MetaTrader5 MCP Server

# Glossary This glossary explains technical terms used in mtdata with simple language and real-world trading examples. --- ## Basic Trading Concepts (Start Here) If you're new to trading, skim this section first. It's enough to follow the examples in this repo. ### Long vs Short - **Long**: you profit if price goes **up** (buy now, sell later). - **Short**: you profit if price goes **down** (sell now, buy back later). ### TP/SL (Take Profit / Stop Loss) - **Take Profit (TP)**: close a position when price reaches your profit target. - **Stop Loss (SL)**: close a position to cap the loss if price moves against you. ### Bid/Ask and Spread - **Ask**: the price you pay to buy. - **Bid**: the price you receive when you sell. - **Spread**: `ask - bid` (a cost you pay to enter/exit). See also: [Spread](#spread). ### Pip (and Pipette) - A **pip** is a standard unit of FX price movement (often `0.0001`, or `0.01` for JPY pairs). See also: [Pip](#pip). - Some brokers quote an extra digit (“pipettes”): `1 pip = 10 pipettes`. ### Timeframe and Candles (Bars) - A **timeframe** (e.g., `M5`, `H1`, `D1`) controls how data is grouped. - A **candle/bar** summarizes that period (open/high/low/close and volume). ### Lot Size Forex position sizes are often expressed in **lots** (see: [Lot Size](#lot-size)). --- ## Core Concepts ### Time Series A sequence of data points indexed by time. In trading, this is typically OHLCV data (Open, High, Low, Close, Volume) at regular intervals. **Example:** 500 hourly candles of EURUSD form a time series. ### Horizon How many bars into the future a forecast predicts. **Example:** `--horizon 12` with H1 timeframe means "predict the next 12 hours." ### Lookback How many historical bars a model uses to make its prediction. **Example:** `--lookback 500` tells the model to learn from the last 500 candles. --- ## Forecasting Methods ### Theta Method A simple, robust forecasting technique that decomposes a time series into trend and seasonality components. **When to use:** As a baseline for short-to-medium horizons. Often surprisingly accurate. **Example:** ```bash python cli.py forecast_generate EURUSD --timeframe H1 --horizon 12 --model theta ``` ### ARIMA (AutoRegressive Integrated Moving Average) A classical statistical model that predicts future values based on: - **AR (AutoRegressive):** Past values of the series - **I (Integrated):** Differencing to make data stationary - **MA (Moving Average):** Past forecast errors **When to use:** Short-term forecasting when data shows clear autocorrelation patterns. **Interpretation:** ARIMA works best on *stationary* data (constant mean/variance). Raw prices aren't stationary, but returns usually are. ### ETS (Error, Trend, Seasonality) Exponential smoothing that weighs recent observations more heavily than older ones. **When to use:** Data with clear seasonal patterns (e.g., higher volatility during market opens). ### Monte Carlo Simulation Generates thousands of possible future price paths by randomly sampling from historical return distributions. **When to use:** When you need a *range* of outcomes rather than a single prediction. Essential for risk sizing and barrier analysis. **Example:** ```bash python cli.py forecast_generate EURUSD --timeframe H1 --horizon 12 \ --model mc_gbm --model-params "n_sims=2000" ``` **Interpretation:** Instead of one forecast line, you get percentile bands showing where price might land. ### Chronos A foundation model for time series (like GPT for text). Pre-trained on millions of time series, then applied to your data. **When to use:** When you want state-of-the-art accuracy without tuning. Requires `chronos` and `torch` packages. **Example:** ```bash python cli.py forecast_generate EURUSD --timeframe H1 --horizon 24 \ --library pretrained --model chronos2 ``` --- ## Uncertainty & Intervals ### Confidence Interval A range around the forecast indicating uncertainty. A 95% CI means "if model assumptions hold, the true value will fall within this range 95% of the time." **Caution:** Financial data often violates model assumptions (fat tails, regime changes), so intervals may be too narrow. ### Conformal Intervals A distribution-free method to create