.
• If the adjusted DEMA rises above previous value → Bullish
• If it falls below previous value → Bearish
Entry Rules:
• Enter Long when bullish shift occurs and no long position exists
• Enter Short when bearish shift occurs and no short position exists
Take Profit Logic:
3 partial exits for each trade based on ATR:
• TP1 = ±1× ATR
• TP2 = ±2× ATR
• TP3 = ±3× ATR
Profit distribution: 30% / 30% / 40%
Exit Conditions:
• Exit when all TPs hit (full scale-out if sum of all TPs 100%)
• Opposite trend signal closes current trade and opens new one
⯁ WHEN TO USE
Trending environments
Medium–high volatility phases
Swing trading and intraday trend plays
Markets that respect momentum continuation (crypto, indices, FX majors)
⯁ CONCLUSION
This strategy blends DEMA trend recognition with ATR-based volatility adaptation to generate cleaner directional entries and structured take-profit exits. It is designed to capture momentum phases while avoiding noise-driven false signals, delivering a disciplined and scalable trend-following approach.
BTC 30 m Long singal Asset: Bitcoin only
Timeframe: 30 minutes
Entry Conditions (Long):
MACD histogram turns from red to green (negative to positive)
Stochastic K line crosses above D line AND this crossover happens below the lower band (20)
RSI is above the middle band (50)
ai cruhsera pullback strategy to donchain lower and upperbands.. best for cypro lower timeframe scalping..
Crypto Edition 0.2This strategy is built on a trend-following approach, designed to capture sustained market momentum rather than predict reversals.its a pullback strategy. The goal is to stay aligned with the prevailing trend, ride strong moves, avoid ranging-market noiseE
Alt Trading: FuturesOne
The FuturesOne Indicator + Strategy will be continuously enhanced to ensure our users receive the most effective and profit-focused trading system at the best possible value. Version 0 (V0) of the FuturesOne Strategy is built on a refined Opening Range Breakout (ORB) framework, augmented with a quantitative regime-detection and filtering layer. This design allows users to tailor their approach: they may opt for consistent daily ORB opportunities or select a mode that applies quantitative filters to surface fewer, but higher-probability, trade setups.
Crypto Grid 2025+ Long Only (Asym TP)Crypto Grid 2025+ Long Only (Asymmetric Take-Profit) is a long-only mean-reversion grid strategy designed for intraday cryptocurrency trading.
The core idea is to accumulate long positions as price moves downward within a locally defined price range and to exit positions on upward retracements.
The strategy automatically builds a multi-level grid between the highest and lowest price over a user-defined lookback period (“range length”). Each grid level acts as a potential entry point when price crosses it from above.
Key Features
1. Long-only grid logic
The strategy opens long positions only, progressively increasing exposure as price moves into lower grid levels.
2. Asymmetric take-profit mechanism
Instead of taking profit strictly at the next grid level, the strategy allows targeting multiple levels above the entry point. This increases the average profit per winning trade and shifts the reward-to-risk profile toward larger, less frequent wins.
3. Optional partial take-profit
A portion of each trade can be closed at the nearest grid level, while the remainder is held for a more distant asymmetric target. This balances consistency and profit potential.
4. Volume-based market filter
Entries can be restricted to periods of healthy market activity by requiring volume to exceed a moving-average baseline.
5. Capital-scaled position sizing
Position size is determined by risk percentage, grid spacing, and a dynamic sizing mode (original / conservative / aggressive).
6. Built-in risk controls
global stop below the lower boundary of the range,
global take-profit above the upper boundary,
automatic shutdown after a configurable loss-streak.
Market Philosophy
This strategy belongs to the mean-reversion family: it expects short-term overshoots to revert back toward mid-range liquidity zones.
It is not trend-following.
It performs best in choppy, range-bound, or slow-grinding markets — especially on liquid crypto pairs.
Recommended Use Cases
Short timeframes (1–15 minutes)
High-liquidity crypto pairs
Sideways or rotational price action
Exchanges with low fees (due to higher order count)
Not Intended For
Strong trending markets without pullbacks
Assets with thin order books
Use with leverage without additional risk controls
Summary
Crypto Grid 2025+ Long Only (Asymmetric TP) is a refined grid-based mean-reversion strategy optimized for modern crypto markets. Its asymmetric take-profit framework is specifically engineered to reduce the classical issue of “small wins and large occasional losses” found in traditional grid systems, giving it a more favorable long-term trade distribution.
AkdakTrading1Script using M5 Order Blocks with an FVG and the first blocks of an impulse to take trades with a 1:1 risk-reward.
ZanScritp 1:3 | 21.00-22.00 WIB | XAUUSD TF 5MStrategy Overview (Short & Simple Explanation)
This strategy focuses on taking high-quality trades during a specific hour of the day (20:00–21:00 WIB), when market movement is often more reliable. It looks for clear trends, avoids extreme market conditions, and only trades when volatility is healthy.
It uses a fixed Risk–Reward of 1:2, meaning every trade aims for twice the potential profit compared to the risk. Stop Loss (SL) and Take Profit (TP) levels are set immediately when a trade opens and never move afterward.
