From 8610999b1454be5f5c6942b5bc7cfcd4c1ff264f Mon Sep 17 00:00:00 2001 From: MkDev11 Date: Sun, 8 Mar 2026 02:02:25 +0100 Subject: [PATCH] feat(tide-chart): interactive probability calculator, horizon toggle, auto-refresh Closes #12 - Convert static HTML dashboard to Flask web server with API endpoints - Add interactive probability calculator with linear interpolation across 9 percentile levels (POST /api/probability) - Add variable time horizon toggle (1h/24h) with dynamic data refresh (GET /api/data?horizon=1h|24h) - Add manual refresh button and 5-minute auto-refresh with live indicator - 24h horizon includes all 9 assets (equities + crypto); 1h is crypto-only - Dynamic benchmark selection (SPY for 24h, BTC for 1h crypto) - Benchmark column headers update dynamically on horizon switch - Add 20 new focused tests covering horizon logic, probability edge cases, interpolation, and all Flask API endpoints (34 total, all pass) Files changed: - chart.py: horizon-aware fetch, get_assets_for_horizon, ALL_ASSETS, calculate_target_probability, add_relative_to_benchmark - main.py: Flask app with routes, dynamic benchmark header updates, refactored HTML into composable builders - requirements.txt: added flask>=3.0.0 - README.md: updated for new interactive features and API docs - tests/test_tool.py: 20 new tests for all interactive features --- tools/tide-chart/README.md | 53 +- tools/tide-chart/chart.py | 106 ++- tools/tide-chart/main.py | 1327 +++++++++++++-------------- tools/tide-chart/requirements.txt | 1 + tools/tide-chart/tests/test_tool.py | 332 ++++++- 5 files changed, 1041 insertions(+), 778 deletions(-) diff --git a/tools/tide-chart/README.md b/tools/tide-chart/README.md index b6a8d50..f4453a3 100644 --- a/tools/tide-chart/README.md +++ b/tools/tide-chart/README.md @@ -1,33 +1,36 @@ # Tide Chart -> Interactive dashboard comparing 24-hour probability cones for 5 equities using Synth forecasting data. +> Interactive Flask dashboard comparing probability cones for equities and crypto using Synth forecasting data. ## Overview -Tide Chart overlays probabilistic price forecasts for SPY, NVDA, TSLA, AAPL, and GOOGL into a single comparison view. It normalizes all forecasts to percentage change, enabling direct comparison across different price levels, and generates a ranked summary table with key metrics. +Tide Chart overlays probabilistic price forecasts into a single comparison view with an interactive web interface. It supports both equities (SPY, NVDA, TSLA, AAPL, GOOGL) on the 24h horizon and crypto/commodities (BTC, ETH, SOL, XAU) on both 1h and 24h horizons. All forecasts are normalized to percentage change for direct comparison across different price levels. -The tool addresses three questions from the forecast data: -- **Directional alignment** - Are all equities moving the same way? -- **Relative magnitude** - Which equity has the widest expected range? -- **Asymmetric skew** - Is the upside or downside tail larger, individually and relative to SPY? +The tool provides: +- **Probability cones** - Interactive Plotly chart with 5th-95th percentile bands +- **Probability calculator** - Enter a target price to see the exact probability of an asset reaching it +- **Variable time horizons** - Toggle between Intraday (1H) and Next Day (24H) views +- **Live auto-refresh** - Manual refresh button and configurable 5-minute auto-refresh +- **Ranked metrics table** - Sortable table with directional alignment, skew, and relative benchmarks ## How It Works -1. Fetches `get_prediction_percentiles` and `get_volatility` for each of the 5 equities (24h horizon) -2. Normalizes all 289 time steps from raw price to `% change = (percentile - current_price) / current_price * 100` -3. Computes metrics from the final time step (end of 24h window): +1. Starts a Flask server serving the interactive dashboard at `http://localhost:5000` +2. Fetches `get_prediction_percentiles` and `get_volatility` for assets in the selected horizon +3. Normalizes time steps from raw price to `% change = (percentile - current_price) / current_price * 100` +4. Computes metrics from the final time step (end of forecast window): - **Median Move** - 50th percentile % change - **Upside/Downside** - 95th and 5th percentile distances - **Directional Skew** - upside minus downside (positive = bullish asymmetry) - **Range** - total 5th-to-95th percentile width - - **Relative to SPY** - each metric minus SPY's value -4. Ranks equities by median expected move (table columns are sortable by click) -5. Generates an interactive Plotly HTML dashboard and opens it in the browser + - **Relative to Benchmark** - each metric minus benchmark (SPY for equities, BTC for crypto) +5. Ranks assets by median expected move (table columns are sortable by click) +6. Probability calculator uses linear interpolation across 9 percentile levels to estimate P(price <= target) ## Synth Endpoints Used -- `get_prediction_percentiles(asset, horizon="24h")` - Provides 289 time-step probabilistic forecast with 9 percentile levels (0.5% to 99.5%). Used for the probability cone overlay and all derived metrics. -- `get_volatility(asset, horizon="24h")` - Provides forecasted average volatility. Displayed in the ranking table as an independent risk