prediction intervals. Instead of assuming a bell curve, it uses historical forecast errors to calibrate the band width. **How it works:** 1. Run a small rolling backtest 2. Collect actual errors at each horizon step 3. Use error quantiles to set band width **When to use:** When you don't trust model-based intervals. Conformal intervals are empirically calibrated. **Example:** ```bash python cli.py forecast_conformal_intervals EURUSD --timeframe H1 \ --method theta --horizon 12 --steps 25 --alpha 0.1 ``` --- ## Regime Detection ### Regime The current "behavior mode" of the market. Different regimes require different strategies. **Common regimes:** | Regime | Characteristics | Strategy Implication | |--------|-----------------|---------------------| | **Low Volatility / Ranging** | Price oscillates in a narrow band | Mean reversion works well | | **Trending** | Price moves directionally | Trend-following works well | | **High Volatility / Crisis** | Large, unpredictable swings | Reduce position sizes | ### Hidden Markov Model (HMM) An algorithm that assumes the market switches between hidden "states" (regimes). It estimates which state generated the current data based on observed returns and volatility. **Output:** State ID (0, 1, 2...) and confidence probability. **Important:** The model doesn't know "Bull" or "Bear." You must interpret what each state means by examining its mean return and volatility. **Example:** ```bash python cli.py regime_detect EURUSD --timeframe H1 --method hmm --params "n_states=2" ``` **Interpretation:** - State 0: Low volatility (σ = 0.0003) → Ranging market - State 1: High volatility (σ = 0.0008) → Trending or volatile market ### Change-Point Detection (BOCPD) Bayesian Online Change Point Detection. Answers: "Did the market's underlying behavior just change?" **Output:** Probability (0-1) that each bar represents a regime shift. **When to use:** - If probability > 0.5, the previous pattern may be breaking down - Useful for detecting breakouts or structural changes **Example:** ```bash python cli.py regime_detect EURUSD --timeframe H1 --method bocpd --threshold 0.5 ``` --- ## Volatility ### Volatility A measure of how much price typically moves. Higher volatility = larger price swings. **Types:** | Type | Definition | |------|------------| | **Realized** | How much price *did* move (historical) | | **Forecasted** | How much we expect it *will* move | | **Implied** | Market's expectation (from options prices) | ### EWMA (Exponentially Weighted Moving Average) A volatility estimator that gives more weight to recent observations. **Parameter:** `lambda` (0.94 is standard). Higher = slower adaptation; lower = faster reaction to new data. **Example:** ```bash python cli.py forecast_volatility_estimate EURUSD --timeframe H1 \ --horizon 12 --method ewma --params "lambda=0.94" ``` **Interpretation:** If output is `sigma_bar_return: 0.0006`, expect hourly returns to have ~0.06% standard deviation. ### GARCH (Generalized Autoregressive Conditional Heteroskedasticity) A model that captures *volatility clustering*—the tendency for high-volatility periods to follow high-volatility periods. **When to use:** When you observe that big moves cluster together. Common in equity and FX markets. ### HAR-RV (Heterogeneous AutoRegressive Realized Volatility) Uses realized volatility at multiple time scales (daily, weekly, monthly) to forecast future volatility. **When to use:** When you have access to high-frequency intraday data and want more accurate volatility forecasts. --- ## Barrier Analysis ### Barrier A price level that triggers an event when touched: - **Take Profit (TP):** Close position at profit target - **Stop Loss (SL):** Close position to limit loss ### Triple-Barrier Labeling A method to label historical data based on which barrier was hit first: - **+1 (Win):** TP hit first - **-1 (Loss):** SL hit first - **0 (Neutral):** Neither hit within horizon **Use case:** Creating labels for machine learning models. --- ## Barrier Optimization Objectives When optimizing TP/SL levels, you must choose what to maximize. Each objective answers a different trading question. ### Edge **What it measures:** The raw probability advantage of winning