People’s interactions can serve as a foundation for certain studies; here is a simple way to process data at low cost using Google Trends and lightweight Python packages such as SUA.
The script retrieves Google Trends search‑interest data for several Romanian economic keywords (such as munca, recesiune, somaj, inflatie, dobanzi) and I used copilot and gemini artificial intelligence. The copilot comes with bad result for fast asking simple scripts.
It analyzes the data using the SUA library to estimate simple metrics like interest growth and volatility, then displays the results in a PyQt6 interactive chart where the X‑axis shows real calendar dates and the Y‑axis shows the Google Trends 0–100 normalized interest scale.
However, the script is limited by Google Trends constraints:
Google Trends allows maximum 5 keywords per request, so the script cannot query more terms at once.
Google Trends treats diacritic and non‑diacritic words as different searches (e.g., șomaj ≠ somaj), so the script uses non‑diacritic versions to avoid errors and improve compatibility.
Google Trends returns normalized values (0–100), not real search counts, meaning the data shows relative interest, not absolute volume.
The time series resolution is fixed by Google (weekly data for 12 months), so the X‑axis length depends on how many points Google provides.
These limitations come from Google Trends itself, not from the script.
python -m pip install sua
Collecting sua
...
Successfully installed MarkupSafe-3.0.3 altair-6.1.0 asttokens-3.0.1 attrs-26.1.0 beautifulsoup4-4.14.3 blinker-1.9.0
cachetools-7.1.4 cffi-2.0.0 clarabel-0.11.1 click-8.4.1 cloudpickle-3.1.2 cmdstanpy-1.3.0 colorama-0.4.6 contourpy-1.3.2
curl_cffi-0.15.0 cvxpy-1.7.5 cycler-0.12.1 darts-0.44.1 datetime-6.0 decorator-5.3.1 empyrical-0.5.5 executing-2.2.1
fonttools-4.63.0 fpdf-1.7.2 gitdb-4.0.12 gitpython-3.1.50 holidays-0.97 httptools-0.7.1 importlib_resources-7.1.0
ipython-8.39.0 itsdangerous-2.2.0 jedi-0.20.0 jinja2-3.1.6 joblib-1.5.3 jsonschema-4.26.0 jsonschema-specifications-2025.9.1
kiwisolver-1.5.0 llvmlite-0.47.0 lxml-6.1.1 markdown-it-py-4.2.0 matplotlib-3.10.9 matplotlib-inline-0.2.2 mdurl-0.1.2
multitasking-0.0.13 narwhals-2.21.2 nfoursid-1.0.2 numba-0.65.1 numpy-2.2.6 osqp-1.1.1 pandas-2.3.3 pandas-datareader-0.10.0
parso-0.8.7 patsy-1.0.2 peewee-4.0.6 pillow-12.2.0 platformdirs-4.9.6 prompt_toolkit-3.0.52 prophet-1.3.0 protobuf-7.35.0
pure-eval-0.2.3 pyarrow-24.0.0 pycparser-3.0 pydeck-0.9.2 pygments-2.20.0 pyod-3.5.2 pyparsing-3.3.2 pyportfolioopt-1.6.0
python-dateutil-2.9.0.post0 python-multipart-0.0.29 pytz-2026.2 quantstats-0.0.81 referencing-0.37.0 rich-15.0.0 rpds-py-0.30.0
scikit-base-0.13.2 scikit-learn-1.7.2 scipy-1.15.3 scs-3.2.11 seaborn-0.13.2 shap-0.49.1 six-1.17.0 slicer-0.0.8 smmap-5.0.3
soupsieve-2.8.3 stack_data-0.6.3 stanio-0.5.1 starlette-1.1.0 statsmodels-0.14.6 streamlit-1.57.0 sua-1.1.5.1 tabulate-0.10.0
tenacity-9.1.4 threadpoolctl-3.6.0 toml-0.10.2 tqdm-4.67.3 traitlets-5.15.0 tzdata-2026.2 uvicorn-0.48.0 watchdog-6.0.0
wcwidth-0.7.0 websockets-16.0 xarray-2025.6.1 yfinance-1.4.0 zope.interface-8.4
python -m pip install pytrends
Collecting pytrends
...
Installing collected packages: pytrends
Successfully installed pytrends-4.9.2
python -m pip install plotly
Collecting plotly
...
Installing collected packages: plotly
...
Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.
Successfully installed plotly-6.7.0
Now, the plotty use an little tool and this want to access my Scripts folder by environment variables.
If you got this error, then you need to limit the words, because Google returns a 400 Bad Request when your request is not valid for the internal Google Trends API.
raise exceptions.ResponseError.from_response(response)
pytrends.exceptions.ResponseError: The request failed: Google returned a response with code 400
Let's run teh python script and output is this:
python main.py
Average interest growth (returns):
munca: 0.0072
recesiune: 0.3387
somaj: 0.0308
inflatie: 0.0660
dobanzi: 0.0157
Volatility (risk):
munca: 0.0256
recesiune: 2.9473
somaj: 0.1348
inflatie: 0.1607
dobanzi: 0.0527
The PyQt6 will create this bad result, what 1970 ?:
The issue in your script occurs because PyQt’s QDateTimeAxis expects the X‑axis timestamp to be expressed in milliseconds (since the Unix epoch), while Python’s .timestamp() function returns the value in seconds. When you send seconds instead of milliseconds, PyQt interprets a value that is 1000 times smaller, which places all chart points somewhere at the beginning of 1970 (right after January 1st, 1970).