measure. +- `get_prediction_percentiles(asset, horizon)` - Provides time-step probabilistic forecast with 9 percentile levels (0.5% to 99.5%). Used for probability cones, metrics, and the probability calculator. +- `get_volatility(asset, horizon)` - Provides forecasted average volatility. Displayed in the ranking table as an independent risk measure. ## Usage @@ -35,25 +38,27 @@ The tool addresses three questions from the forecast data: # Install dependencies pip install -r requirements.txt -# Run the tool (opens dashboard in browser) +# Run the dashboard server (opens browser automatically) python main.py +# Custom port +TIDE_CHART_PORT=8080 python main.py + # Run tests python -m pytest tests/ -v ``` -## Example Output - -The dashboard contains two sections: - -**Probability Cone Comparison** - Interactive Plotly chart with semi-transparent bands (5th-95th percentile) and median lines for each equity. Hover to see exact values at any time step. +## API Endpoints -**Equity Rankings** - Sortable table showing price, median move (% and $), forecasted volatility, directional skew (% and $), probability range (% and $), median vs SPY, and skew vs SPY. Click any column header to re-sort. Values are color-coded green (positive) or red (negative), with nominal dollar amounts shown alongside percentages for immediate context. +- `GET /` - Serves the interactive dashboard HTML +- `GET /api/data?horizon=24h` - Returns chart traces, table rows, and insights as JSON +- `POST /api/probability` - Calculates target price probability (body: `{"asset": "SPY", "target_price": 600, "horizon": "24h"}`) ## Technical Details - **Language:** Python 3.10+ -- **Dependencies:** plotly (for chart generation) -- **Synth Assets Used:** SPY, NVDA, TSLA, AAPL, GOOGL -- **Output:** Single HTML file (requires internet for Plotly CDN and fonts; no server needed) +- **Dependencies:** plotly, flask +- **Equities (24h only):** SPY, NVDA, TSLA, AAPL, GOOGL +- **Crypto + Commodities (1h & 24h):** BTC, ETH, SOL, XAU +- **Output:** Flask web server with Plotly CDN (requires internet for fonts/plotly) - **Mock Mode:** Works without API key using bundled mock data diff --git a/tools/tide-chart/chart.py b/tools/tide-chart/chart.py index 7c36fac..3f03362 100644 --- a/tools/tide-chart/chart.py +++ b/tools/tide-chart/chart.py @@ -1,9 +1,9 @@ """ Data processing module for the Tide Chart dashboard. -Fetches prediction percentiles and volatility for 5 equities, +Fetches prediction percentiles and volatility for supported assets, normalizes to percentage change, calculates comparison metrics, -and ranks equities by forecast outlook. +ranks assets by forecast outlook, and computes target price probabilities. """ import sys @@ -11,22 +11,42 @@ sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../..")) -from synth_client import SynthClient - EQUITIES = ["SPY", "NVDA", "TSLA", "AAPL", "GOOGL"] +CRYPTO_ASSETS = ["BTC", "ETH", "SOL", "XAU"] PERCENTILE_KEYS = ["0.005", "0.05", "0.2", "0.35", "0.5", "0.65", "0.8", "0.95", "0.995"] +PERCENTILE_LEVELS = [0.005, 0.05, 0.2, 0.35, 0.5, 0.65, 0.8, 0.95, 0.995] + + +ALL_ASSETS = EQUITIES + CRYPTO_ASSETS + + +def get_assets_for_horizon(horizon: str) -> list[str]: + """Return the list of supported assets for a given time horizon. + + Equities (SPY, NVDA, TSLA, AAPL, GOOGL) only support 24h. + Crypto + XAU (BTC, ETH, SOL, XAU) support both 1h and 24h. + The 24h horizon includes all assets. + """ + if horizon == "1h": + return list(CRYPTO_ASSETS) + return list(ALL_ASSETS) + +def fetch_all_data(client, horizon: str = "24h") -> dict: + """Fetch prediction percentiles and volatility for all assets in a horizon. -def fetch_all_data(client): - """Fetch prediction percentiles and volatility for all 5 equities. + Args: + client: SynthClient instance. + horizon: "1h" or "24h". Returns: dict: {asset: {"percentiles": ..., "volatility": ..., "current_price": float}} """ + assets = get_assets_for_horizon(horizon) data = {} - for asset in EQUITIES: - forecast = client.get_prediction_percentiles(asset, horizon="24h") - vol = client.get_volatility(asset, horizon="24h") + for asset in assets: + forecast = client.get_prediction_percentiles(asset, horizon=horizon) + vol = client.get_volatility(asset, horizon=horizon) data[asset] = { "current_price": forecast["current_price"], "percentiles": forecast["forecast_future"]["percentiles"], @@ -56,9 +76,9 @@ def normalize_percentiles(percentiles, current_price): def calculate_metrics(data): - """Calculate comparison metrics for each equity. + """Calculate comparison metrics for each asset. - Uses the final time step (end of 24h window) for metric computation. + Uses the final time step (end of forecast window) for metric computation. Args: data: Dict from fetch_all_data(). @@ -102,20 +122,30 @@ def calculate_metrics(data): return metrics -def add_relative_to_spy(metrics): - """Add relative-to-SPY fields for each equity. +def add_relative_to_benchmark(metrics) -> dict: + """Add relative-to-benchmark fields for each asset. + + Uses SPY as benchmark for equities, BTC for crypto assets. Args: metrics: Dict from calculate_metrics(). Returns: - Same dict with added relative_median and relative_skew fields. + Same dict with added relative_median, relative_skew, and benchmark fields. """ - spy = metrics["SPY"] + assets = list(metrics.keys()) + benchmark = "SPY" if "SPY" in metrics else assets[0] + bench_m = metrics[benchmark] for asset, m in metrics.items(): - m["relative_median"] = m["median_move"] - spy["median_move"] - m["relative_skew"] = m["skew"] - spy["skew"] - return metrics + m["relative_median"] = m["median_move"] - bench_m["median_move"] + m["relative_skew"] = m["skew"] - bench_m["skew"] + return metrics, benchmark + + +def add_relative_to_spy(metrics): + """Add relative-to-SPY fields for each equity (legacy wrapper).""" + result, _ = add_relative_to_benchmark(metrics) + return result def rank_equities(metrics, sort_by="median_move", ascending=False): @@ -135,7 +165,7 @@ def rank_equities(metrics, sort_by="median_move", ascending=False): def get_normalized_series(data): - """Get full normalized time series for all equities (for charting). + """Get full normalized time series for all assets (for charting). Args: data: Dict from fetch_all_data(). @@ -149,3 +179,41 @@ def get_normalized_series(data): info["percentiles"], info["current_price"] ) return series + + +def calculate_target_probability(percentiles: list[dict], target_price: float) -> float: + """Calculate the probability of an asset reaching a target price. + + Uses the final time step's percentile distribution and linear interpolation + to estimate P(price <= target). Returns the probability as a percentage (0-100). + + Args: + percentiles: List of percentile dicts (time steps). Uses the final step. + target_price: The target price to evaluate. + + Returns: + float: Probability (0-100) that the price will be at or below the target. + """ + final_step = percentiles[-1] + prices = [final_step[k] for k in PERCENTILE_KEYS] + levels = PERCENTILE_LEVELS + + # Target below the lowest percentile + if target_price <= prices[0]: + return levels[0] * 100 + + # Target above the highest percentile + if target_price >= prices[-1]: + return levels[-1] * 100 + + # Linear interpolation between bracketing percentiles + for i in range(len(prices) - 1): + if prices[i] <= target_price <= prices[i + 1]: + price_range = prices[i + 1] - prices[i] + if price_range == 0: + return levels[i] * 100 + fraction = (target_price - prices[i]) / price_range + prob = levels[i] + fraction * (levels[i + 1] - levels[i]) + return prob * 100 + + return 50.0 diff --git a/tools/tide-chart/main.py b/tools/tide-chart/main.py index a351f68..bc905df 100644 --- a/tools/tide-chart/main.py +++ b/tools/tide-chart/main.py @@ -1,84 +1,76 @@ +import sys +import os + +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../..")) + """ -Tide Chart - Equity Forecast Comparison Dashboard. +Tide Chart - Interactive Equity & Crypto Forecast Dashboard. -Generates an interactive HTML dashboard comparing 24h probability cones -for 5 equities (SPY, NVDA, TSLA, AAPL, GOOGL) using Synth API data. -Opens the dashboard in the default browser. +Flask-based dashboard with probability cones, target price calculator, +variable time horizons (1h/24h), and live auto-refresh. """ -import sys -import os import json import webbrowser -import tempfile +import threading from datetime import datetime, timedelta, timezone from zoneinfo import ZoneInfo -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../..")) - +from flask import Flask, jsonify, request, Response from synth_client import SynthClient from chart import ( fetch_all_data, calculate_metrics, - add_relative_to_spy, + add_relative_to_benchmark, rank_equities, get_normalized_series, + calculate_target_probability, + get_assets_for_horizon, ) -EQUITY_COLORS = { +ASSET_COLORS = { "SPY": {"primary": "#e8d44d", "rgb": "232,212,77"}, "NVDA": {"primary": "#3db8e8", "rgb": "61,184,232"}, "TSLA": {"primary": "#e85a6e", "rgb": "232,90,110"}, "AAPL": {"primary": "#9b6de8", "rgb": "155,109,232"}, "GOOGL": {"primary": "#4dc87a", "rgb": "77,200,122"}, + "BTC": {"primary": "#f7931a", "rgb": "247,147,26"}, + "ETH": {"primary": "#627eea", "rgb": "98,126,234"}, + "SOL": {"primary": "#00ffa3", "rgb": "0,255,163"}, + "XAU": {"primary": "#ffd700", "rgb": "255,215,0"}, } -EQUITY_LABELS = { +ASSET_LABELS = { "SPY": "S&P 500", "NVDA": "NVIDIA", "TSLA": "Tesla", "AAPL": "Apple", "GOOGL": "Alphabet", + "BTC": "Bitcoin", + "ETH": "Ethereum", + "SOL": "Solana", + "XAU": "Gold", } +# Backwards compat aliases +EQUITY_COLORS = ASSET_COLORS +EQUITY_LABELS = ASSET_LABELS -def generate_dashboard_html(normalized_series, metrics, ranked): - """Generate a self-contained HTML dashboard. - Args: - normalized_series: {asset: list of normalized percentile dicts} (289 steps) - metrics: {asset: {median_move, upside, downside, skew, range_pct, - volatility, current_price, relative_median, relative_skew}} - ranked: List of (asset, metrics_dict) sorted by median_move. - - Returns: - str: Complete HTML document string. - """ - timestamp = datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC") - - # Generate ET time axis (289 steps x 5 min = 24h) - et = ZoneInfo("America/New_York") - now_et = datetime.now(et) - time_points = [ - (now_et + timedelta(minutes=i * 5)).strftime("%Y-%m-%dT%H:%M") - for i in range(289) - ] - - # Build Plotly traces for probability cones +def build_traces(normalized_series: dict, metrics: dict, time_points: list[str]) -> list[dict]: + """Build Plotly trace dicts for probability cones.""" traces = [] - for asset in ["SPY", "NVDA", "TSLA", "AAPL", "GOOGL"]: + for asset in normalized_series: series = normalized_series[asset] - steps = time_points - color = EQUITY_COLORS[asset] - label = EQUITY_LABELS[asset] + color = ASSET_COLORS[asset] + label = ASSET_LABELS[asset] upper = [s.get("0.95", 0) for s in series] lower = [s.get("0.05", 0) for s in series] median = [s.get("0.5", 0) for s in series] - # Upper bound (invisible line for fill) traces.append({ - "x": steps, + "x": time_points, "y": upper, "type": "scatter", "mode": "lines", @@ -89,9 +81,8 @@ def generate_dashboard_html(normalized_series, metrics, ranked): "hoverinfo": "skip", }) - # Lower bound with fill to upper traces.append({ - "x": steps, + "x": time_points, "y": lower, "type": "scatter", "mode": "lines", @@ -104,7 +95,6 @@ def generate_dashboard_html(normalized_series, metrics, ranked): "hoverinfo": "skip", }) - # Median line - pre-format hover text (d3-format unreliable in unified hover) current_price = metrics[asset]["current_price"] hover_text = [] for v in median: @@ -113,7 +103,7 @@ def generate_dashboard_html(normalized_series, metrics, ranked): sign_nom = "+" if nom >= 0 else "-" hover_text.append(f"{sign_pct}{v:.2f}% ({sign_nom}${abs(nom):,.2f})") traces.append({ - "x": steps, + "x": time_points, "y": median, "customdata": hover_text, "type": "scatter", @@ -123,19 +113,20 @@ def generate_dashboard_html(normalized_series, metrics, ranked): "name": f"{label} ({asset})", "hovertemplate": ( f"{label}
" - "%{{x|%I:%M %p}}
" - "Median: %{{customdata}}" + "%{x|%I:%M %p}
" + "Median: %{customdata}" "" ), }) + return traces - traces_json = json.dumps(traces) - # Build rank table rows - table_rows = "" +def build_table_rows(ranked: list, benchmark: str) -> str: + """Build HTML table rows for ranked assets.""" + rows = "" for rank_idx, (asset, m) in enumerate(ranked, 1): - color = EQUITY_COLORS[asset]["primary"] - label = EQUITY_LABELS[asset] + color = ASSET_COLORS[asset]["primary"] + label = ASSET_LABELS[asset] def fmt_val(val, nominal=None, suffix="%"): sign = "+" if val > 0 else "" @@ -147,10 +138,10 @@ def fmt_val(val, nominal=None, suffix="%"): return f'{pct_str} ({nom_str})' return f'{pct_str}' - rel_median = "-" if asset == "SPY" else fmt_val(m["relative_median"]) - rel_skew = "-" if asset == "SPY" else fmt_val(m["relative_skew"]) + rel_median = "-" if asset == benchmark else fmt_val(m["relative_median"]) + rel_skew = "-" if asset == benchmark else fmt_val(m["relative_skew"]) - table_rows += f""" + rows += f""" {rank_idx} @@ -167,669 +158,607 @@ def fmt_val(val, nominal=None, suffix="%"): {rel_median} {rel_skew} """ + return rows + - # Build directional alignment indicator +def build_insights(metrics: dict) -> dict: + """Compute insight card data from metrics.""" directions = [m["median_move"] for m in metrics.values()] - alignment_text = "All Bullish" if all(d > 0 for d in directions) else \ - "All Bearish" if all(d < 0 for d in directions) else "Mixed" - alignment_class = "bullish" if all(d > 0 for d in directions) else \ - "bearish" if all(d < 0 for d in directions) else "mixed" + if all(d > 0 for d in directions): + alignment_text, alignment_class = "All Bullish", "bullish" + elif all(d < 0 for d in directions): + alignment_text, alignment_class = "All Bearish", "bearish" + else: + alignment_text, alignment_class = "Mixed", "mixed" - # Widest range equity widest = max(metrics.items(), key=lambda x: x[1]["range_pct"]) - widest_name = f"{EQUITY_LABELS[widest[0]]} ({widest[1]['range_pct']:.2f}%)" + widest_name = f"{ASSET_LABELS[widest[0]]} ({widest[1]['range_pct']:.2f}%)" - # Most skewed equity most_skewed = max(metrics.items(), key=lambda x: abs(x[1]["skew"])) skew_dir = "upside" if most_skewed[1]["skew"] > 0 else "downside" - skew_name = f"{EQUITY_LABELS[most_skewed[0]]} ({skew_dir})" - - html = f""" - - - - -Tide Chart - Equity Forecast Comparison - - - - -
- -
-
-

Tide Chart

- 24h Forecast -
-

Equity probability cone comparison — {timestamp}

-
- -
-
-
Directional Alignment
-
{alignment_text}
-
-
-
Widest Range
-
{widest_name}
-
-
-
Most Asymmetric
-
{skew_name}
-
-
- -
-
- Probability Cones (5th - 95th Percentile) - -
-
-
click legend to toggle assets · scroll to zoom · drag to pan · double-click to reset
-
- -
-
- Equity Rankings - -
- - - - - - - - - - - - - - - - - {table_rows} - -
#AssetPriceMedian Move\u25B4\u25BEVolatility\u25B4\u25BESkew\u25B4\u25BERange\u25B4\u25BE24h Bounds\u25B4\u25BEvs SPY\u25B4\u25BESkew vs SPY\u25B4\u25BE
-
- -
- Data from Synth API - · Built with Venth -
- -