vs losing. **Formula:** `Edge = P(TP first) - P(SL first)` **Example:** Edge = 0.20 means you win 20% more often than you lose. **When to use:** - General-purpose objective for consistent advantage - When you want high win rates regardless of payoff size - Good starting point for most strategies **Limitation:** Ignores how much you win/lose. A setup with 60% wins but tiny TP and large SL may have positive edge but negative EV. --- ### Kelly Criterion **What it measures:** The optimal fraction of your capital to bet for maximum long-term growth. **Formula:** `Kelly = P(win) - P(loss) / (TP/SL)` **Example:** Kelly = 0.25 means bet 25% of capital per trade for maximum growth. **When to use:** - Position sizing decisions - When you want to grow capital as fast as possible - Long-term systematic trading **Limitation:** Full Kelly is aggressive and leads to large drawdowns. Most traders use "fractional Kelly" (e.g., 0.25 × Kelly). **Interpretation of output:** - `kelly`: Raw Kelly fraction based on all paths - `kelly_cond`: Kelly fraction based only on paths that resolved (TP or SL hit) --- ### EV (Expected Value) **What it measures:** The average profit per trade, accounting for both probability and payoff size. **Formula:** `EV = P(win) × TP - P(loss) × SL` **Example:** EV = 0.15 means you expect to gain 0.15% per trade on average. **When to use:** - When payoff asymmetry matters (small wins, big losses or vice versa) - Comparing setups with different TP/SL ratios - Maximizing total profit over many trades **Variants:** - `ev`: Based on all paths (includes no-hit scenarios as 0) - `ev_cond`: Based only on resolved paths (ignores trades that never hit TP or SL) --- ### EV Per Bar **What it measures:** Expected value normalized by how long the trade takes. **Formula:** `EV_per_bar = EV / mean_time_to_resolution` **Example:** Setup A has EV=0.30 but takes 20 bars; Setup B has EV=0.15 but takes 5 bars. Setup B has higher EV per bar (0.03 vs 0.015). **When to use:** - When capital turnover matters (you want fast trades to reinvest) - Comparing setups across different timeframes - Scalping and high-frequency strategies **Limitation:** Favors fast trades, which may have higher transaction costs. --- ### Prob Resolve **What it measures:** The probability that the trade closes (hits either TP or SL) within the horizon. **Formula:** `Prob_resolve = P(TP first) + P(SL first) = 1 - P(no hit)` **Example:** Prob_resolve = 0.85 means 85% of trades close before the time limit. **When to use:** - When you want trades to actually complete - Avoiding setups where most trades expire without hitting targets - Day trading where you must close by end of session **Limitation:** High resolve probability doesn't mean profitable—could resolve at a loss. --- ### Profit Factor **What it measures:** Ratio of expected gains to expected losses. **Formula:** `Profit_factor = (P(win) × TP) / (P(loss) × SL)` **Example:** Profit factor = 2.0 means you expect $2 in wins for every $1 in losses. **When to use:** - When you want a risk/reward focused metric - Comparing profitability of different strategies - Common in backtesting reports **Interpretation:** - Profit factor > 1: Profitable - Profit factor > 2: Very good - Profit factor < 1: Losing money --- ### Min Loss Prob **What it measures:** Minimizes the probability of losing (SL hit first). **Formula:** Minimize `P(SL first)` **Example:** Setup with min_loss_prob = 0.15 means only 15% chance of hitting stop loss. **When to use:** - Capital preservation is priority - Conservative trading styles - When you can't afford drawdowns (e.g., trading client money) **Limitation:** May result in very small TP targets or trades that rarely resolve. --- ### Utility (Log Utility) **What it measures:** Risk-adjusted expected value using logarithmic utility (diminishing returns on gains). **Formula:** `Utility = P(win) × log(1 + TP) + P(loss) × log(1 - SL)` **Example:** Log utility penalizes large losses more than it rewards equivalent gains. **When to use:** - Risk-averse traders - When you want to avoid ruin (log utility naturally avoids bets that could wipe you out) - Academic/theoretical contexts **Interpretation:** Higher utility = better risk-adjusted