When a buy or sell signal appears, the strategy automatically draws:
An entry line
A Stop Loss line
A Take Profit line
A label showing the trade information
The system is designed to avoid “repainting,” ensuring trade entries stay consistent, while SL and TP always trigger exactly when price touches them—creating a stable and predictable trading workflow.
Target Audience
This strategy is designed for:
1. Beginner to Intermediate Traders
Those who want a simple, rule-based system focused on:
Clear trend direction
Fixed risk-reward
Easy-to-understand SL/TP logic
2. Scalpers & Intraday Traders
Traders who prefer:
Short trading windows
High-probability session filtering
Clean execution without repainting
Oracle Protocol — Arch Public (Testing Clone) Oracle Protocol — Arch Public Series (testing clone)
This model implements the Arch Public Oracle structure: a systematic accumulation-and-distribution engine built around a dynamic Accumulation Cost Base (ACB), strict profit-gate exit logic, and a capital-bounded flywheel reinvestment system.
It is designed for transparent execution, deterministic behaviour, and rule-based position management.
Core Function Set
1. Accumulation Framework (ACB-Driven)
The accumulation engine evaluates market movement against defined entry conditions, including:
Percentage-based entry-drop triggers
Optional buy-below-ACB mode
Capital-gated entries tied to available ledger balance
Fixed-dollar and min-dollar entry rules (as seen in Arch public materials)
Automated sizing through flywheel capital
Range-bounded ledger for controlled backtesting input
Each qualifying buy updates the live ACB, maintains the internal ledger, and forms the next reference point for exit evaluation.
No forecasting mechanisms are included.
2. Profit-Gate Exit System
Exits are governed by the standard Arch public approach:
A sealed ACB reference for threshold evaluation
Optional live-ACB visibility
Profit-gate triggers defined per asset class
Candle-confirmation integration (“ProfitGate + Candle” mode)
Distribution only when the smallest active threshold is met
This provides a consistent cadence with published Arch diagrams and PDFs.
3. Once-Per-Rally Governance
After a distribution, the algorithm locks until price retraces below the most recent accumulation base.
Only after re-arming can the next profit gate activate.
This prevents over-frequency selling and aligns with the public-domain Oracle behaviour.
4. Quiet-Bars & Threshold Cluster Control
A volatility-stabilisation layer prevents multiple exits from micro-fluctuations or transient spikes.
This ensures clean execution during fast markets and high volatility.
5. Flywheel Reinvestment
Distribution proceeds automatically return to the capital pool where permitted, creating a closed system of:
Entry sizing
Exit proceeds
Ledger-managed capital state
All sizing respects capital boundaries and does not breach dollar floors or overrides.
6. Automation Hooks and Integration
The script exposes:
3Commas-compatible JSON sizing
Entry/exit signalling via alertcondition()
Deterministic event reporting suitable for external automation
This allows consistent deployment across automated execution environments.
7. Visual Tooling
Optional displays include:
Live ACB line
Exit-guide markers
Capital, state, and ledger panels
Realized/unrealized outcome tracking based on internal logic only
Visual components do not influence execution rules.
Operating Notes
This model is rule-based, deterministic, and non-predictive.
It executes only according to the explicit thresholds, capital limits, and state transitions defined within the script.
No discretionary or forward-looking logic is included.
CSS_LFU_v0.1Overview:
A multi-factor, market-adaptive swing strategy designed for intraday and short-term crypto trading. It synthesizes momentum, volatility, and trend signals into a unified composite score over a configurable lookback window. The strategy leverages a modular, signal-weighted approach to ensure robust entry timing while remaining compatible with human-in-the-loop validation and algorithmic execution.
Core Modules:
AJFFRSI (RSX-based Momentum): Measures smoothed price momentum with noise-reduction filters to detect crossovers relative to the QQE trailing stop.
QQE (Quantitative Qualitative Easing RSI): A modified RSI with a dynamic trailing stop that adapts to short-term volatility, identifying exhaustion and potential reversal points.
Keltner Channel Zones: Determines overextension relative to trend, providing buy/sell zones based on ATR-banded EMA.
WaveTrend Oscillator: Confirms short-term swings and market direction through smoothed oscillator cross signals.
Rolling Composite Score: Aggregates module signals over a unified lookback (e.g., 144 bars) to normalize noise and capture consistent trends.
Signal Logic:
Each module outputs a discrete score (+1 / 0 / -1).
The rolling composite score sums all module scores over the lookback period.
Long positions trigger when the rolling score meets or exceeds the long threshold.
Short positions trigger when the rolling score meets or falls below the short threshold.
Multi-dimensional signal aggregation reduces false positives from single indicators.
Rolling lookback ensures score normalization across different volatility regimes.
Highly modular: easy to adapt modules or weights to different instruments or timeframes.
Fully compatible with automated execution pipelines, including custom exchange screener bots.
Use Case:
Ideal for quant-driven altcoin or multi-asset strategies where high-frequency validation is critical and sequential module weighting enhances trend flip detection.