- - - -""" + skew_name = f"{ASSET_LABELS[most_skewed[0]]} ({skew_dir})" - return html + return { + "alignment_text": alignment_text, + "alignment_class": alignment_class, + "widest_name": widest_name, + "skew_name": skew_name, + } -def main(): - """Fetch data, build dashboard, open in browser.""" - import warnings - with warnings.catch_warnings(): - warnings.simplefilter("ignore") - client = SynthClient() +def make_time_points(horizon: str) -> list[str]: + """Generate ET timezone time axis for the given horizon.""" + et = ZoneInfo("America/New_York") + now_et = datetime.now(et) + if horizon == "1h": + steps = 61 + interval_min = 1 + else: + steps = 289 + interval_min = 5 + return [ + (now_et + timedelta(minutes=i * interval_min)).strftime("%Y-%m-%dT%H:%M") + for i in range(steps) + ] - print("Fetching equity data...") - data = fetch_all_data(client) - print("Calculating metrics...") +def fetch_and_process(client, horizon: str = "24h") -> dict: + """Fetch data, compute metrics, and build all dashboard components.""" + data = fetch_all_data(client, horizon=horizon) metrics = calculate_metrics(data) - metrics = add_relative_to_spy(metrics) + metrics, benchmark = add_relative_to_benchmark(metrics) ranked = rank_equities(metrics, sort_by="median_move") normalized = get_normalized_series(data) + time_points = make_time_points(horizon) + traces = build_traces(normalized, metrics, time_points) + table_rows = build_table_rows(ranked, benchmark) + insights = build_insights(metrics) + + assets_with_prices = { + asset: {"current_price": info["current_price"]} + for asset, info in data.items() + } + + return { + "traces": traces, + "table_rows": table_rows, + "insights": insights, + "metrics": { + asset: {k: v for k, v in m.items()} + for asset, m in metrics.items() + }, + "assets": assets_with_prices, + "benchmark": benchmark, + "horizon": horizon, + "timestamp": datetime.now(timezone.utc).strftime("%Y-%m-%d %H:%M UTC"), + } + + +def generate_dashboard_html(client) -> str: + """Generate the full interactive HTML dashboard.""" + result = fetch_and_process(client, "24h") + traces_json = json.dumps(result["traces"]) + assets_json = json.dumps(result["assets"]) + horizon_label = "24h Forecast" + benchmark = result["benchmark"] + ins = result["insights"] + timestamp = result["timestamp"] + table_rows = result["table_rows"] + + # The HTML uses raw braces for JS/CSS, so we use explicit concatenation + # where Python formatting is needed, and raw strings for JS blocks. + html = ( + '\n\n\n' + '\n' + '\n' + 'Tide Chart - Forecast Comparison\n' + '\n' + "\n\n\n" + '
\n' + "\n" + '
\n' + '
\n' + '

Tide Chart

\n' + f' {horizon_label}\n' + "
\n" + f'

Probability cone comparison — {timestamp}

\n' + "
\n" + "\n" + '
\n' + '
\n' + ' \n' + ' \n' + "
\n" + ' \n' + ' \n" + "
\n" + "\n" + '
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\n' + ' Probability Calculator\n' + ' \n' + "
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\n" + "\n" + '
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\n' + '
Directional Alignment
\n' + f'
{ins["alignment_text"]}
\n' + "
\n" + '
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Widest Range
\n' + f'
{ins["widest_name"]}
\n' + "
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Most Asymmetric
\n' + f'
{ins["skew_name"]}
\n' + "
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\n' + '
\n' + ' Probability Cones (5th - 95th Percentile)\n' + ' \n' + "
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click legend to toggle assets · scroll to zoom · drag to pan · double-click to reset
\n' + "
\n" + "\n" + '
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\n' + ' Asset Rankings\n' + ' \n' + "
\n" + ' \n' + " \n" + " \n" + " \n" + " \n" + " \n" + ' \n' + ' \n' + ' \n' + ' \n' + ' \n' + f' \n' + f' \n' + " \n" + " \n" + f" {table_rows}\n" + " \n" + "
#AssetPriceMedian Move\u25B4\u25BEVolatility\u25B4\u25BESkew\u25B4\u25BERange\u25B4\u25BEBounds\u25B4\u25BEvs {benchmark}\u25B4\u25BESkew vs {benchmark}\u25B4\u25BE
\n" + "
\n" + "\n" + '
\n' + ' Data from Synth API\n' + " · Built with Venth\n" + "
\n" + "\n" + "
\n" + "\n" + "\n" + "\n" + ) + return html + - print("Generating dashboard...") - html = generate_dashboard_html(normalized, metrics, ranked) +def create_app(client=None) -> Flask: + """Create the Flask application with all routes.""" + if client is None: + import warnings + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + client = SynthClient() + + app = Flask(__name__) + + @app.route("/") + def index(): + html = generate_dashboard_html(client) + return Response(html, mimetype="text/html") + + @app.route("/api/data") + def api_data(): + horizon = request.args.get("horizon", "24h") + if horizon not in ("1h", "24h"): + return jsonify({"error": "Invalid horizon. Use '1h' or '24h'."}), 400 + result = fetch_and_process(client, horizon) + return jsonify({ + "traces": result["traces"], + "table_rows": result["table_rows"], + "insights": result["insights"], + "assets": result["assets"], + "benchmark": result["benchmark"], + "horizon": horizon, + "timestamp": result["timestamp"], + }) + + @app.route("/api/probability", methods=["POST"]) + def api_probability(): + body = request.get_json(silent=True) or {} + asset = body.get("asset", "") + target_price = body.get("target_price") + horizon = body.get("horizon", "24h") + + if horizon not in ("1h", "24h"): + return jsonify({"error": "Invalid horizon."}), 400 + + valid_assets = get_assets_for_horizon(horizon) + if asset not in valid_assets: + return jsonify({"error": f"{asset} not available for {horizon} horizon."}), 400 + + if target_price is None or not isinstance(target_price, (int, float)) or target_price <= 0: + return jsonify({"error": "Invalid target_price. Must be a positive number."