Let's fix this issue:
from pytrends.request import TrendReq
# Din moment ce modulele de mai jos erau importate dar nefolosite în codul tău,
# m-am asigurat că restul rulării rămâne intactă.
try:
from sua import expected_returns, risk_models
except ImportError:
pass
from PyQt6.QtWidgets import QApplication, QMainWindow
from PyQt6.QtCharts import QChart, QChartView, QLineSeries, QDateTimeAxis, QValueAxis
from PyQt6.QtGui import QPainter
from PyQt6.QtCore import QPointF, QDateTime, Qt
import sys
# -----------------------------
# 1. Keywords (fără diacritice)
# -----------------------------
keywords = ["munca", "recesiune", "somaj", "inflatie", "dobanzi"]
# -----------------------------
# 2. Preluare date Pytrends
# -----------------------------
pytrends = TrendReq(hl='ro-RO', tz=180)
pytrends.build_payload(keywords, timeframe='today 12-m', geo='RO')
raw = pytrends.interest_over_time()
# Convertim în structuri simple
dates = list(raw.index) # Python datetime objects
data = {k: list(raw[k]) for k in keywords}
# -----------------------------
# 3. Calcul SUA (randament + risc)
# -----------------------------
returns = {k: [] for k in keywords}
for k in keywords:
series = data[k]
for i in range(1, len(series)):
prev = series[i-1] or 1
curr = series[i] or 1
returns[k].append((curr - prev) / prev)
mu = {k: sum(returns[k]) / len(returns[k]) for k in keywords}
def covariance(a, b):
mean_a = sum(a) / len(a)
mean_b = sum(b) / len(b)
return sum((a[i]-mean_a)*(b[i]-mean_b) for i in range(len(a))) / len(a)
S = {k: covariance(returns[k], returns[k]) for k in keywords}
print("Average interest growth (returns):")
for k, v in mu.items():
print(f"{k}: {v:.4f}")
print("\nVolatility (risk):")
for k, v in S.items():
print(f"{k}: {v:.4f}")
# -----------------------------
# 4. PyQt6 Chart GUI cu date reale (CORECTAT)
# -----------------------------
class Window(QMainWindow):
def __init__(self):
super().__init__()
self.setWindowTitle("Google Trends Economic Interest (RO)")
chart = QChart()
chart.setTitle("Interest Over Time (Google Trends Romania)")
# Axa X = date reale
axis_x = QDateTimeAxis()
axis_x.setFormat("yyyy-MM-dd")
axis_x.setTitleText("Date")
axis_x.setTickCount(10)
# !!! CORECȚIE 1: Să îi spunem axei care este limita minimă și maximă reală !!!
# Conversie din primul și ultimul obiect datetime din Pytrends în QDateTime
qt_start_date = QDateTime.fromMSecsSinceEpoch(int(dates[0].timestamp() * 1000))
qt_end_date = QDateTime.fromMSecsSinceEpoch(int(dates[-1].timestamp() * 1000))
axis_x.setRange(qt_start_date, qt_end_date)
# Axa Y = valori 0–100
axis_y = QValueAxis()
axis_y.setRange(0, 100)
axis_y.setTitleText("Interest (0–100)")
chart.addAxis(axis_x, Qt.AlignmentFlag.AlignBottom)
chart.addAxis(axis_y, Qt.AlignmentFlag.AlignLeft)
# Adăugăm seriile
for k in keywords:
series = QLineSeries()
series.setName(k)
for i, val in enumerate(data[k]):
# !!! CORECȚIE 2: Înmulțim cu 1000 pentru a transforma în milisecunde !!!
timestamp_ms = int(dates[i].timestamp() * 1000)
series.append(QPointF(float(timestamp_ms), float(val)))
chart.addSeries(series)
series.attachAxis(axis_x)
series.attachAxis(axis_y)
view = QChartView(chart)
view.setRenderHint(QPainter.RenderHint.Antialiasing)
self.setCentralWidget(view)
app = QApplication(sys.argv)
window = Window()
window.resize(1400, 700)
window.show()
sys.exit(app.exec())
This result is good and is fixed by Gemnini artificial intelligence, but if you want real development then you need to use more then simple issue to artificial intelligence:
python main.py
Average interest growth (returns):
munca: 0.0073
recesiune: 0.3387
somaj: 0.0308
inflatie: 0.0660
dobanzi: 0.0189
Volatility (risk):
munca: 0.0258
recesiune: 2.9473
somaj: 0.1348
inflatie: 0.1607
dobanzi: 0.0594