outcome. Negative utility means the trade is worse than doing nothing. --- ## Objective Selection Guide | Your Priority | Use Objective | Why | |---------------|---------------|-----| | Win rate | `edge` | Maximizes probability advantage | | Total profit | `ev` | Accounts for both probability and payoff | | Fast trades | `ev_per_bar` | Optimizes profit per unit time | | Position sizing | `kelly` | Tells you how much to bet | | Avoid losses | `min_loss_prob` | Minimizes chance of stop-loss hit | | Trade completion | `prob_resolve` | Ensures trades actually close | | Risk-adjusted | `utility` | Penalizes large losses | | Gross profit ratio | `profit_factor` | Gains vs losses ratio | --- ## Backtesting Metrics ### MAE (Mean Absolute Error) **What it measures:** Average size of forecast errors, ignoring direction. **Formula:** `MAE = average(|actual - predicted|)` **Example:** MAE = 0.0015 means forecasts are off by 0.15% on average. **Interpretation:** - Lower is better - Easy to interpret (same units as forecast) - Doesn't penalize large errors more than small ones --- ### RMSE (Root Mean Squared Error) **What it measures:** Average size of forecast errors, penalizing large errors more. **Formula:** `RMSE = sqrt(average((actual - predicted)²))` **Example:** RMSE = 0.0020 with MAE = 0.0015 suggests some large outlier errors. **Interpretation:** - Lower is better - Always ≥ MAE (equality means all errors are same size) - RMSE much larger than MAE → outliers are a problem --- ### Directional Accuracy **What it measures:** How often the forecast predicts the correct direction (up/down). **Example:** Directional accuracy = 0.58 means 58% of forecasts got the direction right. **Interpretation:** - 0.50 = random guessing - 0.55+ is generally useful for trading - High directional accuracy with high MAE: right direction, wrong magnitude --- ## Monte Carlo Simulation Terms ### n_sims (Number of Simulations) How many random price paths to generate. More simulations = more stable results but slower. **Guidelines:** - Quick checks: 1,000 - Normal use: 2,000-5,000 - Publication/final decisions: 10,000+ --- ### seed Random number generator seed. Set this for reproducible results. **Example:** `--params "seed=42"` ensures the same random paths each time. --- ### Drift (μ) The expected average return per period. In GBM simulations, drift represents the trend component. --- ### Diffusion (σ) The volatility component of price movement. Represents random fluctuation around the drift. --- ## Probability Terms ### prob_tp_first Probability that take-profit is hit before stop-loss. **Example:** prob_tp_first = 0.62 means 62% of simulated paths hit TP first. --- ### prob_sl_first Probability that stop-loss is hit before take-profit. **Example:** prob_sl_first = 0.28 means 28% of simulated paths hit SL first. --- ### prob_no_hit Probability that neither TP nor SL is hit within the horizon. **Example:** prob_no_hit = 0.10 means 10% of paths expire without hitting either barrier. **Important:** High prob_no_hit means your barriers may be too far or horizon too short. --- ### prob_resolve Probability that the trade completes (hits TP or SL). **Formula:** prob_resolve = prob_tp_first + prob_sl_first = 1 - prob_no_hit --- ## Grid Optimization Terms ### grid_style How to generate the TP/SL combinations to test: | Style | Description | Use When | |-------|-------------|----------| | `fixed` | Regular grid from min to max | You know the range to search | | `volatility` | Scales barriers to current volatility | Adaptive to market conditions | | `ratio` | Varies risk/reward ratios | You want specific RR profiles | | `preset` | Pre-configured for trading styles | Quick setup for scalp/swing/position | --- ### refine Two-stage optimization: coarse search, then fine search around the best result. **Example:** `--refine true --refine-radius 0.3` searches ±30% around the best initial candidate. --- ### top_k Number of best candidates to return from optimization. **Example:** `--top-k 5` returns the top 5 TP/SL combinations instead of just the best. --- ## Signal Processing ### Denoising Removing random fluctuations ("noise") to reveal the underlying trend ("signal"). **Trade-off:** More smoothing = clearer trend but more lag (delay). **Common methods:** | Method | Best For | |--------|----------| | `ema` | General smoothing | | `median` | Spike removal | | `kalman` | Adaptive filtering | | `wavelet` | Frequency-based separation | **Example:** ```bash python cli.py data_fetch_candles EURUSD --timeframe H1 --limit 500 \ --denoise ema --denoise-params "alpha=0.2" ``` ### Stationarity A statistical property where mean and variance don't change over time. Most forecasting models assume stationarity. **Problem:** Raw prices are *not* stationary (they trend up or down). **Solution:** Use returns (percent change) instead, which are typically stationary. --- ## Technical Indicators ### Moving Average The average price over the last N bars. Smooths out short-term fluctuations. - **SMA:** Simple Moving Average (equal weights) - **EMA:** Exponential Moving Average (recent bars weighted more) ### RSI (Relative Strength Index) A momentum oscillator measuring speed and change of price movements. Range: 0-100. **Interpretation:** - RSI > 70: Potentially overbought (consider selling) - RSI < 30: Potentially oversold (consider buying) - RSI = 50: Neutral ### MACD (Moving Average Convergence Divergence) Shows relationship between two moving averages. **Components:** - MACD line: Fast EMA - Slow EMA - Signal line: EMA of MACD line - Histogram: MACD - Signal **Interpretation:** When MACD crosses above Signal, bullish momentum. Below, bearish momentum. ### ATR (Average True Range) Measures volatility by averaging the true range (high-low including gaps) over N bars. **Use case:** Setting stop-loss distance. A common rule is `SL = 2 × ATR`. --- ## Pattern Recognition ### Candlestick Patterns Visual patterns formed by one or more candles that historically precede certain price moves. **Examples:** - **Engulfing:** Large candle completely covers previous candle (reversal signal) - **Doji:** Open ≈ Close (indecision) - **Hammer:** Small body with long lower wick (potential bottom) ### Chart Patterns Larger-scale geometric shapes formed over multiple candles. **Examples:** - **Head and Shoulders:** Three peaks, middle highest (reversal) - **Double Top/Bottom:** Two peaks/troughs at similar level - **Triangle:** Converging trendlines (breakout imminent) --- ## Data & Execution ### OHLCV Standard candle data format: - **O**pen: First price of the period - **H**igh: Highest price - **L**ow: Lowest price - **C**lose: Last price - **V**olume: Trading activity ### Market Depth (DOM) Order book showing pending buy/sell orders at various price levels. ### Slippage Difference between expected execution price and actual fill price. Occurs due to market movement or insufficient liquidity. **Example:** You place a market buy at 1.1750, but get filled at 1.1752. Slippage = 2 pips. **Why it matters:** Slippage reduces profits and increases losses. Account for it when backtesting. ### Pip Smallest price increment for a currency pair. For most pairs: 0.0001. For JPY pairs: 0.01. **Example:** EURUSD moves from 1.1750 to 1.1775 = 25 pips. **Note:** Some brokers show "pipettes" (5 decimal places), where 1 pip = 10 pipettes. ### Spread Difference between bid (sell) and ask (buy) price. This is a transaction cost. **Example:** Bid 1.1748, Ask 1.1750 → Spread = 2 pips. **Impact:** You start every trade 1 spread behind. Tight spreads are crucial for scalping. ### Lot Size Standard position size in forex: - **Standard lot:** 100,000 units - **Mini lot:** 10,000 units - **Micro lot:** 1,000 units **Example:** 0.1 lots of EURUSD = 10,000 EUR notional. ### Risk/Reward Ratio (RR) Ratio of potential profit to potential loss. **Formula:** RR = TP distance / SL distance **Example:** TP = 50 pips, SL = 25 pips → RR = 2.0 (you risk 25 to make 50). **Rule of thumb:** Higher RR means you can be wrong more often and still profit. ### Drawdown Peak-to-trough decline in account equity. **Example:** Account peaks at $10,000, drops to $8,500 → Drawdown = 15%. **Maximum drawdown:** Largest historical drawdown. Key risk metric. --- ## See Also - [Forecasting Guide](FORECAST.md) - [Regime Detection](forecast/REGIMES.md) - [Barrier Analysis](BARRIER_FUNCTIONS.md) - [Technical Indicators](TECHNICAL_INDICATORS.md)