}), 400 + + try: + forecast = client.get_prediction_percentiles(asset, horizon=horizon) + percentiles = forecast["forecast_future"]["percentiles"] + current_price = forecast["current_price"] + prob_below = calculate_target_probability(percentiles, target_price) + return jsonify({ + "asset": asset, + "target_price": target_price, + "current_price": current_price, + "horizon": horizon, + "probability_below": round(prob_below, 4), + "probability_above": round(100.0 - prob_below, 4), + }) + except Exception as e: + return jsonify({"error": str(e)}), 500 + + return app + + +def main(): + """Start the Tide Chart dashboard server.""" + import warnings + with warnings.catch_warnings(): + warnings.simplefilter("ignore") + client = SynthClient() - out_path = os.path.join(tempfile.gettempdir(), "tide_chart.html") - with open(out_path, "w", encoding="utf-8") as f: - f.write(html) + app = create_app(client) + port = int(os.environ.get("TIDE_CHART_PORT", 5000)) - print(f"Dashboard saved to {out_path}") - webbrowser.open(f"file://{out_path}") + print(f"Tide Chart running at http://localhost:{port}") + threading.Timer(1.0, lambda: webbrowser.open(f"http://localhost:{port}")).start() + app.run(host="0.0.0.0", port=port, debug=False) if __name__ == "__main__": diff --git a/tools/tide-chart/requirements.txt b/tools/tide-chart/requirements.txt index 73e33e5..0f8f77b 100644 --- a/tools/tide-chart/requirements.txt +++ b/tools/tide-chart/requirements.txt @@ -1,2 +1,3 @@ plotly>=5.0.0 requests>=2.28.0 +flask>=3.0.0 diff --git a/tools/tide-chart/tests/test_tool.py b/tools/tide-chart/tests/test_tool.py index 095d465..2a480d4 100644 --- a/tools/tide-chart/tests/test_tool.py +++ b/tools/tide-chart/tests/test_tool.py @@ -1,32 +1,40 @@ +import sys +import os + +# Add project root to path +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../..")) +# Add tool directory to path +sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) + """ Tests for the Tide Chart tool. All tests run against mock data (no API key needed). They verify data fetching, normalization, metric calculation, -ranking, and dashboard generation. +ranking, dashboard generation, horizon toggling, probability +calculation, and Flask API endpoints. """ -import sys -import os +import json import warnings -# Add project root to path -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../../..")) -# Add tool directory to path -sys.path.insert(0, os.path.join(os.path.dirname(__file__), "..")) - from synth_client import SynthClient from chart import ( - EQUITIES, + CRYPTO_ASSETS, + ALL_ASSETS, PERCENTILE_KEYS, + PERCENTILE_LEVELS, fetch_all_data, normalize_percentiles, calculate_metrics, add_relative_to_spy, + add_relative_to_benchmark, rank_equities, get_normalized_series, + get_assets_for_horizon, + calculate_target_probability, ) -from main import generate_dashboard_html +from main import generate_dashboard_html, create_app, build_insights, make_time_points def _make_client(): @@ -42,12 +50,12 @@ def test_client_loads_in_mock_mode(): def test_fetch_all_equities_data(): - """Verify fetch_all_data returns data for all 5 equities.""" + """Verify fetch_all_data returns data for all 9 assets (24h default).""" client = _make_client() data = fetch_all_data(client) - assert len(data) == 5 - for asset in EQUITIES: + assert len(data) == 9 + for asset in ALL_ASSETS: assert asset in data assert "current_price" in data[asset] assert "percentiles" in data[asset] @@ -83,7 +91,7 @@ def test_calculate_metrics_median_move(): data = fetch_all_data(client) metrics = calculate_metrics(data) - for asset in EQUITIES: + for asset in ALL_ASSETS: m = metrics[asset] final = data[asset]["percentiles"][-1] cp = data[asset]["current_price"] @@ -97,7 +105,7 @@ def test_calculate_metrics_skew(): data = fetch_all_data(client) metrics = calculate_metrics(data) - for asset in EQUITIES: + for asset in ALL_ASSETS: m = metrics[asset] assert abs(m["skew"] - (m["upside"] - m["downside"])) < 1e-10 @@ -108,7 +116,7 @@ def test_calculate_metrics_range(): data = fetch_all_data(client) metrics = calculate_metrics(data) - for asset in EQUITIES: + for asset in ALL_ASSETS: m = metrics[asset] assert abs(m["range_pct"] - (m["upside"] + m["downside"])) < 1e-10 @@ -127,7 +135,9 @@ def test_relative_to_spy(): assert metrics["SPY"]["relative_median"] == 0.0 assert metrics["SPY"]["relative_skew"] == 0.0 - for asset in ["NVDA", "TSLA", "AAPL", "GOOGL"]: + for asset in ALL_ASSETS: + if asset == "SPY": + continue m = metrics[asset] expected_rel_median = m["median_move"] - spy_median expected_rel_skew = m["skew"] - spy_skew @@ -143,7 +153,7 @@ def test_rank_equities_sorting(): metrics = add_relative_to_spy(metrics) ranked = rank_equities(metrics, sort_by="median_move") - assert len(ranked) == 5 + assert len(ranked) == 9 for i in range(len(ranked) - 1): assert ranked[i][1]["median_move"] >= ranked[i + 1][1]["median_move"] @@ -166,8 +176,8 @@ def test_get_normalized_series(): data = fetch_all_data(client) series = get_normalized_series(data) - assert len(series) == 5 - for asset in EQUITIES: + assert len(series) == 9 + for asset in ALL_ASSETS: assert asset in series assert len(series[asset]) == 289 # First step should be near 0 (current price normalized) @@ -178,26 +188,18 @@ def test_get_normalized_series(): def test_generate_dashboard_html(): """Verify dashboard HTML generation produces valid output.""" client = _make_client() - data = fetch_all_data(client) - metrics = calculate_metrics(data) - metrics = add_relative_to_spy(metrics) - ranked = rank_equities(metrics, sort_by="median_move") - normalized = get_normalized_series(data) - - html = generate_dashboard_html(normalized, metrics, ranked) + html = generate_dashboard_html(client) assert isinstance(html, str) assert "" in html assert "Tide Chart" in html assert "plotly" in html.lower() - # Check all equity tickers appear - for asset in EQUITIES: + # Check all asset tickers appear (default 24h = all assets) + for asset in ALL_ASSETS: assert asset in html # Check table has rows assert "" in html assert "cone-chart" in html - # Check relative_skew column exists (Skew vs SPY header) - assert "Skew vs SPY" in html # Check sortable table headers assert "sortable" in html assert "data-sort" in html @@ -206,8 +208,7 @@ def test_generate_dashboard_html(): assert "$" in html # Check legendgroup is set for trace grouping assert "legendgroup" in html - # Check 24h Bounds column - assert "24h Bounds" in html + # Check Bounds column assert "data-sort=\"bounds\"" in html # Check column header tooltips assert "data-tip=" in html @@ -221,6 +222,16 @@ def test_generate_dashboard_html(): assert "yaxis.autorange" in html # Check tooltip focus support assert "data-tip]:focus-visible::after" in html + # Check new interactive elements + assert "horizon-toggle" in html + assert "Intraday (1H)" in html + assert "Next Day (24H)" in html + assert "Probability Calculator" in html + assert "calc-asset" in html + assert "calc-price" in html + assert "auto-refresh" in html.lower() + assert "/api/data" in html + assert "/api/probability" in html def test_calculate_metrics_nominal_values(): @@ -229,7 +240,7 @@ def test_calculate_metrics_nominal_values(): data = fetch_all_data(client) metrics = calculate_metrics(data) - for asset in EQUITIES: + for asset in ALL_ASSETS: m = metrics[asset] final = data[asset]["percentiles"][-1] cp = data[asset]["current_price"] @@ -253,7 +264,7 @@ def test_calculate_metrics_projection_bounds(): data = fetch_all_data(client) metrics = calculate_metrics(data) - for asset in EQUITIES: + for asset in ALL_ASSETS: m = metrics[asset] final = data[asset]["percentiles"][-1] @@ -270,11 +281,240 @@ def test_volatility_values(): data = fetch_all_data(client) metrics = calculate_metrics(data) - for asset in EQUITIES: + for asset in ALL_ASSETS: assert metrics[asset]["volatility"] > 0 assert isinstance(metrics[asset]["volatility"], float) +# --- New tests for issue #12 interactive features --- + + +def test_get_assets_for_horizon_24h(): + """Verify 24h horizon returns all assets (equities + crypto).""" + assets = get_assets_for_horizon("24h") + assert assets == ALL_ASSETS + + +def test_get_assets_for_horizon_1h(): + """Verify 1h horizon returns crypto assets.""" + assets = get_assets_for_horizon("1h") + assert assets == CRYPTO_ASSETS + + +def test_fetch_all_data_1h_horizon(): + """Verify fetch_all_data returns crypto data for 1h horizon.""" + client = _make_client() + data = fetch_all_data(client, horizon="1h") + + assert len(data) == len(CRYPTO_ASSETS) + for asset in CRYPTO_ASSETS: + assert asset in data + assert "current_price" in data[asset] + assert "percentiles" in data[asset] + assert "average_volatility" in data[asset] + assert isinstance(data[asset]["percentiles"], list) + assert len(data[asset]["percentiles"]) > 0 + + +def test_add_relative_to_benchmark_equities(): + """Verify benchmark is SPY for 24h (all assets).""" + client = _make_client() + data = fetch_all_data(client, horizon="24h") + metrics = calculate_metrics(data) + metrics, benchmark = add_relative_to_benchmark(metrics) + + assert benchmark == "SPY" + assert metrics["SPY"]["relative_median"] == 0.0 + assert metrics["SPY"]["relative_skew"] == 0.0 + + +def test_add_relative_to_benchmark_crypto(): + """Verify benchmark is BTC for crypto assets.""" + client = _make_client() + data = fetch_all_data(client, horizon="1h") + metrics = calculate_metrics(data) + metrics, benchmark = add_relative_to_benchmark(metrics) + + assert benchmark == "BTC" + assert metrics["BTC"]["relative_median"] == 0.0 + assert metrics["BTC"]["relative_skew"] == 0.0 + + +def test_calculate_target_probability_within_range(): + """Verify probability calculation returns value between bounds.""" + client = _make_client() + data = fetch_all_data(client, horizon="24h") + percentiles = data["SPY"]["percentiles"] + current_price = data["SPY"]["current_price"] + + prob = calculate_target_probability(percentiles, current_price) + assert 0 < prob < 100 + + +def test_calculate_target_probability_extreme_low(): + """Verify probability for very low target clamps to lowest level.""" + client = _make_client() + data = fetch_all_data(client, horizon="24h") + percentiles = data["SPY"]["percentiles"] + + prob = calculate_target_probability(percentiles, 0.01) + assert prob == PERCENTILE_LEVELS[0] * 100 + + +def test_calculate_target_probability_extreme_high(): + """Verify probability for very high target clamps to highest level.""" + client = _make_client() + data = fetch_all_data(client, horizon="24h") + percentiles = data["SPY"]["percentiles"] + + prob = calculate_target_probability(percentiles, 999999.0) + assert prob == PERCENTILE_LEVELS[-1] * 100 + + +def test_calculate_target_probability_interpolation(): + """Verify linear interpolation with synthetic data.""" + # Construct a minimal percentile step + step = {k: float(i + 1) * 10 for i, k in enumerate(PERCENTILE_KEYS)} + # step: {"0.005": 10, "0.05": 20, "0.2": 30, ...} + percentiles = [step] + + # Target exactly at a percentile boundary + prob = calculate_target_probability(percentiles, 20.0) + assert abs(prob - PERCENTILE_LEVELS[1] * 100) < 1e-6 # 5.0 + + # Target midway between 2nd and 3rd percentile (20.0 and 30.0) + midpoint = 25.0 + prob = calculate_target_probability(percentiles, midpoint) + expected = (PERCENTILE_LEVELS[1] + 0.5 * (PERCENTILE_LEVELS[2] - PERCENTILE_LEVELS[1])) * 100 + assert abs(prob - expected) < 1e-6 + + +def test_make_time_points_24h(): + """Verify 24h generates 289 time points.""" + points = make_time_points("24h") + assert len(points) == 289 + + +def test_make_time_points_1h(): + """Verify 1h generates 61 time points.""" + points = make_time_points("1h") + assert len(points) == 61 + + +def test_build_insights(): + """Verify insight card data structure.""" + client = _make_client() + data = fetch_all_data(client) + metrics = calculate_metrics(data) + ins = build_insights(metrics) + + assert "alignment_text" in ins + assert "alignment_class" in ins + assert "widest_name" in ins + assert "skew_name" in ins + assert ins["alignment_class"] in ("bullish", "bearish", "mixed") + + +def test_flask_index_route(): + """Verify Flask index route returns HTML.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.get("/") + assert resp.status_code == 200 + assert b"Tide Chart" in resp.data + assert b"" in resp.data + + +def test_flask_api_data_24h(): + """Verify /api/data returns valid JSON for 24h.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.get("/api/data?horizon=24h") + assert resp.status_code == 200 + data = json.loads(resp.data) + assert "traces" in data + assert "table_rows" in data + assert "insights" in data + assert "assets" in data + assert data["horizon"] == "24h" + assert data["benchmark"] == "SPY" + + +def test_flask_api_data_1h(): + """Verify /api/data returns valid JSON for 1h.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.get("/api/data?horizon=1h") + assert resp.status_code == 200 + data = json.loads(resp.data) + assert data["horizon"] == "1h" + assert data["benchmark"] == "BTC" + assert "BTC" in data["assets"] + + +def test_flask_api_data_invalid_horizon(): + """Verify /api/data rejects invalid horizon.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.get("/api/data?horizon=7d") + assert resp.status_code == 400 + data = json.loads(resp.data) + assert "error" in data + + +def test_flask_api_probability_valid(): + """Verify /api/probability returns correct structure.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.post("/api/probability", + data=json.dumps({"asset": "SPY", "target_price": 600.0, "horizon": "24h"}), + content_type="application/json") + assert resp.status_code == 200 + data = json.loads(resp.data) + assert "probability_below" in data + assert "probability_above" in data + assert "current_price" in data + assert abs(data["probability_below"] + data["probability_above"] - 100.0) < 0.01 + + +def test_flask_api_probability_invalid_asset(): + """Verify /api/probability rejects asset not in horizon.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.post("/api/probability", + data=json.dumps({"asset": "SPY", "target_price": 600.0, "horizon": "1h"}), + content_type="application/json") + assert resp.status_code == 400 + data = json.loads(resp.data) + assert "not available" in data["error"] + + +def test_flask_api_probability_invalid_price(): + """Verify /api/probability rejects non-positive price.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.post("/api/probability", + data=json.dumps({"asset": "SPY", "target_price": -10, "horizon": "24h"}), + content_type="application/json") + assert resp.status_code == 400 + + +def test_flask_api_probability_missing_body(): + """Verify /api/probability handles missing JSON body.""" + client = _make_client() + app = create_app(client) + with app.test_client() as tc: + resp = tc.post("/api/probability", content_type="application/json") + assert resp.status_code == 400 + + if __name__ == "__main__": test_client_loads_in_mock_mode() test_fetch_all_equities_data() @@ -290,4 +530,24 @@ def test_volatility_values(): test_calculate_metrics_projection_bounds() test_generate_dashboard_html() test_volatility_values() + test_get_assets_for_horizon_24h() + test_get_assets_for_horizon_1h() + test_fetch_all_data_1h_horizon() + test_add_relative_to_benchmark_equities() + test_add_relative_to_benchmark_crypto() + test_calculate_target_probability_within_range() + test_calculate_target_probability_extreme_low() + test_calculate_target_probability_extreme_high() + test_calculate_target_probability_interpolation() + test_make_time_points_24h() + test_make_time_points_1h() + test_build_insights() + test_flask_index_route() + test_flask_api_data_24h() + test_flask_api_data_1h() + test_flask_api_data_invalid_horizon() + test_flask_api_probability_valid() + test_flask_api_probability_invalid_asset() + test_flask_api_probability_invalid_price() + test_flask_api_probability_missing_body() print("All tests passed!")